{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", "

1  Requirements and helper functions
1.1  Requirements
1.2  Mathematical notations for stationary problems
1.3  Generating stationary data
1.4  Mathematical notations for piecewise stationary problems
1.5  Generating fake piecewise stationary data
2  Python implementations of some statistical tests
2.1  Monitored
2.2  CUSUM
2.3  PHT
2.4  Gaussian GLR
2.5  Bernoulli GLR
2.6  Sub-Gaussian GLR
2.7  List of all Python algorithms
3  Comparing the different implementations
3.1  Generating some toy data
3.2  Checking time efficiency
3.3  Checking detection delay
3.4  Checking false alarm probabilities
3.5  Checking missed detection probabilities
4  More simulations and some plots
4.1  Run a check for a grid of values
4.2  A version using joblib.Parallel to use multi-core computations
4.3  Checking on a small grid of values
4.4  Plotting the result as a 2D image
4.4.1  First example
4.4.1.1  For Monitored
4.4.1.2  For CUSUM
4.4.2  Second example
4.4.2.1  For Monitored
4.4.2.2  For Monitored for Gaussian data
4.4.2.3  For CUSUM
4.4.2.4  For PHT
4.4.2.5  For Bernoulli GLR
4.4.2.6  For Gaussian GLR
4.4.2.7  For Sub-Gaussian GLR
5  Exploring the parameters of change point detection algorithms: how to tune them?
5.1  A simple problem function
5.2  A generic function
5.3  Plotting the result as a 1D plot
5.4  Experiments for Monitored
5.5  Experiments for Bernoulli GLR
5.6  Experiments for Gaussian GLR
5.7  Experiments for CUSUM
5.8  Experiments for Sub-Gaussian GLR
5.9  Other experiments
6  Conclusions
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Requirements and helper functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Requirements\n", "\n", "This notebook requires to have [`numpy`](https://www.numpy.org/) and [`matplotlib`](https://matplotlib.org/) installed.\n", "One function needs a function from [`scipy.special`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.comb.html#scipy.special.comb).\n", "[`joblib`](https://joblib.readthedocs.io/en/latest/) is used to have parallel computations (at the end).\n", "\n", "The bottleneck performance of the main functions are very simple functions, for which we can write efficient versions using either [`numba.jit`](https://numba.pydata.org/numba-doc/latest/reference/jit-compilation.html#numba.jit) or [`cython`](https://cython.readthedocs.io/en/latest/src/quickstart/build.html#jupyter-notebook)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: watermark in /usr/local/lib/python3.6/dist-packages (1.5.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.15.4)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (1.1.0)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.0.2)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (0.12.1)\n", "Requirement already satisfied: numba in /usr/local/lib/python3.6/dist-packages (0.41.0)\n", "Requirement already satisfied: cython in /usr/local/lib/python3.6/dist-packages (0.29.1)\n", "Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from watermark) (7.2.0)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.0.1)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.3.0)\n", "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.7.3)\n", "Requirement already satisfied: llvmlite>=0.26.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba) (0.26.0)\n", "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (2.0.7)\n", "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (0.12.1)\n", "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (2.2.0)\n", "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (4.3.0)\n", "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (40.5.0)\n", "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (4.3.2)\n", "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (0.7.5)\n", "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (4.6.0)\n", "Requirement already satisfied: backcall in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (0.1.0)\n", "Requirement already satisfied: six in /home/lilian/.local/lib/python3.6/site-packages (from cycler>=0.10->matplotlib) (1.11.0)\n", "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->watermark) (0.1.7)\n", "Requirement already satisfied: parso>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from jedi>=0.10->ipython->watermark) (0.3.1)\n", "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->watermark) (0.2.0)\n", "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->watermark) (0.6.0)\n", "The watermark extension is already loaded. To reload it, use:\n", " %reload_ext watermark\n", "Lilian Besson and Emilie Kaufmann \n", "\n", "CPython 3.6.7\n", "IPython 7.2.0\n", "\n", "numpy 1.15.4\n", "scipy 1.1.0\n", "matplotlib 3.0.2\n", "joblib 0.12.1\n", "numba 0.41.0\n", "cython 0.29.1\n", "\n", "compiler : GCC 8.2.0\n", "system : Linux\n", "release : 4.15.0-42-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "!pip3 install watermark numpy scipy matplotlib joblib numba cython\n", "%load_ext watermark\n", "%watermark -v -m -p numpy,scipy,matplotlib,joblib,numba,cython -a \"Lilian Besson and Emilie Kaufmann\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "try:\n", " from tqdm import tqdm_notebook as tqdm\n", "except:\n", " def tqdm(iterator, *args, **kwargs):\n", " return iterator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mathematical notations for stationary problems\n", "\n", "We consider $K \\geq 1$ arms, which are distributions $\\nu_k$.\n", "We focus on Bernoulli distributions, which are characterized by their means, $\\nu_k = \\mathcal{B}(\\mu_k)$ for $\\mu_k\\in[0,1]$.\n", "A stationary bandit problem is defined here by the vector $[\\mu_1,\\dots,\\mu_K]$.\n", "\n", "For a fixed problem and a *horizon* $T\\in\\mathbb{N}$, $T\\geq1$, we draw samples from the $K$ distributions to get *data*: $\\forall t, r_k(t) \\sim \\nu_k$, ie, $\\mathbb{P}(r_k(t) = 1) = \\mu_k$ and $r_k(t) \\in \\{0,1\\}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating stationary data\n", "\n", "Here we give some examples of stationary problems and examples of data we can draw from them." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def bernoulli_samples(means, horizon=1000):\n", " if np.size(means) == 1:\n", " return np.random.binomial(1, means, size=horizon)\n", " else:\n", " results = np.zeros((np.size(means), horizon))\n", " for i, mean in enumerate(means):\n", " results[i] = np.random.binomial(1, mean, size=horizon)\n", " return results" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "problem1 = [0.5]\n", "\n", "bernoulli_samples(problem1, horizon=20)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "61.5 µs ± 2.59 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit bernoulli_samples(problem1, horizon=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now for Gaussian data:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "sigma = 0.25 # Bernoulli are 1/4-sub Gaussian too!" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def gaussian_samples(means, horizon=1000, sigma=sigma):\n", " if np.size(means) == 1:\n", " return np.random.normal(loc=means, scale=sigma, size=horizon)\n", " else:\n", " results = np.zeros((np.size(means), horizon))\n", " for i, mean in enumerate(means):\n", " results[i] = np.random.normal(loc=mean, scale=sigma, size=horizon)\n", " return results" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.62387502, 0.25629644, 0.67019174, 0.17045626, 0.64599757,\n", " 0.37851622, 0.7365035 , 0.59688851, 0.63485378, 0.59576263,\n", " 0.27670489, 0.69860233, 0.49817269, 0.26672791, -0.05480486,\n", " 0.43916024, 0.4352945 , 0.630749 , -0.11451167, 0.11158323])" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gaussian_samples(problem1, horizon=20)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "75.8 µs ± 4.3 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit gaussian_samples(problem1, horizon=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For bandit problem with $K \\geq 2$ arms, the *goal* is to design an online learning algorithm that roughly do the following:\n", "\n", "- For time $t=1$ to $t=T$ (unknown horizon)\n", " 1. Algorithm $A$ decide to draw arm $A(t) \\in\\{1,\\dots,K\\}$,\n", " 2. Get the reward $r(t) = r_{A(t)}(t) \\sim \\nu_{A(t)}$ from the (Bernoulli) distribution of that arm,\n", " 3. Give this observation of reward $r(t)$ coming from arm $A(t)$ to the algorithm,\n", " 4. Update internal state of the algorithm\n", "\n", "An algorithm is efficient if it obtains a high (expected) sum reward, ie, $\\sum_{t=1}^T r(t)$.\n", "\n", "Note that I don't focus on bandit algorithm here." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0.],\n", " [1., 1., 0., 1., 0., 0., 1., 0., 1., 0., 1., 1., 1., 0., 0., 1.,\n", " 0., 0., 1., 0.],\n", " [1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 0., 1., 1., 1.]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "problem2 = [0.1, 0.5, 0.9]\n", "\n", "bernoulli_samples(problem2, horizon=20)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.46905853, 0.08312363, -0.23166553, 0.59750825, 0.23356612,\n", " -0.02132874, -0.26851831, 0.18202564, 0.37409142, -0.14397223,\n", " -0.22241626, -0.18888125, -0.13127815, 0.2314255 , 0.49433066,\n", " 0.39645143, -0.11529154, -0.13321403, 0.34457873, -0.16463127],\n", " [ 0.09801182, 0.8898005 , 0.76216447, 0.72837837, 0.25624864,\n", " 0.71225959, 0.56964125, 0.6309913 , 1.06315774, 0.28064027,\n", " 0.2251329 , 0.61036472, 0.43995415, 0.57338936, 0.74570767,\n", " 0.89390964, 0.50845914, 0.62240528, 0.27255829, 0.03454648],\n", " [ 1.04280525, 0.38307288, 0.81820526, 0.95947436, 1.21587846,\n", " 0.82568302, 0.60475185, 1.09188758, 1.40998237, 0.7291101 ,\n", " 0.94030203, 0.95715236, 0.6893489 , 1.26446965, 0.80080259,\n", " 0.96966515, 0.79117913, 0.48041062, 0.92280092, 1.27883349]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "problem2 = [0.1, 0.5, 0.9]\n", "\n", "gaussian_samples(problem2, horizon=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For instance on these data, the best arm is clearly the third one, with expected reward of $\\mu^* = \\max_k \\mu_k = 0.9$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mathematical notations for piecewise stationary problems\n", "\n", "Now we fix the horizon $T\\in\\mathbb{N}$, $T\\geq1$ and we also consider a set of $\\Upsilon_T$ *break points*, $\\tau_1,\\dots,\\tau_{\\Upsilon_T} \\in\\{1,\\dots,T\\}$. We denote $\\tau_0 = 0$ and $\\tau_{\\Upsilon_T+1} = T$ for convenience of notations.\n", "We can assume that breakpoints are far \"enough\" from each other, for instance that there exists an integer $N\\in\\mathbb{N},N\\geq1$ such that $\\min_{i=0}^{\\Upsilon_T} \\tau_{i+1} - \\tau_i \\geq N K$. That is, on each *stationary interval*, a uniform sampling of the $K$ arms gives at least $N$ samples by arm.\n", "\n", "Now, in any stationary interval $[\\tau_i + 1, \\tau_{i+1}]$, the $K \\geq 1$ arms are distributions $\\nu_k^{(i)}$.\n", "We focus on Bernoulli distributions, which are characterized by their means, $\\nu_k^{(i)} := \\mathcal{B}(\\mu_k^{(i)})$ for $\\mu_k^{(i)}\\in[0,1]$.\n", "A piecewise stationary bandit problem is defined here by the vector $[\\mu_k^{(i)}]_{1\\leq k \\leq K, 1 \\leq i \\leq \\Upsilon_T}$.\n", "\n", "For a fixed problem and a *horizon* $T\\in\\mathbb{N}$, $T\\geq1$, we draw samples from the $K$ distributions to get *data*: $\\forall t, r_k(t) \\sim \\nu_k^{(i)}$ for $i$ the unique index of stationary interval such that $t\\in[\\tau_i + 1, \\tau_{i+1}]$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating fake piecewise stationary data\n", "\n", "The format to define piecewise stationary problem will be the following. It is compact but generic!\n", "\n", "The first example considers a unique arm, with 2 breakpoints uniformly spaced.\n", "- On the first interval, for instance from $t=1$ to $t=500$, that is $\\tau_1 = 500$, $\\mu_1^{(1)} = 0.1$,\n", "- On the second interval, for instance from $t=501$ to $t=1000$, that is $\\tau_2 = 100$, $\\mu_1^{(2)} = 0.5$,\n", "- On the third interval, for instance from $t=1001$ to $t=1500$, that $\\mu_1^{(3)} = 0.9$." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# With 1 arm only!\n", "problem_piecewise_0 = lambda horizon: {\n", " \"listOfMeans\": [\n", " [0.1], # 0 to 499\n", " [0.5], # 500 to 999\n", " [0.8], # 1000 to 1499\n", " ],\n", " \"changePoints\": [\n", " int(0 * horizon / 1500.0),\n", " int(500 * horizon / 1500.0),\n", " int(1000 * horizon / 1500.0),\n", " ],\n", "}" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "code_folding": [ 1 ] }, "outputs": [], "source": [ "# With 2 arms\n", "problem_piecewise_1 = lambda horizon: {\n", " \"listOfMeans\": [\n", " [0.1, 0.2], # 0 to 399\n", " [0.1, 0.3], # 400 to 799\n", " [0.5, 0.3], # 800 to 1199\n", " [0.4, 0.3], # 1200 to 1599\n", " [0.3, 0.9], # 1600 to end\n", " ],\n", " \"changePoints\": [\n", " int(0 * horizon / 2000.0),\n", " int(400 * horizon / 2000.0),\n", " int(800 * horizon / 2000.0),\n", " int(1200 * horizon / 2000.0),\n", " int(1600 * horizon / 2000.0),\n", " ],\n", "}" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "code_folding": [ 1 ] }, "outputs": [], "source": [ "# With 3 arms\n", "problem_piecewise_2 = lambda horizon: {\n", " \"listOfMeans\": [\n", " [0.2, 0.5, 0.9], # 0 to 399\n", " [0.2, 0.2, 0.9], # 400 to 799\n", " [0.2, 0.2, 0.1], # 800 to 1199\n", " [0.7, 0.2, 0.1], # 1200 to 1599\n", " [0.7, 0.5, 0.1], # 1600 to end\n", " ],\n", " \"changePoints\": [\n", " int(0 * horizon / 2000.0),\n", " int(400 * horizon / 2000.0),\n", " int(800 * horizon / 2000.0),\n", " int(1200 * horizon / 2000.0),\n", " int(1600 * horizon / 2000.0),\n", " ],\n", "}" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "code_folding": [ 1 ] }, "outputs": [], "source": [ "# With 3 arms\n", "problem_piecewise_3 = lambda horizon: {\n", " \"listOfMeans\": [\n", " [0.4, 0.5, 0.9], # 0 to 399\n", " [0.5, 0.4, 0.7], # 400 to 799\n", " [0.6, 0.3, 0.5], # 800 to 1199\n", " [0.7, 0.2, 0.3], # 1200 to 1599\n", " [0.8, 0.1, 0.1], # 1600 to end\n", " ],\n", " \"changePoints\": [\n", " int(0 * horizon / 2000.0),\n", " int(400 * horizon / 2000.0),\n", " int(800 * horizon / 2000.0),\n", " int(1200 * horizon / 2000.0),\n", " int(1600 * horizon / 2000.0),\n", " ],\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can write a utility function that transform this compact representation into a full list of means." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "def getFullHistoryOfMeans(problem, horizon=2000):\n", " \"\"\"Return the vector of mean of the arms, for a piece-wise stationary MAB.\n", "\n", " - It is a numpy array of shape (nbArms, horizon).\n", " \"\"\"\n", " pb = problem(horizon)\n", " listOfMeans, changePoints = pb['listOfMeans'], pb['changePoints']\n", " nbArms = len(listOfMeans[0])\n", " if horizon is None:\n", " horizon = np.max(changePoints)\n", " meansOfArms = np.ones((nbArms, horizon))\n", " for armId in range(nbArms):\n", " nbChangePoint = 0\n", " for t in range(horizon):\n", " if nbChangePoint < len(changePoints) - 1 and t >= changePoints[nbChangePoint + 1]:\n", " nbChangePoint += 1\n", " meansOfArms[armId][t] = listOfMeans[nbChangePoint][armId]\n", " return meansOfArms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For examples :" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n", " 0.1, 0.1, 0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n", " 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getFullHistoryOfMeans(problem_piecewise_0, horizon=50)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n", " 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n", " 0.4, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],\n", " [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3,\n", " 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,\n", " 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,\n", " 0.3, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getFullHistoryOfMeans(problem_piecewise_1, horizon=50)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,\n", " 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,\n", " 0.2, 0.2, 0.2, 0.2, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7,\n", " 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7],\n", " [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.2, 0.2, 0.2,\n", " 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,\n", " 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,\n", " 0.2, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n", " [0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9,\n", " 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n", " 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n", " 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getFullHistoryOfMeans(problem_piecewise_2, horizon=50)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6,\n", " 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7,\n", " 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],\n", " [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.4, 0.4, 0.4,\n", " 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,\n", " 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,\n", " 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n", " [0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.7, 0.7, 0.7,\n", " 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,\n", " 0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getFullHistoryOfMeans(problem_piecewise_3, horizon=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we need to be able to generate samples from such distributions." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "def piecewise_bernoulli_samples(problem, horizon=1000):\n", " fullMeans = getFullHistoryOfMeans(problem, horizon=horizon)\n", " nbArms, horizon = np.shape(fullMeans)\n", " results = np.zeros((nbArms, horizon))\n", " for i in range(nbArms):\n", " mean_i = fullMeans[i, :]\n", " for t in range(horizon):\n", " mean_i_t = max(0, min(1, mean_i[t])) # crop to [0, 1] !\n", " results[i, t] = np.random.binomial(1, mean_i_t)\n", " return results" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def piecewise_gaussian_samples(problem, horizon=1000, sigma=sigma):\n", " fullMeans = getFullHistoryOfMeans(problem, horizon=horizon)\n", " nbArms, horizon = np.shape(fullMeans)\n", " results = np.zeros((nbArms, horizon))\n", " for i in range(nbArms):\n", " mean_i = fullMeans[i, :]\n", " for t in range(horizon):\n", " mean_i_t = mean_i[t]\n", " results[i, t] = np.random.normal(loc=mean_i_t, scale=sigma, size=1)\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examples:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n", " 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,\n", " 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n", " 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n", " 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "array([[0., 1., 0., 1., 0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n", " 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 1., 1., 1., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 1.,\n", " 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 0., 0., 1., 0.,\n", " 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0.,\n", " 0., 1., 0., 1.]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getFullHistoryOfMeans(problem_piecewise_0, horizon=100)\n", "piecewise_bernoulli_samples(problem_piecewise_0, horizon=100)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.00315559, 0.2159303 , 0.01763935, -0.09514176, 0.07699997,\n", " 0.12997326, 0.36806044, 0.26968036, -0.29470607, -0.12074206,\n", " 0.38758995, 0.09229431, -0.1390067 , 0.01674725, 0.29248761,\n", " -0.00743648, -0.32053746, -0.05338394, -0.22795571, -0.07397954,\n", " 0.12089186, -0.05089131, 0.07538026, 0.37811067, 0.28527238,\n", " -0.44775343, 0.05399375, 0.56363279, 0.12052245, 0.04308359,\n", " 0.05259644, -0.09940602, 0.59624864, 0.47309089, 0.73371138,\n", " 0.71548802, 0.58391054, 0.57838614, 0.84475862, 0.17150277,\n", " 0.08094113, 0.12821665, 0.9622493 , 0.46279281, 0.69436121,\n", " 0.67516175, 0.63060937, 0.3242004 , 0.46987869, 0.87444974,\n", " 0.72351635, 0.13288009, 0.60558045, 0.42477301, 0.08474411,\n", " 0.28108802, 0.38570242, 0.16206467, 0.45767589, 0.62731769,\n", " 0.50540122, 0.09523314, 0.84005644, 0.29788404, 0.22493549,\n", " 0.43068172, 1.01212527, 0.66926627, 1.12914167, 0.56228928,\n", " 0.29419603, 0.83918851, 0.55743231, 1.2773794 , 0.47145187,\n", " 0.5581184 , 0.69071478, 0.78587582, 0.66516129, 0.13434453,\n", " 0.70156 , 1.00476689, 0.70888586, 0.61272042, 0.47476143,\n", " 0.52100125, 0.54323956, 0.84789493, 0.54069643, 1.35941397,\n", " 0.93167352, 0.85845656, 1.00137879, 0.97582926, 0.83642737,\n", " 0.63083642, 0.78315512, 0.92622805, 0.63902527, 0.48292036]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "piecewise_gaussian_samples(problem_piecewise_0, horizon=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We easily spot the (approximate) location of the breakpoint!\n", "\n", "Another example:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 1.,\n", " 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1.,\n", " 0., 1., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n", " 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 1., 1., 1.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 0., 1.,\n", " 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0.,\n", " 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0.,\n", " 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1.]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "piecewise_bernoulli_samples(problem_piecewise_1, horizon=100)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.03440385, 0.35339867, 0.39487031, 0.03900667, -0.04061467,\n", " 0.42847871, 0.12854273, 0.11698151, 0.53333388, 0.55173192,\n", " 0.24042019, 0.6838909 , 0.64939741, 0.9333824 , 0.9194079 ,\n", " 0.49387316, 0.32736459, 0.2525435 , 0.37381781, 0.55302675],\n", " [ 0.28821344, 0.18003026, 0.25372301, 0.40054737, 0.55935287,\n", " -0.38328144, 0.06330063, 0.12570344, 0.46626937, 0.12882954,\n", " 0.35432014, 0.31273292, 0.90022341, 0.23341473, 0.0748465 ,\n", " 0.4811396 , 1.02582958, 0.99077802, 1.07355786, 0.48020466]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "piecewise_gaussian_samples(problem_piecewise_1, horizon=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "# Python implementations of some statistical tests\n", "\n", "I will implement here the following statistical tests.\n", "I give a link to the implementation of the correspond bandit policy in my framework [`SMPyBandits`](https://smpybandits.github.io/)\n", "\n", "- Monitored (based on a McDiarmid inequality), for Monitored-UCB or [`M-UCB`](),\n", "- CUSUM, for [`CUSUM-UCB`](https://smpybandits.github.io/docs/Policies.CD_UCB.html?highlight=cusum#Policies.CD_UCB.CUSUM_IndexPolicy),\n", "- PHT, for [`PHT-UCB`](https://smpybandits.github.io/docs/Policies.CD_UCB.html?highlight=cusum#Policies.CD_UCB.PHT_IndexPolicy),\n", "- Gaussian GLR, for [`GaussianGLR-UCB`](https://smpybandits.github.io/docs/Policies.CD_UCB.html?highlight=glr#Policies.CD_UCB.GaussianGLR_IndexPolicy),\n", "- Bernoulli GLR, for [`BernoulliGLR-UCB`](https://smpybandits.github.io/docs/Policies.CD_UCB.html?highlight=glr#Policies.CD_UCB.BernoulliGLR_IndexPolicy)." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "class ChangePointDetector(object):\n", " def __init__(self, **kwargs):\n", " self._kwargs = kwargs\n", " for key, value in kwargs.items():\n", " setattr(self, key, value)\n", "\n", " def __str__(self):\n", " return f\"{self.__class__.__name__}{f'({repr(self._kwargs)})' if self._kwargs else ''}\"\n", "\n", " def detect(self, all_data, t):\n", " raise NotImplementedError" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having classes is simply to be able to pretty print the algorithms when they have parameters:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ChangePointDetector\n" ] } ], "source": [ "print(ChangePointDetector())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ChangePointDetector({'w': 10, 'b': 1})\n" ] } ], "source": [ "print(ChangePointDetector(w=10, b=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Monitored`\n", "\n", "It uses a McDiarmid inequality. For a (pair) window size $w\\in\\mathbb{N}$ and a threshold $b\\in\\mathbb{R}^+$.\n", "At time $t$, if there is at least $w$ data in the data vector $(X_i)_i$, then let $Y$ denote the last $w$ data.\n", "A change is detected if\n", "$$ |\\sum_{i=w/2+1}^{w} Y_i - \\sum_{i=1}^{w/2} Y_i | > b ? $$" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "NB_ARMS = 1\n", "WINDOW_SIZE = 80" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "import numba" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "class Monitored(ChangePointDetector):\n", " def __init__(self, window_size=WINDOW_SIZE, threshold_b=None):\n", " super().__init__(window_size=window_size, threshold_b=threshold_b)\n", "\n", " def __str__(self):\n", " if self.threshold_b:\n", " return f\"Monitored($w={self.window_size:.3g}$, $b={self.threshold_b:.3g}$)\"\n", " else:\n", " latexname = r\"\\sqrt{\\frac{w}{2} \\log(2 T^2)}\"\n", " return f\"Monitored($w={self.window_size:.3g}$, $b={latexname}$)\"\n", "\n", " def detect(self, all_data, t):\n", " r\"\"\" A change is detected for the current arm if the following test is true:\n", "\n", " .. math:: |\\sum_{i=w/2+1}^{w} Y_i - \\sum_{i=1}^{w/2} Y_i | > b ?\n", "\n", " - where :math:`Y_i` is the i-th data in the latest w data from this arm (ie, :math:`X_k(t)` for :math:`t = n_k - w + 1` to :math:`t = n_k` current number of samples from arm k).\n", " - where :attr:`threshold_b` is the threshold b of the test, and :attr:`window_size` is the window-size w.\n", " \"\"\"\n", " data = all_data[:t]\n", " # don't try to detect change if there is not enough data!\n", " if len(data) < self.window_size:\n", " return False\n", "\n", " # compute parameters\n", " horizon = len(all_data)\n", " threshold_b = self.threshold_b\n", " if threshold_b is None:\n", " threshold_b = np.sqrt(self.window_size/2 * np.log(2 * NB_ARMS * horizon**2))\n", "\n", " last_w_data = data[-self.window_size:]\n", " sum_first_half = np.sum(last_w_data[:self.window_size//2])\n", " sum_second_half = np.sum(last_w_data[self.window_size//2:])\n", " return abs(sum_first_half - sum_second_half) > threshold_b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `CUSUM`\n", "\n", "The two-sided CUSUM algorithm, from [Page, 1954], works like this:\n", "\n", "- For each *data* k, compute:\n", "\n", "$$\n", "s_k^- = (y_k - \\hat{u}_0 - \\varepsilon) 1(k > M),\\\\\n", "s_k^+ = (\\hat{u}_0 - y_k - \\varepsilon) 1(k > M),\\\\\n", "g_k^+ = \\max(0, g_{k-1}^+ + s_k^+),\\\\\n", "g_k^- = \\max(0, g_{k-1}^- + s_k^-).\n", "$$\n", "\n", "- The change is detected if $\\max(g_k^+, g_k^-) > h$, where $h=$`threshold_h` is the threshold of the test,\n", "- And $\\hat{u}_0 = \\frac{1}{M} \\sum_{k=1}^{M} y_k$ is the mean of the first M samples, where M is `M` the min number of observation between change points." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "#: Precision of the test.\n", "EPSILON = 0.5\n", "\n", "#: Default value of :math:`\\lambda`.\n", "LAMBDA = 1\n", "\n", "#: Hypothesis on the speed of changes: between two change points, there is at least :math:`M * K` time steps, where K is the number of arms, and M is this constant.\n", "MIN_NUMBER_OF_OBSERVATION_BETWEEN_CHANGE_POINT = 50\n", "\n", "MAX_NB_RANDOM_EVENTS = 1" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "from scipy.special import comb" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "def compute_h__CUSUM(horizon, \n", " verbose=False,\n", " M=MIN_NUMBER_OF_OBSERVATION_BETWEEN_CHANGE_POINT,\n", " max_nb_random_events=MAX_NB_RANDOM_EVENTS,\n", " nbArms=1,\n", " epsilon=EPSILON,\n", " lmbda=LAMBDA,\n", " ):\n", " r\"\"\" Compute the values :math:`C_1^+, C_1^-, C_1, C_2, h` from the formulas in Theorem 2 and Corollary 2 in the paper.\"\"\"\n", " T = int(max(1, horizon))\n", " UpsilonT = int(max(1, max_nb_random_events))\n", " K = int(max(1, nbArms))\n", " C1_minus = np.log(((4 * epsilon) / (1-epsilon)**2) * comb(M, int(np.floor(2 * epsilon * M))) * (2 * epsilon)**M + 1)\n", " C1_plus = np.log(((4 * epsilon) / (1+epsilon)**2) * comb(M, int(np.ceil(2 * epsilon * M))) * (2 * epsilon)**M + 1)\n", " C1 = min(C1_minus, C1_plus)\n", " if C1 == 0: C1 = 1 # FIXME\n", " h = 1/C1 * np.log(T / UpsilonT)\n", " return h" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "class CUSUM(ChangePointDetector):\n", " def __init__(self,\n", " epsilon=EPSILON,\n", " M=MIN_NUMBER_OF_OBSERVATION_BETWEEN_CHANGE_POINT,\n", " threshold_h=None,\n", " ):\n", " assert 0 < epsilon < 1, f\"Error: epsilon for CUSUM must be in (0, 1) but is {epsilon}.\"\n", " super().__init__(epsilon=epsilon, M=M, threshold_h=threshold_h)\n", " \n", " def __str__(self):\n", " if self.threshold_h:\n", " return fr\"CUSUM($\\varepsilon={self.epsilon:.3g}$, $M={self.M}$, $h={self.threshold_h:.3g}$)\"\n", " else:\n", " return fr\"CUSUM($\\varepsilon={self.epsilon:.3g}$, $M={self.M}$, $h=$'auto')\"\n", "\n", " def detect(self, all_data, t):\n", " r\"\"\" Detect a change in the current arm, using the two-sided CUSUM algorithm [Page, 1954].\n", "\n", " - For each *data* k, compute:\n", "\n", " .. math::\n", "\n", " s_k^- &= (y_k - \\hat{u}_0 - \\varepsilon) 1(k > M),\\\\\n", " s_k^+ &= (\\hat{u}_0 - y_k - \\varepsilon) 1(k > M),\\\\\n", " g_k^+ &= \\max(0, g_{k-1}^+ + s_k^+),\\\\\n", " g_k^- &= \\max(0, g_{k-1}^- + s_k^-).\n", "\n", " - The change is detected if :math:`\\max(g_k^+, g_k^-) > h`, where :attr:`threshold_h` is the threshold of the test,\n", " - And :math:`\\hat{u}_0 = \\frac{1}{M} \\sum_{k=1}^{M} y_k` is the mean of the first M samples, where M is :attr:`M` the min number of observation between change points.\n", " \"\"\"\n", " data = all_data[:t]\n", "\n", " # compute parameters\n", " horizon = len(all_data)\n", " threshold_h = self.threshold_h\n", " if self.threshold_h is None:\n", " threshold_h = compute_h__CUSUM(horizon, self.M, 1, epsilon=self.epsilon)\n", "\n", " gp, gm = 0, 0\n", " # First we use the first M samples to calculate the average :math:`\\hat{u_0}`.\n", " u0hat = np.mean(data[:self.M])\n", " for k in range(self.M + 1, len(data)):\n", " y_k = data[k]\n", " sp = u0hat - y_k - self.epsilon # no need to multiply by (k > self.M)\n", " sm = y_k - u0hat - self.epsilon # no need to multiply by (k > self.M)\n", " gp = max(0, gp + sp)\n", " gm = max(0, gm + sm)\n", " if max(gp, gm) >= threshold_h:\n", " return True\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `PHT`\n", "\n", "The two-sided CUSUM algorithm, from [Hinkley, 1971], works like this:\n", "\n", "- For each *data* k, compute:\n", "\n", "$$\n", "s_k^- = y_k - \\hat{y}_k - \\varepsilon,\\\\\n", "s_k^+ = \\hat{y}_k - y_k - \\varepsilon,\\\\\n", "g_k^+ = \\max(0, g_{k-1}^+ + s_k^+),\\\\\n", "g_k^- = \\max(0, g_{k-1}^- + s_k^-).\n", "$$\n", "\n", "- The change is detected if $\\max(g_k^+, g_k^-) > h$, where $h=$`threshold_h` is the threshold of the test,\n", "- And $\\hat{y}_k = \\frac{1}{k} \\sum_{s=1}^{k} y_s$ is the mean of the first k samples." ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [], "source": [ "class PHT(ChangePointDetector):\n", " def __init__(self,\n", " epsilon=EPSILON,\n", " M=MIN_NUMBER_OF_OBSERVATION_BETWEEN_CHANGE_POINT,\n", " threshold_h=None,\n", " ):\n", " assert 0 < epsilon < 1, f\"Error: epsilon for CUSUM must be in (0, 1) but is {epsilon}.\"\n", " super().__init__(epsilon=epsilon, M=M, threshold_h=threshold_h)\n", " \n", " def __str__(self):\n", " if self.threshold_h:\n", " return fr\"PHT($\\varepsilon={self.epsilon:.3g}$, $M={self.M}$, $h={self.threshold_h:.3g}$)\"\n", " else:\n", " return fr\"PHT($\\varepsilon={self.epsilon:.3g}$, $M={self.M}$, $h=$'auto')\"\n", "\n", " def detect(self, all_data, t):\n", " r\"\"\" Detect a change in the current arm, using the two-sided PHT algorithm [Hinkley, 1971].\n", "\n", " - For each *data* k, compute:\n", "\n", " .. math::\n", "\n", " s_k^- &= y_k - \\hat{y}_k - \\varepsilon,\\\\\n", " s_k^+ &= \\hat{y}_k - y_k - \\varepsilon,\\\\\n", " g_k^+ &= \\max(0, g_{k-1}^+ + s_k^+),\\\\\n", " g_k^- &= \\max(0, g_{k-1}^- + s_k^-).\n", "\n", " - The change is detected if :math:`\\max(g_k^+, g_k^-) > h`, where :attr:`threshold_h` is the threshold of the test,\n", " - And :math:`\\hat{y}_k = \\frac{1}{k} \\sum_{s=1}^{k} y_s` is the mean of the first k samples.\n", " \"\"\"\n", " data = all_data[:t]\n", "\n", " # compute parameters\n", " horizon = len(all_data)\n", " threshold_h = self.threshold_h\n", " if threshold_h is None:\n", " threshold_h = compute_h__CUSUM(horizon, self.M, 1, epsilon=self.epsilon)\n", "\n", " gp, gm = 0, 0\n", " y_k_hat = 0\n", " # First we use the first M samples to calculate the average :math:`\\hat{u_0}`.\n", " for k, y_k in enumerate(data):\n", " y_k_hat = (k * y_k_hat + y_k) / (k + 1) # XXX smart formula to update the mean!\n", " sp = y_k_hat - y_k - self.epsilon\n", " sm = y_k - y_k_hat - self.epsilon\n", " gp = max(0, gp + sp)\n", " gm = max(0, gm + sm)\n", " if max(gp, gm) >= threshold_h:\n", " return True\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## `Gaussian GLR`\n", "\n", "The Generalized Likelihood Ratio test (GLR) works with a one-dimensional exponential family, for which we have a function `kl` such that if $\\mu_1,\\mu_2$ are the means of two distributions $\\nu_1,\\nu_2$, then $\\mathrm{KL}(\\mathcal{D}(\\nu_1), \\mathcal{D}(\\nu_1))=$ `kl` $(\\mu_1,\\mu_2)$.\n", "\n", "- For each *time step* $s$ between $t_0=0$ and $t$, compute:\n", "$$G^{\\mathcal{N}_1}_{t_0:s:t} = (s-t_0+1) \\mathrm{kl}(\\mu_{t_0,s}, \\mu_{t_0,t}) + (t-s) \\mathrm{kl}(\\mu_{s+1,t}, \\mu_{t_0,t}).$$\n", "\n", "- The change is detected if there is a time $s$ such that $G^{\\mathcal{N}_1}_{t_0:s:t} > b(t_0, s, t, \\delta)$, where $b(t_0, s, t, \\delta)=$ `threshold_h` is the threshold of the test,\n", "- And $\\mu_{a,b} = \\frac{1}{b-a+1} \\sum_{s=a}^{b} y_s$ is the mean of the samples between $a$ and $b$.\n", "\n", "The threshold is computed as:\n", "$$ b(t_0, s, t, \\delta):= \\left(1 + \\frac{1}{t - t_0 + 1}\\right) 2 \\log\\left(\\frac{2 (t - t_0) \\sqrt{(t - t_0) + 2}}{\\delta}\\right).$$\n", "\n", "Another threshold we want to check is the following:\n", "$$ b(t_0, s, t, \\delta):= \\log\\left(\\frac{(s - t_0 + 1) (t - s)}{\\delta}\\right).$$" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "from math import log, isinf\n", "\n", "def compute_c__GLR_0(t0, s, t, horizon=None, delta=None):\n", " r\"\"\" Compute the values :math:`c` from the corollary of of Theorem 2 from [\"Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds\", O.-A. Maillard, 2018].\n", "\n", " - The threshold is computed as:\n", "\n", " .. math:: h := \\left(1 + \\frac{1}{t - t_0 + 1}\\right) 2 \\log\\left(\\frac{2 (t - t_0) \\sqrt{(t - t_0) + 2}}{\\delta}\\right).\n", " \"\"\"\n", " if delta is None:\n", " T = int(max(1, horizon))\n", " delta = 1.0 / T\n", " t_m_t0 = abs(t - t0)\n", " c = (1 + (1 / (t_m_t0 + 1.0))) * 2 * log((2 * t_m_t0 * np.sqrt(t_m_t0 + 2)) / delta)\n", " if c < 0 and isinf(c): c = float('+inf')\n", " return c" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "from math import log, isinf\n", "\n", "def compute_c__GLR(t0, s, t, horizon=None, delta=None):\n", " r\"\"\" Compute the values :math:`c` from the corollary of of Theorem 2 from [\"Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds\", O.-A. Maillard, 2018].\n", "\n", " - The threshold is computed as:\n", "\n", " .. math:: h := \\log\\left(\\frac{(s - t_0 + 1) (t - s)}{\\delta}\\right).\n", " \"\"\"\n", " if delta is None: \n", " T = int(max(1, horizon))\n", " delta = 1.0 / T\n", " arg = (s - t0 + 1) * (t - s) / delta\n", " if arg <= 0: c = float('+inf')\n", " else: c = log(arg)\n", " return c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Gaussian distributions of known variance, the Kullback-Leibler divergence is easy to compute:\n", "\n", "Kullback-Leibler divergence for Gaussian distributions of means $x$ and $y$ and variances $\\sigma^2_x = \\sigma^2_y$, $\\nu_1 = \\mathcal{N}(x, \\sigma_x^2)$ and $\\nu_2 = \\mathcal{N}(y, \\sigma_x^2)$ is:\n", "\n", "$$\\mathrm{KL}(\\nu_1, \\nu_2) = \\frac{(x - y)^2}{2 \\sigma_y^2} + \\frac{1}{2}\\left( \\frac{\\sigma_x^2}{\\sigma_y^2} - 1 \\log\\left(\\frac{\\sigma_x^2}{\\sigma_y^2}\\right) \\right).$$" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "code_folding": [ 0 ] }, "outputs": [], "source": [ "def klGauss(x, y, sig2x=0.25):\n", " r\"\"\" Kullback-Leibler divergence for Gaussian distributions of means ``x`` and ``y`` and variances ``sig2x`` and ``sig2y``, :math:`\\nu_1 = \\mathcal{N}(x, \\sigma_x^2)` and :math:`\\nu_2 = \\mathcal{N}(y, \\sigma_x^2)`:\n", "\n", " .. math:: \\mathrm{KL}(\\nu_1, \\nu_2) = \\frac{(x - y)^2}{2 \\sigma_y^2} + \\frac{1}{2}\\left( \\frac{\\sigma_x^2}{\\sigma_y^2} - 1 \\log\\left(\\frac{\\sigma_x^2}{\\sigma_y^2}\\right) \\right).\n", "\n", " See https://en.wikipedia.org/wiki/Normal_distribution#Other_properties\n", "\n", " - sig2y = sig2x (same variance).\n", " \"\"\"\n", " return (x - y) ** 2 / (2. * sig2x)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "code_folding": [ 3 ] }, "outputs": [], "source": [ "class GaussianGLR(ChangePointDetector):\n", " def __init__(self, mult_threshold_h=1, delta=None):\n", " super().__init__(mult_threshold_h=mult_threshold_h, delta=delta)\n", " \n", " def __str__(self):\n", " return r\"Gaussian-GLR($h_0={}$, $\\delta={}$)\".format(\n", " f\"{self.mult_threshold_h:.3g}\" if self.mult_threshold_h is not None else 'auto',\n", " f\"{self.delta:.3g}\" if self.delta is not None else 'auto',\n", " )\n", "\n", " def detect(self, all_data, t):\n", " r\"\"\" Detect a change in the current arm, using the Generalized Likelihood Ratio test (GLR) and the :attr:`kl` function.\n", "\n", " - For each *time step* :math:`s` between :math:`t_0=0` and :math:`t`, compute:\n", "\n", " .. math::\n", "\n", " G^{\\mathcal{N}_1}_{t_0:s:t} = (s-t_0+1)(t-s) \\mathrm{kl}(\\mu_{s+1,t}, \\mu_{t_0,s}) / (t-t_0+1).\n", "\n", " - The change is detected if there is a time :math:`s` such that :math:`G^{\\mathcal{N}_1}_{t_0:s:t} > h`, where :attr:`threshold_h` is the threshold of the test,\n", " - And :math:`\\mu_{a,b} = \\frac{1}{b-a+1} \\sum_{s=a}^{b} y_s` is the mean of the samples between :math:`a` and :math:`b`.\n", " \"\"\"\n", " data = all_data[:t]\n", " t0 = 0\n", " horizon = len(all_data)\n", "\n", " # compute parameters\n", " mean_all = np.mean(data[t0 : t+1])\n", " mean_before = 0\n", " mean_after = mean_all\n", " for s in range(t0, t):\n", " # DONE okay this is efficient we don't compute the same means too many times!\n", " y = data[s]\n", " mean_before = (s * mean_before + y) / (s + 1)\n", " mean_after = ((t + 1 - s + t0) * mean_after - y) / (t - s + t0)\n", " kl_before = klGauss(mean_before, mean_all)\n", " kl_after = klGauss(mean_after, mean_all)\n", " threshold_h = self.mult_threshold_h * compute_c__GLR(t0, s, t, horizon=horizon, delta=self.delta)\n", " glr = (s - t0 + 1) * kl_before + (t - s) * kl_after\n", " if glr >= threshold_h:\n", " return True\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Bernoulli GLR`\n", "\n", "The same GLR algorithm but using the Bernoulli KL, given by:\n", "\n", "$$\\mathrm{KL}(\\mathcal{B}(x), \\mathcal{B}(y)) = x \\log(\\frac{x}{y}) + (1-x) \\log(\\frac{1-x}{1-y}).$$" ] }, { "cell_type": "code", "execution_count": 257, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The cython extension is already loaded. To reload it, use:\n", " %reload_ext cython\n" ] } ], "source": [ "import cython\n", "%load_ext cython" ] }, { "cell_type": "code", "execution_count": 265, "metadata": {}, "outputs": [], "source": [ "def klBern(x: float, y: float) -> float:\n", " r\"\"\" Kullback-Leibler divergence for Bernoulli distributions. https://en.wikipedia.org/wiki/Bernoulli_distribution#Kullback.E2.80.93Leibler_divergence\n", "\n", " .. math:: \\mathrm{KL}(\\mathcal{B}(x), \\mathcal{B}(y)) = x \\log(\\frac{x}{y}) + (1-x) \\log(\\frac{1-x}{1-y}).\"\"\"\n", " x = min(max(x, 1e-6), 1 - 1e-6)\n", " y = min(max(y, 1e-6), 1 - 1e-6)\n", " return x * log(x / y) + (1 - x) * log((1 - x) / (1 - y))" ] }, { "cell_type": "code", "execution_count": 259, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.91 µs ± 107 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%timeit klBern(np.random.random(), np.random.random())" ] }, { "cell_type": "code", "execution_count": 260, "metadata": { "code_folding": [ 2 ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", " \n", " Cython: _cython_magic_e1f92aa869fc57ff4f0ff63e582d134a.pyx\n", " \n", "\n", "\n", "

Generated by Cython 0.29.1

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\n", " Click on a line that starts with a \"+\" to see the C code that Cython generated for it.\n", "

\n", "
 01: from libc.math cimport log
\n", "
+02: eps = 1e-6  #: Threshold value: everything in [0, 1] is truncated to [eps, 1 - eps]
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       "
 03:
\n", "
+04: def klBern(float x, float y) -> float:
\n", "
/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e1f92aa869fc57ff4f0ff63e582d134a_1klBern(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static char __pyx_doc_46_cython_magic_e1f92aa869fc57ff4f0ff63e582d134a_klBern[] = \" Kullback-Leibler divergence for Bernoulli distributions. https://en.wikipedia.org/wiki/Bernoulli_distribution#Kullback.E2.80.93Leibler_divergence\\n\\n    .. math:: \\\\mathrm{KL}(\\\\mathcal{B}(x), \\\\mathcal{B}(y)) = x \\\\log(\\\\frac{x}{y}) + (1-x) \\\\log(\\\\frac{1-x}{1-y}).\";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_e1f92aa869fc57ff4f0ff63e582d134a_1klBern = {\"klBern\", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_46_cython_magic_e1f92aa869fc57ff4f0ff63e582d134a_1klBern, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_e1f92aa869fc57ff4f0ff63e582d134a_klBern};\n",
       "static PyObject *__pyx_pw_46_cython_magic_e1f92aa869fc57ff4f0ff63e582d134a_1klBern(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  float __pyx_v_x;\n",
       "  float __pyx_v_y;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  __Pyx_RefNannyDeclarations\n",
       "  __Pyx_RefNannySetupContext(\"klBern (wrapper)\", 0);\n",
       "  {\n",
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       "    PyObject* values[2] = {0,0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = PyDict_Size(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_x)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
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 05:     r""" Kullback-Leibler divergence for Bernoulli distributions. https://en.wikipedia.org/wiki/Bernoulli_distribution#Kullback.E2.80.93Leibler_divergence
\n", "
 06:
\n", "
 07:     .. math:: \\mathrm{KL}(\\mathcal{B}(x), \\mathcal{B}(y)) = x \\log(\\frac{x}{y}) + (1-x) \\log(\\frac{1-x}{1-y})."""
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" ], "text/plain": [ "" ] }, "execution_count": 260, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython --annotate\n", "from libc.math cimport log\n", "eps = 1e-6 #: Threshold value: everything in [0, 1] is truncated to [eps, 1 - eps]\n", "\n", "def klBern_cython(float x, float y) -> float:\n", " r\"\"\" Kullback-Leibler divergence for Bernoulli distributions. https://en.wikipedia.org/wiki/Bernoulli_distribution#Kullback.E2.80.93Leibler_divergence\n", "\n", " .. math:: \\mathrm{KL}(\\mathcal{B}(x), \\mathcal{B}(y)) = x \\log(\\frac{x}{y}) + (1-x) \\log(\\frac{1-x}{1-y}).\"\"\"\n", " x = min(max(x, 1e-6), 1 - 1e-6)\n", " y = min(max(y, 1e-6), 1 - 1e-6)\n", " return x * log(x / y) + (1 - x) * log((1 - x) / (1 - y))" ] }, { "cell_type": "code", "execution_count": 261, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "900 ns ± 80.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%timeit klBern_cython(np.random.random(), np.random.random())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the class, with this optimized kl function." ] }, { "cell_type": "code", "execution_count": 262, "metadata": { "code_folding": [ 3 ] }, "outputs": [], "source": [ "class BernoulliGLR(ChangePointDetector):\n", " def __init__(self, mult_threshold_h=1, delta=None):\n", " super().__init__(mult_threshold_h=mult_threshold_h, delta=delta)\n", " \n", " def __str__(self):\n", " return r\"Bernoulli-GLR($h_0={}$, $\\delta={}$)\".format(\n", " f\"{self.mult_threshold_h:.3g}\" if self.mult_threshold_h is not None else 'auto',\n", " f\"{self.delta:.3g}\" if self.delta is not None else 'auto',\n", " )\n", "\n", " def detect(self, all_data, t):\n", " r\"\"\" Detect a change in the current arm, using the Generalized Likelihood Ratio test (GLR) and the :attr:`kl` function.\n", "\n", " - For each *time step* :math:`s` between :math:`t_0=0` and :math:`t`, compute:\n", "\n", " .. math::\n", "\n", " G^{\\mathcal{N}_1}_{t_0:s:t} = (s-t_0+1)(t-s) \\mathrm{kl}(\\mu_{s+1,t}, \\mu_{t_0,s}) / (t-t_0+1).\n", "\n", " - The change is detected if there is a time :math:`s` such that :math:`G^{\\mathcal{N}_1}_{t_0:s:t} > h`, where :attr:`threshold_h` is the threshold of the test,\n", " - And :math:`\\mu_{a,b} = \\frac{1}{b-a+1} \\sum_{s=a}^{b} y_s` is the mean of the samples between :math:`a` and :math:`b`.\n", " \"\"\"\n", " data = all_data[:t]\n", " t0 = 0\n", " horizon = len(all_data)\n", "\n", " # compute parameters\n", "\n", " mean_all = np.mean(data[t0 : t+1])\n", " mean_before = 0\n", " mean_after = mean_all\n", " for s in range(t0, t):\n", " # DONE okay this is efficient we don't compute the same means too many times!\n", " y = data[s]\n", " mean_before = (s * mean_before + y) / (s + 1)\n", " mean_after = ((t + 1 - s + t0) * mean_after - y) / (t - s + t0)\n", " kl_before = klBern(mean_before, mean_all)\n", " kl_after = klBern(mean_after, mean_all)\n", " threshold_h = self.mult_threshold_h * compute_c__GLR(t0, s, t, horizon=horizon, delta=self.delta)\n", " glr = (s - t0 + 1) * kl_before + (t - s) * kl_after\n", " if glr >= threshold_h:\n", " return True\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Sub-Gaussian GLR`\n", "\n", "A slightly different GLR algorithm for non-parametric sub-Gaussian distributions.\n", "We assume the distributions $\\nu^1$ and $\\nu^2$ to be $\\sigma^2$-sub Gaussian, for a known value of $\\sigma\\in\\mathbb{R}^+$, and if we consider a confidence level $\\delta\\in(0,1)$ (typically, it is set to $\\frac{1}{T}$ if the horizon $T$ is known, or $\\delta=\\delta_t=\\frac{1}{t^2}$ to have $\\sum_{t=1}{T} \\delta_t < +\\infty$).\n", "\n", "Then we consider the following test: the non-parametric sub-Gaussian Generalized Likelihood Ratio test (GLR) works like this:\n", "\n", "- For each *time step* $s$ between $t_0=0$ and $t$, compute:\n", "$$G^{\\text{sub-}\\sigma}_{t_0:s:t} = |\\mu_{t_0,s} - \\mu_{s+1,t}|.$$\n", "\n", "- The change is detected if there is a time $s$ such that $G^{\\text{sub-}\\sigma}_{t_0:s:t} > b_{t_0}(s,t,\\delta)$, where $b_{t_0}(s,t,\\delta)$ is the threshold of the test,\n", "- And $\\mu_{a,b} = \\frac{1}{b-a+1} \\sum_{s=a}^{b} y_s$ is the mean of the samples between $a$ and $b$.\n", "\n", "The threshold is computed as either the \"joint\" variant:\n", "$$b^{\\text{joint}}_{t_0}(s,t,\\delta) := \\sigma \\sqrt{ \\left(\\frac{1}{s-t_0+1} + \\frac{1}{t-s}\\right) \\left(1 + \\frac{1}{t-t_0+1}\\right) 2 \\log\\left( \\frac{2(t-t_0)\\sqrt{t-t_0+2}}{\\delta} \\right)}.$$\n", "or the \"disjoint\" variant:\n", "\n", "$$b^{\\text{disjoint}}_{t_0}(s,t,\\delta) := \\sqrt{2} \\sigma \\sqrt{\n", " \\frac{1 + \\frac{1}{s - t_0 + 1}}{s - t_0 + 1} \\log\\left( \\frac{4 \\sqrt{s - t_0 + 2}}{\\delta}\\right)\n", " } + \\sqrt{\n", " \\frac{1 + \\frac{1}{t - s + 1}}{t - s + 1} \\log\\left( \\frac{4 (t - t_0) \\sqrt{t - s + 1}}{\\delta}\\right)\n", " }.$$\n" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "# Default confidence level?\n", "DELTA = 0.01\n", "\n", "# By default, assume distributions are 0.25-sub Gaussian, like Bernoulli\n", "# or any distributions with support on [0,1]\n", "SIGMA = 0.25" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "# Whether to use the joint or disjoint threshold function\n", "JOINT = True" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "code_folding": [ 2 ] }, "outputs": [], "source": [ "from math import log, sqrt\n", "\n", "def threshold_SubGaussianGLR_joint(t0, s, t, delta=DELTA, sigma=SIGMA):\n", " return sigma * sqrt(\n", " (1.0 / (s - t0 + 1) + 1.0/(t - s)) * (1.0 + 1.0/(t - t0+1))\n", " * 2 * max(0, log(( 2 * (t - t0) * sqrt(t - t0 + 2)) / delta ))\n", " )" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "code_folding": [ 2 ] }, "outputs": [], "source": [ "from math import log, sqrt\n", "\n", "def threshold_SubGaussianGLR_disjoint(t0, s, t, delta=DELTA, sigma=SIGMA):\n", " return np.sqrt(2) * sigma * (sqrt(\n", " ((1.0 + (1.0 / (s - t0 + 1))) / (s - t0 + 1)) * max(0, log( (4 * sqrt(s - t0 + 2)) / delta ))\n", " ) + sqrt(\n", " ((1.0 + (1.0 / (t - s + 1))) / (t - s + 1)) * max(0, log( (4 * (t - t0) * sqrt(t - s + 1)) / delta ))\n", " ))" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "code_folding": [ 2 ] }, "outputs": [], "source": [ "def threshold_SubGaussianGLR(t0, s, t, delta=DELTA, sigma=SIGMA, joint=JOINT):\n", " if joint:\n", " return threshold_SubGaussianGLR_joint(t0, s, t, delta, sigma=sigma)\n", " else:\n", " return threshold_SubGaussianGLR_disjoint(t0, s, t, delta, sigma=sigma)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we can write the CD algorithm:" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "code_folding": [ 3 ] }, "outputs": [], "source": [ "class SubGaussianGLR(ChangePointDetector):\n", " def __init__(self, delta=DELTA, sigma=SIGMA, joint=JOINT):\n", " super().__init__(delta=delta, sigma=sigma, joint=joint)\n", " \n", " def __str__(self):\n", " return fr\"SubGaussian-GLR($\\delta=${self.delta:.3g}, $\\sigma=${self.sigma:.3g}, {'joint' if self.joint else 'disjoint'})\"\n", "\n", " def detect(self, all_data, t):\n", " r\"\"\" Detect a change in the current arm, using the non-parametric sub-Gaussian Generalized Likelihood Ratio test (GLR) works like this:\n", "\n", " - For each *time step* :math:`s` between :math:`t_0=0` and :math:`t`, compute:\n", " \n", " .. math:: G^{\\text{sub-}\\sigma}_{t_0:s:t} = |\\mu_{t_0,s} - \\mu_{s+1,t}|.\n", "\n", " - The change is detected if there is a time :math:`s` such that :math:`G^{\\text{sub-}\\sigma}_{t_0:s:t} > b_{t_0}(s,t,\\delta)`, where :math:`b_{t_0}(s,t,\\delta)` is the threshold of the test,\n", "\n", " The threshold is computed as:\n", " \n", " .. math:: b_{t_0}(s,t,\\delta) := \\sigma \\sqrt{ \\left(\\frac{1}{s-t_0+1} + \\frac{1}{t-s}\\right) \\left(1 + \\frac{1}{t-t_0+1}\\right) 2 \\log\\left( \\frac{2(t-t_0)\\sqrt{t-t_0+2}}{\\delta} \\right)}.\n", "\n", " - And :math:`\\mu_{a,b} = \\frac{1}{b-a+1} \\sum_{s=a}^{b} y_s` is the mean of the samples between :math:`a` and :math:`b`.\n", " \"\"\"\n", " data = all_data[:t]\n", " t0 = 0\n", " horizon = len(all_data)\n", " delta = self.delta\n", " if delta is None:\n", " delta = 1.0 / max(1, horizon)\n", "\n", " mean_before = 0\n", " mean_after = np.mean(data[t0 : t+1])\n", " for s in range(t0, t):\n", " # DONE okay this is efficient we don't compute the same means too many times!\n", " y = data[s]\n", " mean_before = (s * mean_before + y) / (s + 1)\n", " mean_after = ((t + 1 - s + t0) * mean_after - y) / (t - s + t0)\n", "\n", " # compute threshold\n", " threshold = threshold_SubGaussianGLR(t0, s, t, delta=delta, sigma=self.sigma, joint=self.joint)\n", " glr = abs(mean_before - mean_after)\n", " if glr >= threshold:\n", " # print(f\"DEBUG: t0 = {t0}, t = {t}, s = {s}, horizon = {horizon}, delta = {delta}, threshold = {threshold} and mu(s+1, t) = {mu(s+1, t)}, and mu(t0, s) = {mu(t0, s)}, and and glr = {glr}.\")\n", " return True\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List of all Python algorithms" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "all_CD_algorithms = [\n", " Monitored, CUSUM, PHT,\n", " GaussianGLR, BernoulliGLR, SubGaussianGLR\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing the different implementations\n", "I now want to compare, on a simple non stationary problem, the efficiency of the different change detection algorithms, in terms of:\n", "\n", "- speed of computations (we should see that naive Python is much slower than Numba, which is also slower than the Cython version),\n", "- memory of algorithms? I guess we will draw the same observations,\n", "\n", "But most importantly, in terms of:\n", "\n", "- detection delay, as a function of the amplitude of the breakpoint, or number of prior data (from $t=1$ to $t=\\tau$), or as a function of the parameter(s) of the algorithm,\n", "- probability of false detection, or missed detection." ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": [ "def str_of_CDAlgorithm(CDAlgorithm, *args, **kwargs):\n", " detector = CDAlgorithm(*args, **kwargs)\n", " return str(detector)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating some toy data" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "# With 1 arm only! With 1 change only!\n", "toy_problem_piecewise = lambda firstMean, secondMean, tau: lambda horizon: {\n", " \"listOfMeans\": [\n", " [firstMean], # 0 to 499\n", " [secondMean], # 500 to 999\n", " ],\n", " \"changePoints\": [\n", " 0,\n", " tau\n", " ],\n", "}" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [], "source": [ "def get_toy_data(firstMean=0.5, secondMean=0.9, tau=None, horizon=100, gaussian=False):\n", " if tau is None:\n", " tau = horizon // 2\n", " elif isinstance(tau, float):\n", " tau = int(tau * horizon)\n", " problem = toy_problem_piecewise(firstMean, secondMean, tau)\n", " if gaussian:\n", " data = piecewise_gaussian_samples(problem, horizon=horizon)\n", " else:\n", " data = piecewise_bernoulli_samples(problem, horizon=horizon)\n", " data = data.reshape(horizon)\n", " return data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is now very easy to get data and \"see\" manually on the data the location of the breakpoint:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.,\n", " 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n", " 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1.,\n", " 0., 1., 0., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_toy_data(firstMean=0.1, secondMean=0.9, tau=0.5, horizon=100)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_toy_data(firstMean=0.1, secondMean=0.9, tau=0.2, horizon=100)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n", " 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n", " 1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0., 1.,\n", " 0., 0., 1., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n", " 1., 0., 0., 1., 1., 0., 0., 1., 0., 1., 0., 1., 0., 0., 0.])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_toy_data(firstMean=0.1, secondMean=0.4, tau=0.5, horizon=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And similarly for Gaussian data, we clearly see a difference around the middle of the vector:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.28359652, -0.01616356, -0.48312871, 0.31544065, 0.14213706,\n", " 0.30068739, 0.13366152, 0.15525014, 0.15127236, 0.8348038 ,\n", " 0.86049004, 1.28897058, 0.80222673, 0.83035987, 1.26805377,\n", " 0.9509542 , 1.00407429, 1.65299813, 1.2069301 , 1.06948091])" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_toy_data(firstMean=0.1, secondMean=0.9, tau=0.5, horizon=20, gaussian=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, we want to check that detecting the change becomes harder when:\n", "\n", "- the gap $\\Delta = |\\mu^{(2)} - \\mu^{(1)}|$ decreases,\n", "- the number of samples before the change decreases ($\\tau$ decreases),\n", "- the number of samples after the change decreases ($T - \\tau$ decreases)." ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "# Cf. https://stackoverflow.com/a/36313217/\n", "from IPython.display import display, Markdown" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [], "source": [ "def check_onemeasure(measure, name,\n", " firstMean=0.1,\n", " secondMean=0.4,\n", " tau=0.5,\n", " horizon=100,\n", " repetitions=50,\n", " gaussian=False,\n", " unit=\"\",\n", " list_of_args_kwargs=None,\n", " CDAlgorithms=None,\n", " ):\n", " if CDAlgorithms is None:\n", " CDAlgorithms = tuple(all_CD_algorithms)\n", " if isinstance(tau, float):\n", " tau = int(tau * horizon)\n", " print(f\"\\nGenerating toy {'Gaussian' if gaussian else 'Bernoulli'} data for mu^1 = {firstMean}, mu^2 = {secondMean}, tau = {tau} and horizon = {horizon}...\")\n", " results = np.zeros((repetitions, len(CDAlgorithms)))\n", " list_of_args = [tuple() for _ in CDAlgorithms]\n", " list_of_kwargs = [dict() for _ in CDAlgorithms]\n", " for rep in tqdm(range(repetitions), desc=\"Repetitions\"):\n", " data = get_toy_data(firstMean=firstMean, secondMean=secondMean, tau=tau, horizon=horizon, gaussian=gaussian)\n", " for i, CDAlgorithm in enumerate(CDAlgorithms):\n", " if list_of_args_kwargs:\n", " list_of_args[i], list_of_kwargs[i] = list_of_args_kwargs[i]\n", " results[rep, i] = measure(data, tau, CDAlgorithm, *list_of_args[i], **list_of_kwargs[i])\n", " # print and display a table of the results\n", " markdown_text = \"\"\"\n", "| Algorithm | {} |\n", "|------|------|\n", "{}\n", " \"\"\".format(name, \"\\n\".join([\n", " \"| {} | ${:.3g}${} |\".format(\n", " str_of_CDAlgorithm(CDAlgorithm, *list_of_args[i], **list_of_kwargs[i]),\n", " mean_result, unit\n", " )\n", " for CDAlgorithm, mean_result in zip(CDAlgorithms, np.mean(results, axis=0))\n", " ]))\n", " print(markdown_text)\n", " display(Markdown(markdown_text))\n", " return results" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "def eval_CDAlgorithm(CDAlgorithm, data, t, *args, **kwargs):\n", " detector = CDAlgorithm(*args, **kwargs)\n", " return detector.detect(data, t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking time efficiency\n", "I don't really care about memory efficiency, so I won't check it." ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [], "source": [ "import time" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": [ "def time_efficiency(data, tau, CDAlgorithm, *args, **kwargs):\n", " startTime = time.time()\n", " horizon = len(data)\n", " for t in range(0, horizon + 1):\n", " _ = eval_CDAlgorithm(CDAlgorithm, data, t, *args, **kwargs)\n", " endTime = time.time()\n", " return endTime - startTime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To benchmark each of the CD algorithm, we can use the [`line_profiler`](https://github.com/rkern/line_profiler#line_profiler) module and it's `lprun` magic." ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "scrolled": true }, "outputs": [], "source": [ "!pip3 install line_profiler >/dev/null" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 100 and horizon = 200...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e3f4fc21a94427887077de34ce13974", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.00534$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.00534$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%lprun -f Monitored.detect check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=200, unit=\" seconds\", CDAlgorithms=[Monitored])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$20\\%$ of the time is spent computing the threshold, and $55\\%$ is spent computing the two sums: we cannot optimize these!" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 100 and horizon = 200...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "18125917538d44ecb616a0b72b515bcc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.137$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.137$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%lprun -f CUSUM.detect check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=200, unit=\" seconds\", CDAlgorithms=[CUSUM])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$10\\%$ of the time is spent computing the threshold, and about $10\\%$ to $25\\%$ are spent on the few maths computations that can hardly be optimized." ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 100 and horizon = 200...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a4eb61f92caf4f6380faf5e3b2629172", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.224$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.224$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%lprun -f PHT.detect check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=200, unit=\" seconds\", CDAlgorithms=[PHT])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$10\\%$ of the time is spent computing the threshold, and about $10\\%$ to $25\\%$ are spent on the few maths computations that can hardly be optimized." ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 100 and horizon = 200...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "89c33eabb8034ee58fe90223ff4bfdf1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.399$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.399$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%lprun -f GaussianGLR.detect check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=200, unit=\" seconds\", CDAlgorithms=[GaussianGLR])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$30\\%$ of the time is spent computing the threshold (at every step it must be recomputed!), and about $10\\%$ to $25\\%$ are spent on the few maths computations that can hardly be optimized." ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 100 and horizon = 200...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c116573d035b47eea18e97b92bcd3424", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.307$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.307$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%lprun -f BernoulliGLR.detect check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=200, unit=\" seconds\", CDAlgorithms=[BernoulliGLR])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$30\\%$ of the time is spent computing the threshold (at every step it must be recomputed!), and about $10\\%$ to $25\\%$ are spent on the few maths computations that can hardly be optimized." ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 100 and horizon = 200...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a45ae607cce0497292cacc0c049b4ff3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.176$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.176$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%lprun -f SubGaussianGLR.detect check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=200, unit=\" seconds\", CDAlgorithms=[SubGaussianGLR])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$30\\%$ of the time is spent computing the threshold (at every step it must be recomputed!), and about $10\\%$ to $25\\%$ are spent on the few maths computations that can hardly be optimized." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For examples:" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c7b52d4cb8c741d987be7ce72dc605e4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000568$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.00856$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0198$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0265$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0163$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.0141$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000568$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.00856$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0198$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0265$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0163$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.0141$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=100, unit=\" seconds\")" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Gaussian data for mu^1 = 0.1, mu^2 = 0.4, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c1d9ca45b5c34f05949ffc4dde9c39db", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000694$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0109$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0256$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0359$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0224$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.0182$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000694$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0109$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0256$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0359$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0224$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.0182$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=100, unit=\" seconds\", gaussian=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two `GLR` are very slow, compared to the `Monitored` approach, and slow compared to `CUSUM` or `PHT`!" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.2, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000693$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0117$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0276$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0378$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0237$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.024$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000693$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0117$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0276$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0378$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0237$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.024$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.2 s, sys: 28 ms, total: 6.23 s\n", "Wall time: 6.43 s\n" ] } ], "source": [ "%%time\n", "_ = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.2, tau=0.5, horizon=100, unit=\" seconds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compare the results for $T=100$, $T=500$, $T=1000$:" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "59e0f269f9a94387953b0e4cbe721a91", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000614$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.00824$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0214$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0188$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0115$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.00945$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.000614$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.00824$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.0214$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.0188$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.0115$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.00945$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.59 s, sys: 12 ms, total: 3.6 s\n", "Wall time: 3.59 s\n" ] } ], "source": [ "%%time\n", "results_T100 = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=100, unit=\" seconds\")" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 250 and horizon = 500...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "deeae078de7149e3a068b0bd8cf3f67d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.00808$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.251$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.374$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.333$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.194$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.164$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.00808$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.251$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.374$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0.333$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.194$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.164$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 6s, sys: 87.6 ms, total: 1min 6s\n", "Wall time: 1min 6s\n" ] } ], "source": [ "%%time\n", "results_T500 = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=500, unit=\" seconds\")" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 500 and horizon = 1000...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.0162$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.943$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $1.3$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $1.1$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.635$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.552$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.0162$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0.943$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $1.3$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $1.1$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0.635$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.552$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 47s, sys: 319 ms, total: 3min 47s\n", "Wall time: 3min 47s\n" ] } ], "source": [ "%%time\n", "results_T1000 = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=1000, unit=\" seconds\")" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 1000 and horizon = 2000...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=10, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2920: RuntimeWarning: Mean of empty slice.\n", " out=out, **kwargs)\n", "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.0321$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $3.71$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $5.15$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $3.96$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $2.27$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $2.05$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.0321$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $3.71$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $5.15$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $3.96$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $2.27$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $2.05$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 51s, sys: 283 ms, total: 2min 51s\n", "Wall time: 2min 51s\n" ] } ], "source": [ "%%time\n", "results_T2000 = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=2000, repetitions=10, unit=\" seconds\")" ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 1250 and horizon = 2500...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "447dfcfb6096488e967a6913a74e100f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=10, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2920: RuntimeWarning: Mean of empty slice.\n", " out=out, **kwargs)\n", "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.0484$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $5.74$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $7.52$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $5.5$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $3.41$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $3.18$ seconds |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Time |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.0484$ seconds |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $5.74$ seconds |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $7.52$ seconds |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $5.5$ seconds |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $3.41$ seconds |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $3.18$ seconds |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4min 13s, sys: 239 ms, total: 4min 14s\n", "Wall time: 4min 14s\n" ] } ], "source": [ "%%time\n", "results_T2500 = check_onemeasure(time_efficiency, \"Time\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=2500, repetitions=10, unit=\" seconds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The three `GLR` and `CUSUM` and `PHT` are comparable, and the Bernoulli `GLR` is essentially the most efficient, except for `Monitored` which is the only one to be way faster." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When going from a horizon of $T=100$ to $T=500$ and $T=1000$, we see that `Monitored` time complexity is essentially constant, while the complexity of `CUSUM`, `PHT` and all the `GLR` tests blows up quadratically:" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [], "source": [ "data_X = np.array([100, 500, 1000, 2000, 2500])\n", "data_Y = [\n", " [\n", " np.mean(results_T100, axis=0)[i],\n", " np.mean(results_T500, axis=0)[i],\n", " np.mean(results_T1000, axis=0)[i],\n", " np.mean(results_T2000, axis=0)[i],\n", " np.mean(results_T2500, axis=0)[i],\n", " ]\n", " for i in range(len(all_CD_algorithms))\n", "]" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Text(0.5, 0, 'Time horizon $T$')" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Text(0, 0.5, 'Time complexity in seconds')" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Comparison of time complexity efficiency of different CD algorithms')" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hUJijelIKhUKhKLYU/80Fs6FSpUrS3d3d2mooFArFA8WxY8eipJQ5fdBfLHkgjZS7uztHjx61thoKhULxQCGEKMjuH1ZBDfcpFAqFotiijJRCoVAoii3KSCkUCoWi2PJAzkllR3JyMlevXiUxMdHaqigKCUdHR2rUqIGdnZ21VVEoFEVEiTFSV69epUyZMri7u5P99m2KBxkpJdHR0Vy9epU6depYWx2FQlFElJjhvsTERCpWrKgMVAlFCEHFihVVT1nx0LLj4g66bumKx1oPum7pyo6LO6ytUpFQYnpSgDJQJRx1fxUPKzsu7mD679NJTNFe0sLiw5j++3QAetTtYUXNCp8S05NSKBSKksqy48tMBiqNxJRElh1fZiWNig5lpCyIEIJXX033+mA0GnF1daVnz573nWebNm0ACA4OZsOGDQXWMSemT5/OokWLCi1/hUJx/4TFh2Urvx5/PVt5SeKhNVI/nAil7by91Jmwg7bz9vLDiYJ4Q9coXbo0Z86cISEhAYDdu3dTvXpOzmLzx++/ax6v78dIGY3GApWtUCisz4azOT/3VUpXKUJNrMNDaaR+OBHKxO9PE3orAQmE3kpg4venLWKounfvzo4d2oTmxo0b6d+/vynsxo0b9O7dGw8PD1q3bs2pU6cArRczcOBAfH19qVu3LsuXLzelcXZ2BmDChAkcOHAALy8vlixZQmJiIm+88QbNmjXD29ubffv2AbBmzRp69epFp06d6Ny5MwALFy6kZcuWeHh4MG1aulfqOXPm8Oijj9KuXTvOnTtX4HNXKBSW5b///Je5R+ZmG+ZocGRM8zFFrFHRU6IWTqThPuHeV70kJKcw9psAxn4TkGu84Hm5T1L269ePmTNn0rNnT06dOsXAgQM5cOAAANOmTcPb25sffviBvXv38vrrrxMQoJUXGBjIvn37iI2NpUGDBgwfPjzD90Dz5s1j0aJFbN++HYDFixcjhOD06dMEBgbStWtXzp8/D8Dx48c5deoUFSpUYNeuXQQFBXHkyBGklPTq1Yv9+/dTunRpNm3aREBAAEajkebNm9OiRYt7vm4KhaJwWHNmDYuPLTYd1yxTk6SUJCLuRFCldBXGNB9T4hdNQAk1UtbEw8OD4OBgNm7cSPfu3TOEHTx4kO+++w6ATp06ER0dze3btwHo0aMHDg4OODg44ObmRnh4ODVq1MixnIMHDzJq1CgAGjZsSO3atU1G6sknn6RChQoA7Nq1i127duHt7Q1AXFwcQUFBxMbG8txzz1GqVCkAevXqZcGroFAoCsLq06szLIrwdvPm086f4mzvbEWtrIMyUoVAr169ePfdd/H39yc6OjpfaRwcHEz/GwyGAs0nlS5d2vS/lJKJEycydOjQDHGWLl163/krFIrC4/OTn/NxwMem4xaVW/Bp508pZVfKilpZjxJppPIakkubk0pITjHJnOwMzH2+Gb29C7bQAWDgwIGUK1eOZs2a4e/vb5K3b9+e9evXM3XqVPz9/alUqRJly5bNV55lypQhNjY2S16dOnXi/PnzXLlyhQYNGnD8+PEM6bp168bUqVN55ZVXcHZ2JjQ0FDs7Ozp06MCAAQOYOHEiRqORbdu2ZTFkCoWi6JBS8tnJz/js5Gcm2eNVHuejTh89tAYKSqiRyos0Q7Twl3Ncu5VAtXJOvNetgUUMFECNGjUYPXp0FnnaAgkPDw9KlSrF2rVr852nh4cHBoMBT09PBgwYwFtvvcXw4cNp1qwZtra2rFmzJkNvLI2uXbty9uxZfHx8AG0hxrp162jevDl9+/bF09MTNzc3WrZsef8nrFAoCoSUko9OfMSq06tMstZVW7O803KcbJ2sqJn1EVJKa+twzzz22GMys9PDs2fP0qhRIytppCgq1H1WlDSklCw9vpQvz3xpkrWt1palHZfiaOto0bKEEMeklI9ZNNNC5qHsSSkUCkVxQErJ4qOLWftP+qhK++rtWdJxCQ6GrCMjDyPKSCkUCoUVkFKy4K8FrDu7ziTzrenL4icWY2+wt6JmxYti8TGvEKKBECLA7HdbCDHW2nopFApFYZAqU5nz55wMBqpzrc58+MSHykBlolj0pKSU5wAvACGEAQgFtlpVKYVCoSgEUmUqs/+Yzebzm02yJ2s/yfwO87GzUQ49M1MsjFQmOgMXpJSXra2IQqFQWJJUmcqMwzP4Puh7k+xp96f5T/v/YGtTHJtj61Mshvsy0Q/YmFkohHhTCHFUCHE0MjLSCmopFArF/ZOSmsLUQ1MzGKiedXsqA5UHxcpICSHsgV7A5sxhUsqVUsrHpJSPubq6Fr1y+eT69ev069ePevXq0aJFC7p3787KlSuzuOsYMGAAW7ZsAWD79u14e3vj6elJ48aN+fzzz7PESSNtw9ng4GCEEEyZMsUUFhUVhZ2dHSNHjizMU1QoFPeIMdXI5EOT+enCTyZZr3q9mN12tjJQeVCsjBTwNHBcShle6CWd+haWNIXp5bS/p74tcJZSSp577jl8fX25cOECx44dY+7cuYSH53w6ycnJvPnmm2zbto2TJ09y4sQJfH1981VenTp1TDuuA2zevJkmTZoU9DQUCoUFMaYamXRgUgZ378/Xf55ZbWdhsDFYUbMHg+JmpPqTzVCfxTn1LWwbDTEhgNT+bhtdYEO1b98+7OzsGDZsmEnm6elJ+/btc0wTGxuL0WikYsWKgLaHX4MGDfJVXqlSpWjUqBFpHzZ/88039OnTpwBnoFAoLElyajLj94/n5+CfTbKXHn2JaT7TsBHFrfktnhSbfqYQojTwJFDwDeSmu9x7muQE+H6I9ss175gcg86cOXPP7i4qVKhAr169qF27Np07d6Znz570798fG5v8VeB+/fqxadMmKleujMFgoFq1aly7du2edFAoFJYnOSWZ9/a/x54re0yyfg36ManVJIQQVtTswaLYmHIpZbyUsqKUMmcr8ICSU4VMk69evZo9e/bw+OOPs2jRIgYOHJhjusyyp556it27d7Np0yb69u1rYc0VCsX9kJSSxDj/cRkM1KuNXlUG6j4oNkaqJNCkSROOHTuWRV6xYkVu3ryZQXbjxg0qVapkOm7WrBlvv/02u3fvNvmcypwucxoAe3t7WrRoweLFi3nxxRcteToKheI+uJtyl7H7xuJ/1d8k82vsx/iW45WBug+KzXCfRcllSA5In5NKTkiX2TnBM8vB4/7ndDp16sSkSZNYuXIlb775plbUqVPcvHmTa9eumTZHvXz5MidPnsTLy4u4uDiOHj1qWiwREBBA7dq1AfD19WXp0qX4+flhb2/PmjVr6NixY5Zy33nnHZ544gmTo0OFQmEdEo2JjN03lkPXDplkA5sOZGzzscpA3Scl00jlRZoh2jMTYq6CSw3o/EGBDBRoQ3Fbt25l7NixzJ8/H0dHR9zd3Vm6dCnr1q3jjTfeIDExETs7O1avXo2LiwuxsbEsWLCAoUOH4uTkROnSpVmzZg0APXv25NixY7Ro0QKDwUC9evVYsWJFlnKbNGmiVvUpFFYmwZjA6L2j+SPsD5NsSLMhjPIepQxUAVCuOhQPFOo+K4ojd5LvMGrvKI5cP2KSveX5FsM8hxUrA6VcdSgUCsVDRnxyPG/9+hbHI9K9Yo/0GslQT+Xp2hIoI6VQKBT3SVxSHG/teYsTESdMsjHNxzC42WAralWyUEZKoVAo7oPYpFiG/TqMU5GnTLJ3H3sXvyZ+VtSq5KGMlEKhUNwjMXdjGLZ7GGeiz5hk77d8n1cbv2pFrUomykgpFArFPRBzN4Yhu4Zw9sZZk2xSq0n0b9jfilqVXJSRUigUinxyM/Emb+5+k8AbgSbZ1NZT6dNA7ZlZWCgjZUEMBgPNmjXDaDTSqFEj1q5dS6lSpXB2diYuLs4Ub82aNRw9epSqVauyebPmleT06dM0a9YMgIEDBzJ69GirnINCocie6IRohuweQtDNIAAEgultpvN8/eetrFnJxqLbIgkh6gkhHPT/fYUQo4UQ5SxZhqXYcXEHXbd0xWOtB123dM2wjf794uTkREBAAGfOnMHe3j7bD2/NmTx5MgEBAQQEBJjSBgQEKAOlUBQzohKiGPTLoAwGambbmcpAFQGW3rvvOyBFCPEIsBKoCWywcBkFZsfFHUz/fTph8WFIJGHxYUz/fbpFDFUa7du3599//7VYfgqFwjpE3olk4C8DuRBzAQAbYcOcdnPo/UhvK2v2cGDp4b5UKaVRCPEc8JGU8iMhxIk8U1mYZmub3XOaxJREJhyYwIQDE3KNd9rvdJ55GY1Gfv75Z5566ikAEhIS8PLyMoXfuHGDXr163bOOCoWiaAmPD2fwrsEE3w4GwCAM/Kfdf+het7t1FXuIsLSRShZC9Af8gGd0mZ2Fyyi2mBuj9u3bM2jQICB9GDCNtDkphUJRfLkef52BvwwkJDYE0AzU/A7z6ebezcqaPVxY2ki9AQwD5kgpLwkh6gD/tXAZxZbMxkihUDyYXIu7xsBfBhIaFwqArbBl4RML6VK7i5U1e/iwqJGSUv4DjDY7vgTMt2QZ+SGvIbm0OanElESTzNHgyPQ20+lRt0dhq6dQKIoxV2OvMuiXQVyL1zxc29rYsviJxXSq1cnKmj2cWMRICSFOAzlupy6l9MhHHuWA1UBTPa+BUsrDltAvM2mGaNnxZVyPv06V0lUY03yMMlAKxUPOldtXGLRrENfjrwNgZ2PH0o5L6VCjg5U1e3ixiKsOIURt/d8R+t+0Ib5XASmlzH01gpbHWuCAlHK1EMIeKCWlvJVdXOWq4+FF3WdFYREcE8ygXYOIuBMBgL2NPcs6LaNd9XZW1sxyPLSuOqSUlwGEEE9KKb3Ngt4XQhwHcjVSQggXoAMwQM8vCUiyhG4KhUKRFxdjLjL4l8FEJkQC4GBwYHmn5bSp1sbKmiks/Z2UEEK0NTtok88y6gCRwFdCiBNCiNVCiNKZMn5TCHFUCHE0MjLSslorFIqHln9v/svAnQNNBsrJ1olPOn+iDFQxwdJGahDwqRAiWAhxGfgUGJiPdLZAc+AzvScWT6bel5RypZTyMSnlY66urhZWW6FQPIycv3meQbsGEZ0YDaQbqFZVW1lZM0Uall7ddwzw1IfvkFLG5DPpVeCqlPJP/XgLeQwRKhQKRUE4d+Mcg3cN5tZdbeq7lG0pPuvyGc0rN7eyZgpzLGqk9H37XgDcAVshBABSypm5pZNSXhdChAghGkgpzwGdgX8sqZtCoVCk8U/0P7y5+01i7mrv0c52znzW5TO83LzySKkoaiz9Me+PQAxwDLh7j2lHAev1lX0X0T4MVigUCotyJuoMb+5+k9ikWADK2JXh8yc/p5nrvW+nVqSc+hb2zISYq+BSAzp/AB4l30WIpeekakgp+0opF0gpF6f98pNQShmgzzl5SCl7SylvWli3IiE8PJyXX36ZunXr0qJFC3x8fNi6dWuhlnn06NEC7ZweFxfH8OHDqVevHs2bN6dFixasWrUKgODgYJo2bZolzYABA6hTpw5eXl54enqyZ8+e+y5foSgqTkaeZMiuISYDVda+LKu6rXowDNS20RATAkjt77bRmryEY2kj9bsQopjfbY2YbdsI6tSZs40aE9SpMzHbthU4TyklvXv3pkOHDly8eJFjx46xadMmrl69agGNc+axxx5j+fLl951+8ODBlC9fnqCgII4fP87OnTu5ceNGnukWLlxIQEAAS5cuZdiwYfddvkJRFAREBDB091DikjXfbuUcyrG662qaVGxiZc3ywa/TITkhoyw5QetZlXAsbaTaAceEEOeEEKeEEKeFEKcsXEaBidm2jbCpH2C8dg2kxHjtGmFTPyiwodq7dy/29vYZGuzatWszatQogoODad++Pc2bN6d58+b8/vvvAPj7+9OzZ09T/JEjR7JmzRoAJkyYQOPGjfHw8ODdd98FYPPmzTRt2hRPT086dOiQJY8jR47g4+ODt7c3bdq04dy5c4C2qe3zzz/PU089Rf369Rk/fjwAFy5c4MiRI8yePRsbG606uLq68v777+f7vH18fAgNDb2fS6ZQFAnHwo8xdPdQ4pPjASjvUJ7VXVfTqOID8GH47WtwO4fnK6ZwX4CLA5aek3rawvndF2cb3nvFk4lDLc0nAAAgAElEQVSJXHtvPNfeG59rvEaBZ3MM+/vvv2nePPuVQW5ubuzevRtHR0eCgoLo379/rjuhR0dHs3XrVgIDAxFCcOuWtgJp5syZ/PLLL1SvXt0kM6dhw4YcOHAAW1tbfv31VyZNmsR3330HQEBAACdOnMDBwYEGDRowatQo/v77bzw9PU0G6n7YuXMnvXsr3zqK4slf1/9ixJ4RJBi1nkgFxwqs7rqa+uXrW1mzfHDtBGzsn3O4S42i08VKWHoJ+mUhhCfQXhcdkFKetGQZDxIjRozg4MGD2Nvb8+uvvzJy5EgCAgIwGAycP38+17QuLi44OjoyaNAgevbsaeoptW3blgEDBtCnTx+efz6rV9CYmBj8/PwICgpCCEFycrIprHPnzri4uADQuHFjLl++nCX9nDlz2Lx5MxEREVy7di1XHd977z0mTZrE1atXOXy4ULZZVCgKxB9hfzBqzyjTZtKVnCrxRdcvqFuurpU1ywf//ATfvwnGhOzD7Zy0xRMlHEu7jx8DrAfc9N86IcQoS5ZRnGnSpAnHjx83HX/yySfs2bOHyMhIlixZQuXKlTl58iRHjx4lKUnb9cnW1pbU1FRTmsTERJP8yJEjvPjii2zfvt3kQHHFihXMnj2bkJAQWrRoQXR0dAYdpk6dSseOHTlz5gzbtm0z5Qfg4OBg+t9gMGA0GmncuDEnT5406ZDm0v727dt5nu/ChQs5f/488+fPZ+DA/HyzrVAUHYdCDzFyz0iTgXJzcuPLbl8WfwMlJRxYDN++lm6gHF2IsX+eoO3VOLupKkHbqxFTbtBDsbrP0sN9g4BWUsp4ACHEfOAw8JGFy8mV3IbkIH1OSpo14MLRkaqzZuLyzDO5pMydTp06MWnSJD777DOGDx8OwJ07d7QyY2KoUaMGNjY2rF27lpSUFECbs/rnn3+4e/cuCQkJ7Nmzh3bt2hEXF8edO3fo3r07bdu2pW5d7cG6cOECrVq1olWrVvz888+EhIRkPLeYGKpXrw5gmtvKjUceeYTHHnuMKVOmMGvWLAwGA4mJidzLxsMjR47kyy+/5JdffqFbN+UQTmF99l/dz9v73iYpVXsZrFyqMl92+5JaZWtZWbM8MN6FbWPg5MZ0WYW6xFR6i7AFnyITAQTGOAhb9T+o1bpAbdaDgMX37gNSzI5TdFmxwuWZZ6g6aya21aqBENhWq1ZgAwUghOCHH37gt99+o06dOjz++OP4+fkxf/583nrrLdauXYunpyeBgYGULq1tTVizZk369OlD06ZN6dOnD97e2v68sbGx9OzZEw8PD9q1a8eHH34IaENszZo1o2nTprRp0wZPT88MOowfP56JEyfi7e2N0WjMl96rV68mOjraZLCefPJJFixYYAo/d+4cNWrUMP02b96c5bynTJmSIY1CYS38Q/wZu2+syUBVLV2Vr576qvgbqPho+PrZjAaqdjsYvIeIlRsyvFSDNo8esWRpEStZ9FjEVYcpMyHGobmOT/swqDewRkpp0SupXHU8vKj7rMiNPVf28O5v72JM1V7QqjtX54tuX1DdubqVNcuDiEDY0Adumc0Te7+K7P4hsXv8CR07Nvt0QtDobP4353loXXWkIaX8UAjhj7YUHeANKeUJS5ahUCgU2bEreBfv738fo9QMVA3nGnzZ7UuqOle1smZ58O+vsPkNuJs2DyzgyZkk132J62PGEbd3b45JbasW83OzAJbeu6818LeU8rh+XFYI0cps41iFQqGwODsv7WTCgQmkSG22oVaZWnzR7QuqlK5iZc3y4Mgq+Pl90PXGrhTyuZXcOhlHxOhnSI2PzzGpcHTE7e0celglCEsvnPgMzeVGGnHZyBQKhcJi7Li4g0kHJ5EqtRWq7mXd+aLbF7iVcrOyZrmQYoRfJsKRlemystW522YRYbPXk2C2ShigXN++ODZpTNSKzzGGhWFbtSpub48t8YsmwPJGSkizSS4pZaoQwtJlKBQKBQA/XfiJqYemmgxUXZe6fNHtCyo5VbKyZrmQGKMN711I3+8y1c2b6MSniR4yAWn2baO9uztVZ82kVMuWAJTvU/KXnGfG0gbkohBiNFrvCeAttB3NFQqFwqJsDdrKtN+nIdHeix8p9wiru66molNFK2uWCzcuwcZ+EBloEt1x7kzYjrskXViTHs/WlopDBlNp2DBszL5vfBixtJEaBiwHpgAS2AO8aeEyFArFQ87m85uZeTh9c9VHyz/Kqq6rqOBYwYpa5cHlw/DNK3BH+wA/JVkQGfUEN/cHah/w6jh6elB15iwcGzxqLU2LFRb9TkpKGSGl7CeldJNSVpZSviyljLBkGcUZg8Fgcl1hvomsNTB3sWG+Ae1PP/3EvHnzTPHWrVuHh4cHTZo0wdPTk8GDB5v2BPT19c2yv6C/vz8uLi54eXnRsGFD08a3CkVRsOPiDtptbJfBQDWq0Igvun5RvA1UwEb4upfJQMWGOXPRvwE3fztvMlCiVCkqT5qE+4YNykCZYenVfY+iDfVVllI2FUJ4AL2klLMtWY4lOP/ndQ7/eIG4G3dxruCAz7P1eLRVwVYCOTk5ERAQAMAvv/zCxIkT+e233/KVVkqJlLJAG73mh169etGrVy9A2xh2yZIl/Pzzz1SvXp2UlBTWrl1LeHg45cqVyzGP9u3bs337dhISEvD29ua5556jbdu2haq3QrH9wnamHppqWmIOIBC89OhLlHPMub5aldRU2Ddb2+YIMCbacP2kK7GXDED61mOln+hA1Q8+wK56Mf+eywpYukVcBUwEkgGklKeAfhYuo8Cc//M6+9YHEndDcx4cd+Mu+9YHcv7P6xYr4/bt25QvX950vHDhQlq2bImHhwfTpk0DtN5OgwYNeP3112natCkhISE4OzszefJkPD09ad26NeHh4aa4nTp1wsPDg86dO3PlyhVAcz64ZcsWUznOzs656rVmzRpGjhwJaJvJLlq0yLSNksFgYODAgTRo0CBf5+jk5ISXl5dy06EodBKMCcw4PCODgQKQSFadXmUlrfIg6Q5s9oMDi5ESbl104sLPVXUDpWGoUIFqixZRc8UKZaBywNJzUqWklEeEyLATUr725hFCBAOxaFspGQvyVfQnw3L++C0njEmp7P7qH3Z/lfvX2yNWdMoxLCEhAS8vLxITEwkLC2Ov/hHerl27CAoK4siRI0gp6dWrF/v376dWrVoEBQWxdu1aWrduDUB8fDytW7dmzpw5jB8/nlWrVjFlyhRGjRqFn58ffn5+fPnll4wePZoffvjhns/TnNxci+SHmzdvEhQUZPJrpVAUBldjr/K2/9umjWIzcz3eci+XFuN2mLZAIiyApFgDYX+V406EA5A+9+TSuzdu74/H1uxlVpEVS/ekooQQ9dDvhBDiRSDsHtJ3lFJ6PWjbdqSRNtwXGBjIzp07ef3115FSsmvXLnbt2oW3tzfNmzcnMDCQoKAgQNtgNs1AAdjb25vmj1q0aEFwcDAAhw8f5uWXXwbgtdde4+DBgxbV/fTp03h5eVGvXj2++eabXOMeOHAAT09PqlevTrdu3ahSpZh/MKl4YDl87TD9dvQj8EZgjnGK3Qe71wJgVSdkaABRZ525uNNNN1AadjVqUPOL1VSbN1cZqHxg6Z7UCGAl0FAIEQpcAl61cBkPBD4+PkRFRREZGYmUkokTJzJ06NAMcYKDg00bzaZhZ2dHWk80zZ1Gbpi7+khNTTW5AMkPaa5FOnbsSLNmzQgICGDkyJEkJOTgv0YnbU7q0qVLtG7dmj59+uDl5ZXvchWKvJBSsvbvtSw5vsT0DZRAYGtjS3Jq+ndEjgZHxjQfYy01s3J2G3z/JgnhyYQdceXuLbv0MBsbKgwYgOvIEdiUKmU9HR8wLL1330WgixCiNGAjpYy9l+TALiGEBD6XUq40DxRCvIm+nL1Wrdx3M85tSA7S56SMSel+nGztbej4SsMCL55IIzAwkJSUFCpWrEi3bt2YOnUqr7zyCs7OzoSGhmJnZ5d3Jma0adOGTZs28dprr7F+/Xrat9f8Srq7u3Ps2DH69OnDTz/9lMHJYV5MnDiRd999lx9//JEaNTQPn3kZKHPq1KnDhAkTmD9/Phs3bsw7gUKRD+4k32Ha79PYGbzTJHN1cuVD3w8JjQtl2fFlXI+/TpXSVRjTfAw96vaworY6UsKhpaTunEHk6TLcOO8CMn3aw6FxI6rOnIVT0yZWVPLBxNKr+8YAX6HNLa0SQjQHJkgpd+UjeTspZagQwg3YLYQIlFLuTwvUjdZK0HZBL4ieaYbI0qv70uakdH1Zu3YtBoOBrl27cvbsWXx8fABtccO6deswGAy5ZZeBjz76iDfeeIOFCxfi6urKV199BcCQIUN49tln8fT05KmnnsrSM8uN7t27ExkZydNPP01KSgrlypWjadOmGXxC9ejRw2RQfXx8GDFiRIY8hg0bxqJFiwgODsbd3T3fZSsU2RFyO4Qx/mMIuhlkknm7ebP4icW4lnLFy82reBglc4x3YdtY4nZ+x/WjriTHpzerwsEB19GjqODnh7BVm+/cD5Z21XFSSukphOiG9mHvFOC/Usp7mp0XQkwH4qSUi7ILV646Hl7UfS65HAo9xPj947mdlL40u2+Dvrzf8n3sDPc28lBkxEdjXNOfiG3niAnOOIRXqnVrqs6Yjn3t2lZSLisPvasO0h0cdge+llL+LTIt9cs2kdnwoP5/V2BmHskUCkUJQErJF2e+YPnx5aYtjuxs7JjSegrP13/eytrljIwI5PaMFwk/mETK3XQDZVO2LJUnTMDlud7ko/lT5IGljdQxIcQuoA4wUQhRBkjNIw1AZWCrfkNtgQ1Syp25J1EoFA86d5LvMOXQFHZf3m2SuZVyY6nvUpq5NrOiZrmTfHgzYVMmER9qC6QP25d9+mkqT56EbaVivMHtA4aljdQgwAu4KKW8I4SoCLyRVyJ9wYVnXvEUCkXJ4fLty4zdN5Z/b/1rkjV3a85i38XFdhdzmZLCzbmjiNi0B2lMbz5tK7pQZfZcynTsaEXtSiaWXt2XChw3O44Goi1ZhkKhePDZf3U/E/ZPIDY5fQHwyw1f5t2W72JnUzznnxLP/kPYmEEkXrmF+Sem5Z97CtfJszE453/RkiL/qOUmCoWiyEiVqaw+vZqPT3xsmn+yt7HnA58PePaRZ62sXfak3r1L1EdLif5yTYbJC4eKBqrMX0qpdl2sptvDgDJSCoWiSIhPjmfywcnsuZLu7K9K6Sos9V1Kk0rF8/uh+CNHuD5lEklX0venFDaSir41qLTwO0RpFytq93Bg8S23hRAGIUQ1IUSttJ+lyyjOzJkzhyZNmuDh4YGXlxd//vlnjnGnT5/OokXZrrInKCiInj17Uq9ePVq0aEHHjh3Zv39/tnEtSWZXHvdKeHg4L7/8MnXr1qVFixb4+PiwdetWIKPLEHN8fX1p0KABnp6etGzZ0rSTvKLkcCnmEi/veDmDgWpZpSXf9PymWBqolNu3CftgGlde98tgoJwq3aXO5Gdw/XiXMlBFhKU/5h0FTAPCSe8YS8DDkuVYgrMH9nFg09fERkdRpmIl2vd7nUbtCzbpefjwYbZv387x48dxcHAgKirqnrYpSiMxMZEePXqwaNEik1uNM2fOcPTo0ULfzNXclce9IqWkd+/e+Pn5sWHDBgAuX77MTz/9lGfa9evX89hjj/HVV1/x3nvvsXv37jzTKB4M/EP8mXhgInHJcSbZa41fY1yLcdjaFL/BnNu7dhE+azbGyEiTzMYuFTevO5QbtwDh3d+K2j18WLonNQZoIKVsIqVspv+KpYHatfJjYqMiQUpioyLZtfJjzh7YV6B8w8LCqFSpEg66u+dKlSpRrVo13N3diYqKAuDo0aP4+vqa0pw8eRIfHx/q16/PqlWay4H169fj4+OTwVg0bdqUAQMGAHDkyBF8fHzw9vamTZs2nDt3DsjohgOgZ8+e+Pv7k5KSwoABA2jatCnNmjVjyZIlACxfvpzGjRvj4eFBv379suSxbds2WrVqhbe3N126dDG5DZk+fToDBw7E19eXunXrsnz5cgD27t2Lvb09w4YNM+lQu3ZtRo0ale9r6OPjo1x/lBBSZSqfBXzGqL2jTAbKweDA3PZzGd9yfLEzUMnh4YSMHEno6DEZDJRz9QTqPm+k/JzNykBZAUvXkhAgxsJ53jOL+2YdUsoLY9Jd/vfxYv738eJc473zzfYcw7p27crMmTN59NFH6dKlC3379uWJJ57INb9Tp07xxx9/EB8fj7e3Nz169MjThUbDhg05cOAAtra2/Prrr0yaNInvvvsux/gBAQGEhoZy5swZAJPn3Xnz5nHp0iUcHBxMMnPatWvHH3/8gRCC1atXs2DBAhYv1q5PYGAg+/btIzY2lgYNGjB8+PACu/4AzRFj7969C5SHwvrEJsUy6eAk/EP8TbJqpauxtONSGlUsXjuGyNRUbn37LRGLFpMal97bMzimUKVFDGW83RGvfAvli8/OEQ8TljZSFwF/IcQO4G6aUEr5oYXLKZY4Oztz7NgxDhw4wL59++jbt2+e8zvPPvssTk5OODk50bFjR44cOZIlznPPPUdQUBCPPvoo33//PTExMfj5+REUFIQQIs9NZevWrcvFixcZNWoUPXr0oGvXrgB4eHjwyiuv0Lt372wNw9WrV+nbty9hYWEkJSVRp04dU1iPHj1wcHDAwcEBNzc3Uy/LnBEjRnDw4EHs7e3566+/ctXxlVdeISkpibi4ODUn9YBzMeYiY/aOIfh2sEnWqmorFnZYSHnH4uWa4u7Fi4RN/YCEY8cyyMvVjcfN6zaGxl3gxS/BsayVNFRYerjvCrAbsAfKmP0eGgwGA76+vsyYMYOPP/6Y7777LoM7jcTEjI7bMm+bIoQwudBIY+vWraxZs4YbN24AMHXqVDp27MiZM2fYtm2bKU/zcszLKl++PCdPnsTX15cVK1YwePBgAHbs2MGIESM4fvw4LVu2zOIWZNSoUYwcOZLTp0/z+eefZ9A9bUgz7ZyNRmMWvT/55BP27NlDpNnQSU6sX7+eixcv4ufnd0/Dg4rixZ4re3h5x8sZDNSAJgNY0WVFsTJQMimJyE8/5dKzvTMYKPsyRmp1iqLq4zEY2g+F/puUgbIylv6Yd4Yl87tfchuSg/Q5KWOSqbOHrb0DXd8cWaDFE+fOncPGxob69esD2jBb7dq1SUhI4NixYzz99NNZhuV+/PFHJk6cSHx8PP7+/sybN4/y5cszd+5cfvrpJ9O81J07d0xpYmJiTC7f16xZY5K7u7vz6aefkpqaSmhoqKlXFhUVhb29PS+88AINGjTg1VdfJTU1lZCQEDp27Ei7du3YtGkTcWZDHZnLWbt2bZ7n36lTJyZNmsRnn33G8OHDs+idF0IIZs2aRb169QgMDKRhw4b5TquwLqkylU8DPuXzU5+bZI4GR2a2ncnTdZ62omZZSQgIIGzqVO4Gpe90gZBUbBRHpSax2NgaoPtiaDnYekoqTFjESAkhlkopxwohtmHuH1lHSnl/y8UKiTRDZOnVfXFxcYwaNYpbt25ha2vLI488wsqVKzl79iyDBg1i6tSpGRZNgDbk1rFjR6Kiopg6dSrVqlUDYPv27YwbN46xY8dSuXJlypQpw5QpUwAYP348fn5+zJ49mx490t0WtG3bljp16tC4cWMaNWpkmh8KDQ3ljTfeMPWy5s6dS0pKCq+++ioxMTFIKRk9ejTlypXLoNv06dN56aWXKF++PJ06deLSpUu5nr8Qgh9++IG3336bBQsW4OrqSunSpZk/f74pzp49e0y+qwA2b96cIQ8nJyfeeecdFi5cyBdffJGfy66wMreTbjPxwET2X03/RKK6c3WWdVxGgwoNrKhZRlLi4olcupSb69dr/p90HCskUfXxWziWM4KDC/RZA/Vy90mnKDos4qpDCNFCSnlMCJHtKgEp5W8FLsQM5arj4UXd5+LFhVsXGLNvDJdvXzbJfKr6sKDDAso5lsslZdES6+/P9RkzMYaFmWTC3oBbkxuUrx+PsAHKu8PL34Jr8TGsluahddUhpTym/7WoMVIoFMWXXy//yuSDk7ljTB/SHdh0IKO9R2Owyb9Dz8LEGB1N+Jz/cPt//8sgL13HiSpNgrF3TtEEtdpA33VQuqIVtFTkRvH6UEGhUBR7UlJT+CTgE1adXmWSOdk6MbPtTJ5yf8qKmqUjpSRm6w9EzJ9PSkz6VzEGl7JUfuwOZStdwLRmyesV6LkEbB2yz0xhVUqUkZJSKidjJRhLepFW3B8xd2N4/8D7HAo9ZJLVLFOTpR2X8mj5R62oWTpJV64QNm0adw7/kUHu0ulx3Fz3YSvSjJaALtOh7RhQ7UaxxdLbIjWTUp62ZJ75xdHRkejoaCpWrKgMVQlESkl0dDSOjo7WVuWhJehmEGP2jSEkNsQka1u9LfPbz8fFwfr72EmjkRtr1xL50cdIs88l7KpXp8orPjiHfAJSH96zKwXPr4RGz1hJW0V+sXRP6lMhhAOwBlgvpSyy3Sdq1KjB1atX8/VNjuLBxNHRMcPKQEXRsTN4Jx8c+oAEY4JJNqTZEEZ4jSgW808Jf/+tLSv/52y60MaGCq+/hmvDKGwClqfLy1SDlzdBVeVn9UHA0t9JtRdC1AcGormSPwJ8JaUs9N1C7ezsMuyIoFAoCk5KagrLTizjqzNfmWROtk7MaTeHJ2s/aUXNNFITEoj8+GNurFkLKSkmuUPDhlSdOgGnwEUQsCs9QVUv7QPdslWtoK3ifrD4nJSUMkgIMQU4CiwHvIU2/jZJSvl9bmmFEAY9XaiU8t434FMoFBbjVuItxu8fz+GwwyZZ7bK1Weq7lEfKP2JFzTTiDx8m7INpJIekDz8KBwcqjRxBxd6dEJtfhYh/0hM0fhZ6rwD7UlbQVnG/WHpOygN4A+iBtj3SM1LK40KIasBhIFcjhbaL+llA7UOiUFiRczfOMWbfGELj0nek71CjA3Pbz6WsvfUez5ht24hYvBjj9ax7RZZq1YqqM2dgL67DV13hTlR6YPt3oeNksLG4Cz1FIWPpntRHwGq0XpNp8FpKeU3vXeWIEKIGmnGbA4yzsF4KhSKf/O/i/5j2+zQSU9IXHwz1GMpbXm9hI6zXyN/6aRvXJ09GZt5Q2dGRqlOn4PL884jTm+HHEZCi+3Ez2EOvj8CzX9ErrLAIljZSW6WU/zUXCCHGSCmXZZZnw1JgPDlsSCuEeBN4E6BWrYfK2a9CUSQYU40sPbaUtf+k79NY2q40c9rNoXOtzlbUDBLPnuX6lClZDRRg6+JCueeeg33/gf0L0gNKVYR+G6BW6yLUVGFpLP1a9Ho2sgF5JRJC9AQi0nauyA4p5Uop5WNSysdcXV0LoKJCocjMzcSbDNs9LIOBci/rzoYeG6xqoFJiYrg+cxaXXngRmYOXa2NEBGx5I6OBcm0IQ/YqA1UCsNQGs/2Bl4E6QghzX+FlgBv5yKIt0EsI0R1wBMoKIdZJKV+1hH4KhSJn/on+h7f3vc21+GsmmW9NX+a2m4uzvbNVdJKpqdz67jsiP1xCys2buca1dRbwzw/pgnqd4aWvwNH6324pCo6lhvt+B8KASoC5a9tY4FReiaWUE4GJAEIIX+BdZaAUisJn24VtzDg8g7sp6W5r3vJ6i6EeQ602/5Rw6hTXZ80m8XTGfQHs69cn+coV5N10XYUtuDWJTo/0+FDo9h8wlKjNdB5qLLXB7GXgMuBjifwUCkXhkpyazIdHP2Td2XUmmbOdM/Paz+OJmtk6Myh0jNHRRHz4ITHfZVwEbFetGm4TJ1CmSxdufzqFiK++xxgnsS2VgptHLC7uCSAM8PR8eHyIVXRXFB6WGu47KKVsJ4SIJaM/KQFIKWW+16xKKf0Bf0vopVAoshKdEM17+9/jr+t/mWR1XeqyrOMy3F3ci1wfaTRyc+MmIpcvJzU21iQX9vZUHDKEioMHYePkBKe+xeXWF7j0TMiYgcER+m+AR6y7uENROFiqJ9VO//tQuYpXKB40/o76m7H+Y7kef90k61yrM3PazaG0Xeki1+fOX39xfdZs7p4/n0Hu3LkzlSe8j33NmunC3R9AciYDBeBUThmoEoylP+btIqX8NZPMT0qZt+9xhUJRqPz474/MPDyTpFRtlZxAMMp7FIOaDSry+afk8HAiFizk9o4dGeT2tWtTefIknDt0SBfejYUDiyE2jGyJy/phr6LkYOnZxQ+EEC8A7wLOaB/23gWUkVIorERyajIL/1rIxsCNJlkZuzLM6zCPDjU65JLS8sikJG58/TWRn36GvJPuLFE4OVFp+HAqDPDDxt5eE6amQsB62DMT4iNyztRFbTpckrG0kXoCeAcI0I8/kFJuzCW+QqEoRKISonjH/x2ORxw3yR4p9wjLOi6jVtmi/Sg+7uAhwmfPJik4OIO8bPfuuI1/D7sqVdKFl3+HnRMg7GTGTIQAc79idk7Q+YPCU1phdSxtpMoDjwMXgBpAbSGEkMpbnUJR5JyOPM1Y/7FE3EnvhTxZ+0lmt51NKbui22Q16WooEfPnEbs7w0wADvUfofKUqZRu9Xi68NYVbe7p760ZMylTTXNQCLB3FsRc1XpQnT8Ajz6Fqr/CuljaSP0BzJNSfimEcALmA4eANhYuR6FQ5MLWoK3M+mMWyanaNkICwZjmYxjYdGCROQVNTUwkevUXRK9aleHbJhtnZ1xHj6J8//4IOztNeDcODi2F3z8CY/qegdg6QpvR0G4s2OsLOzz7Fon+iuKBpY1UFynlFQB9g9nRQoiiHfRWKB5iklOSmf/XfL45941JVta+LAs6LKBt9bZFooOUkri9ewn/z1ySQ0MzhLk8/zxu497GtlIlTZCaCqe+gT0zsi6MaPoCdJkB5WqigPN/XufwjxeIu3EX5woO+Dxbj0dbVck74QOOpY1UiBDiVaCulHKmEDMCdAEAACAASURBVKIWkJhXIoVCUXCiEqIY5z+OExEnTLL65euzzHcZNcsWTUN/99Ilwv8zl/gDBzLIHRs3psoHU3Hy8koXhhzR5p1CM23ZWdVL+zBX7btn4vyf19m3PhBjUioAcTfusm99IECJN1QWdx8PpAKdgJlo2yJ9B7S0cDkKhcKMk5EnGbdvHBEJ6fNPT7k/xYw2M4pk/ik1Pp6oFSuIXrMWzHYqN7i44DpuHOVefAFh0N3Mx1yF3dPgzJaMmThXhs7TwLO/8vtkhpSSg1uCTAYqDWNSKod/vKCM1D3SSkrZXAhxAkBKeVMIYW/hMhQKhRlbzm9hzp9zMKYaAbARNrzd/G38mvgV+vyTlJLb//sfEQsWYgw3+15JCMr164vr6NHYli+vyZLuwKFl2s9o9lGuwQHajIR2b4OD2g8gDZkqCT4TzfGdwSTEZnVRAlqPqqRjaSOVrLuAlwBCCFe0npVCobAwSSlJzD0yly3n03skLg4uLOywEJ9qhb+NZuK584TPns2dv/7KIHfy9qbylMk4NWmiCaSE01vg12lwO+McFY2fhSdnQnn3Qtf3QSE1JZWgoxEc/+UyN67F5xrXuYJDEWllPSxtpJYDWwE3IcQc4EUgV4+8CoXi3om4E8Hb/m9zKjLdyUDDCg1Z4ruEGmUK9+PWlNu3ifzoY25u2AApKSa5oVIlKr/3LmV79UrvwV09Bjvfh6sZDRlVmsFT88C9XaHq+iBhTErh7O9hnNh9hdjoTFP5Qv9EzOyV39beBp9n6xWtklbAokZKSrleCHEM6Iy2uWxvKeVZS5ahUDzsnIg4wTj/cUQlRJlk3et0Z3qb6TjZOhVauTI1lZitPxCxeDEpN8zcxBkMVHjtNSqNHIHBWfc/dfsa/DoDTm3KmElpV+3bJq9XwMZQaLo+SNxNMHLmt6uc3HuVhNsZHTvaOhho0r4aXp1rceTHnzm5+1tSjbexsS1Loyf7lPj5KLDcLugVzA4jgI3mYVLK/Dg+VCgUuSCl5Ntz3zLvyDyMUpt/MggD41qM47XGrxXq/FPC6TNcnz2LxJMZ3cOVat2aKpMn4VC/viZIToDfP4aDH0Jy+rZHGOyh9XBo/y445tspQonmzu0kTu4N4Yz/VZISUzKEOZS2xaNjTTx8a+DobMfZA/s4vee/pBq1OahU421O7/kvVeu50Kh9R2uoX2RYqif1//bOPDyq6mzgvzNLkkky2RdCNkjCvoeERUUEZFGrKGprN1u70H7Vr6LWDbH6VbFarUpbraVaq9XWtiqCCwoKgoJAEkD2kIUEkpB932c53x93kswkEyAwIdv5Pc88M/fcs7335t4355z3vG8G2jqUu6dEAgkeakehGJK02FpYvWs167I7PDEEewfzzNxnmBE14wwlLwxrVRVlzz1P9X//6+KOyBAVReT992NevEhTjlLC4Xc1q72aU66VjP0GLHoMQtRrAKC2vIl9m09ydOdpbBbXJXv/YG+mXhnH+MuGY/TuGGlue/NVrK2uRhLW1ha+eOt1paTOBSnlSE/Uo1AoulLcUMxdW+/iUMWh9rRxIeNYM28NUf5RvdKmtNmo+ve/KVvzB+w1Ne3pwmgk5Mc/Imz5cnS+DtP2on3w8YNw8ivXSiInalFyE/omiGJ/o6Konr2f5JOVVoq0u3qKC4r0ZdqiOMbMHIbe0GF+X5qXy571b9NQ5X4yqq6i3G36YMLjMZaFEMuAy9BGUF9IKd/zdBsKxVAhvTide7bdQ2Vzx0vqusTreHjWw/gYfHqlzca9e7UYT0ddl5P9584lcuWDeMXHawl1JZqH8v1v4hLr1DcU5q+C5B+odSegOLeGjI/zyTvQVaGEx5lJXhxPwrRwdDptIkpKScGRg+xZ/zZ5X+/tUsYZc2hYr/S5P+HpeFIvAkl0rEn9XAixUEp5+1nK+QDbAW9Hn96WUj7iyb4pFAMJKSX/OvYvnk572mX96d7Ue/nO2O/0yvqTpbSUst//npr1G1zSjbGxRK58EPM8x7SSpRl2vQBfPAut9R0ZdQaY+XO4/F4tEOEQRkrJqSOVZHycT1FWdZfz0WOCmL54BDHjgtvvpbTbyU7fxZ71b1OcfbxLGSEEzr66DV7ezLnl1t4Top/g6ZHUfGBcm9dzIcRrwOFzKNcCzJdS1gshjMCXQoiNUspdHu6fQtHvabY289iux9iQ06EsQnxCeGbuM6QO87zzFmmxUPnGm5T/6U/YGzr25QgfH8J+tpyQH/0Inbe3tu50dANsehiq810rGX0VLHocwpI83r+BhN0uyd1Xxt5P8ik7Wdfl/MgpYSQviWfYyMD2NKvFwtEvt5K24V2qigpc8guhY9SsS5lx3Y1UFp7ii7dep66iHHNoGHNuuXXQr0eB55VUNhAHtP0FxzrSzohDqbX9S2Z0fFR4D8WQ43T9aVZ8voIjFUfa0yaGTuS5ec8xzM/z5sYNO3dSvPoJWnNyXNLNixcTef99GIcPd3TsgLbulP+lawXh42DJE5A43+N9G0jYLHYy9xSzb9NJqksaXc4JnWD0jEimLYojdLh/e3prUyMHPv2YjA/fo77TmpPeaGTC3AWkXLuM4GHaPYhMSBoSSqkznlZSZuCoEGKP4zgVSBdCbACQUl7XXUGHp4oMtOnCF6SUuzudXw4sB4iLu7jB2hSKi0FacRr3fH4PVS1V7WnXJ13Pqlmr8NZ71rOApaiIkqd+R90nn7ikeyUkMGzVQ/hd4oiuU1+qxW/a+w9c/m80BcO8h2D6baD3+NL2gKG12cqRL4vY/+kpGqpdre/0Rh3jLx3O1IWxBIR27F9rrKlm78b32b/pA1oaXD1KeJl8mbroapKvXopfUPBFkaG/4/Hw8edbUEppA6YKIYKAdUKIiVLKQ07n1wJrAVJSUtQoSzFokFLy5tE3eSb9GWxS2y9jEAbun3E/3xrzLY+uP9lbWqh89VXKX/oLsrnDq4HOz4+w228n5HvfRXh5gbUFdr8E256GVqdpK50BUn8Kc+8D3xA3LQwNmustHNh6igOfF9DSYHU552UyMGluNJPnx+Ib0OG6tKa0mLT313F462asFtdNu35BwSRfvZQpC6/C29fvosgwUPC0x4ltAEKIAOe6e7KZV0pZLYTYCiwBDp0tv0IxkGmyNvGbr37DB7kftKeF+oTy7BXPkhyZ7NG26rZu1WI8nXLdxxS49DrC77kHY0SEY93pA9i0CqpOuFaQtFAzKQ8f7dF+DSTqq5rZ/+kpDn9ZhLXFdQOuKcCLqQtimXh5NF6mjldraV4uaRveIfOrL5B2131RQcOiSL32RsZfPh+Dl/LF7Q5PW/ctRwvR0YzmWFZwDpt5HY5oLQ4FZQIWokX1VSgGLYX1hdy19S6OVnaYek8Om8yzVzxLpF+kx9ppzc+n5InfUr9tm0u699ixDHt4Fb7Tp2sJJYe1dacTrvkIG60pp1ELPdangUZ1SSN7N+WTuasYu811IicgzIdpi+IZO3sYBqNmci+lpODoIdLWv82J/Rld6otMSCL1upsYNXM2OmWmf0Y8Pd13LzBRStnTHWZRwGuOdSkd8B8p5QdnKaNQDFh2nd7Fvdvupbqlwzz5xlE3snLmSrz0nvmP2t7YSPnatVS+8jekU4wnXWAg4Xf+kuBvfUuL8dRQDltXQ8bfXT2Y+gTCFSsh9cegN3qkTwONspN1ZHycR86+si6mXKHRfiQvjidpegQ6vbYBV9rtZGfsJm3925zOyuxSX9zEKcxYejNxk6b0ehiVwYKnlVQO0HjWXJ2QUh4Apnm4LwpFv0NKyetHXufZjGexOxSCQWdg5cyV3Dz6Zo+1UffJJkqeegrraaeQ7EIQdPPNhN+1QovxZG2Fr16Cz5+ClhqnfHpI+RHMWzkk152klBQdrybjk3xOHem6UhGVGEjyknjiJ4a2Kxqb1cLRL7eRtv5tKjuZkSMEo2dcQurSmxiWOOpiiDCo8LSSehDYKYTYjbb3CQAp5S893I5CMeBosjbxyM5H2HhiY3tamCmM5654jqkRU89Q8txpyc6mePVqGr9y3WLoM2Uyw1Y9jGnSRG3dKfNj2PQQVHTaIZI4X5vaixjnkf4MJKRdcuJAOXs/yafkRG2X83ETQpm+JJ7hozo2Krc2NXLgs0/I+Gg99Z1cFOkNBsbPXUDKN5YRMjy61/s/WPG0kvoLsAU4iAp2qFC0U1BXwIqtK8is6pgCmhI+hWeveJYI34gLrt9WX0/5n16g8o03wNphbaYPCSHinnsIvOF6hE4HpcfgkwchZ4trBSGJmnIavVgLXDSEsNnsZKeVsHfTyS5BBoWAxOkRJC+OJzy2I2pwY20N+zZuYP8nH9LcUO9SxstkYspCzYzcP3jojUQ9jaeVlFFKebeH61QoBjQ7i3Zy3/b7qHGaUvvm6G/ywIwHMF7gWo+UktoNGyh5+hls5U7/yev1BH/3O4TfcQf6gABorITPfwtpr4B0skrzDtTMyWcsB8PQsi5rDzK46SR1la5BBnUGwdjZUUxbGEdQhG97ek1pCekfrOPQ1s1dvJL7Bga1m5H7+Pmj8AyeVlIbHRZ+7+M63afiSSmGHFJKXj38Kmv2rmlffzLqjDw08yFuHH3jBdfffOQIxY+vpmmvqxNS39RUIletwmfMaLBZYNdLmoJqdvIhJ3SaA9j5q8Bv8Dspdaal0cLBbYUc2HKKpjqLyzmjt54Jl0czdUEsfkEdG6jL8k+QtuEdju3c3tWMPDKKlGuXMWHuAmVG3gt4Wkl92/H9oFOaiielGHI0Whr59c5f80leh0eHCFMEz817jsnhky+oblt1NaVr1lD97/+A0wvTEBFBxP33EXD11dqCftan8MlKKO9kZTbyclj8Wxg28YL6MdBoqGnhwJZTHNpW2CXIoI+fkcnzY5h0RQw+ftroVkpJ4bHD7Fn/Nif2pXepL2JEIjOuv4lRMy9RZuS9iKc386q4Uoohz6naU9z5+Z1kVWW1pyVHJPP7K35PmOn8Ry3SZqP67Xcoe+45bNVOoyKjkdAf/oCwn/8cnZ8flB3XjCKyNrlWEDxScwI79pohte5UW97Evk2OIINWN0EGF8Yx/tKOIIPSbidnbxp71v+X08ePdakvbuJkUpfeTPykqcqM/CLg6c28RuB/gMsdSZ8Df5FSWrotpFAMIr4s/JL7tt9HnZMroVvG3MJ9qfdd0PpT0/79FD/2OM2HXYMK+F12GZErV+KdMBKaqmDjY5D2V7A7uerxMsPlv9LCtxs86wOwP1NR6AgymN41yGDwMF+mLYpn9IzI9iCDNquFYzu2k7bhHSoKTrpWJgSjZsxmxnU3MSxp6Hrc6As8Pd33ZzQP5i86jr/vSPuJh9tRKPoVUkpePvgyf9z3R6Rj16eXzotVs1Zxw6gbzrtea3k5pb9/lpp161zSjdHRRK58EP/58xF2G+z5K2x9Apqcl38FJH8f5j8M/hduQThQOJ1Tw96P88g7WNHlXES8meQl8SRMCUc4ggy2Njdx8LNNZHz4HnUVZS75dXoDE+bOJ+XaZYQMj7ko/Ve44mkllSqlnOJ0vEUI8bWH21Ao+hUNlgZWfbmKT09+2p4W6RvJ8/OeZ2LY+a37SKuVqn/+k7I//BF7fYeJs/D2JvSnPyX0Jz9G5+OjmZJ/vBLKXKPoEn8pLPktRE1hKCCl5OSRSvZ2E2QwZmwwyUviiRnTEWSwsbaGfR9/wP5PPqC53jX2k9HHxJSFVzH96qX4h4ReFBkU7vG0krIJIRKllDkAQogEwHaWMgrFgCW/Np87t9xJTk1HPKaUyBSemfsMoabze7k17N5DyeOP05KV5ZJuXnglEfc/gFdMNFTkwLsPwfGNroWD4rR1p3HXDYl1J7tdkrO3lL2f5FN+qr7L+YSp4SQviSdyREB7Wm1ZKekfrOPglk3uzcivuo4pC6/Gx1+ZkfcHesN331YhRC6ac9l44DYPt6FQ9Au2F2znge0PUGfp+C/8e+O+x90pd2PU9Xz9yVJcTOnvnqb2o49c0r1GjCDyoYfwn3MZNNfAJw/B7r+A3Wmp1+gHl98Ds24Ho895y9TfOb67mK/W51Bf2YK3rwGdXnQxI9fpBKNnRjJtUTwhUR1hL8pO5mlm5Du2dTEjD4wcRso3ljHhigUYvYbOut1AwNPWfZ8JIUYBYxxJmVLKljOVUSgGGnZpZ+2Btby4/8X29SdvvTePzH6EaxOv7Xl9ra1U/v01yl96CdnY4fpS+PoS/ov/IeTWWxEGPaT/DbashkZn9zsCpn4XFjwMZs9H7u1PHN9dzJY3jmGzaAqmpdE1jpPBqGP8ZcOZujAOc0iHoi44dpi09W+TuzetS53h8SOZsfQmRs+6DJ1emZH3Rzxt3Xc78KbDYSxCiGAhxI+llC+epahCMSCob61n5Zcr2Xpqa3talF8Uz897nvGh43te3xdfUPL4alrz813SA665hoj77sUYGQkntmshNEo6hVeLnQVXPQnDB7dv5pYmKyf2l/H5PzPbFZQLAlKuGsHkeTGYzNpmWmm3k7svjT3r36Eo80iXIrHjJzFj6U3ET0lWZuT9HE9P9/1USvlC24GUskoI8VM6rP0UigFLbk0uK7au4ERNRzDAmcNm8ru5vyPEp2c+2loLCij57ZPUf/aZS7r3qFFEPrwKvxkzoPIEvPVdONYpak1gLCz8P5iwbNCuO7U2W8k7UE5Weiknj1Rgt54hGLeEmddp/gJsVivHdmzr1ow8KWUWM5beRNSoMW4qUvRHPK2k9EIIIaWUAI74UMpPiGLAs/XkVh788kEaLB0OSG8dfyt3Tb8Lg+7cHyN7UxMVf32ZipdfRrZ2hBDXmc2E//KXBH/7FoS1ETY/ArteBJtTmHGjL1x2F1zyv2A0eUSu/oSl1Ub+wQqy00vIO1ThftTkBv8QbyzNzRzcuon0D9ZRV97VjHz85fNIuXYZodGxvdF1RS/iaSX1MfBvIcRfHMc/c6QpFAMSu7Tz0tcv8eev/9ye5qP34dFLHuWahGvOuR4pJXWffkrpb5/EUlTkci7wxmVE3H03huAg2P8mfPYYNJS6VjD5FrjyEQgYfkHy9DesFhsnD1eSnV7CiYMVXUKytxEeZyYwwsSJr8tdlJfe0EJIRDZr73iB5jrX8BpGHxOTr1zC9GuWYg4ZWv4JBxOeVlL3A8vRvE4AbAZe9nAbCsVFoa61jge/eJBtBR3h1KP9o3l+3vOMDRl7zvW05J6gZPVqGnbscEn3mTiRYQ+vwjRlCuTvhP/eD8UHXAvHpMKSJyEm5YJk6U/YrHZOHa0kO72U3K/LsDS7V0yh0X4kTY8kaXoEQZGaJ/Itf3+Przf/B7u1FoQXAhvHy1zLmwICSb7qOqYuukaZkQ8CPG3dZwdecnzOGSFELPA6EInmkHatlHKNJ/umUPSEnOocVmxdQV5tXnvarKhZPH350wT5BHVf0AlbfQPlf36Rytf/AU7h2/VBQYTfczdBN96IqDkF//kBHHnPtbB5uLbuNOnmQbHuZLPZKTxWRXZGKbn7y7pY5rURPMyXpOkRJKVEupiPAxzetoUDn76G3eq4lrLVJaJ7QHgkqdcuY8K8K5UZ+SDC0yOp88UK3COl3CuEMAMZQojNUsquZjkKRS/zWf5nrPxyJY3WDnPw2ybcxi+Tf3lO609SSmo/+JDS3/0Oa5nT+ohOR/AttxD+y/9FbzLA1sdh55/A5rRLw2CCS++ES38JXn5dKx9A2O2SouNVZGWUkru3jOYG9y48A8NNJKVEMColkpDhfi7WdpbmZvIO7CU7bRdHvtiqRRXuhE6vZ8ntdzNGmZEPSvqFkpJSngZOO37XCSGOAtGAUlKKi4bNbuPFr19k7YG17Wkmg4nfXPIbloxcck51NGdmUvLY4zSmu4Z2MCUnM+zhVfiMGQMH3oJP/w/qi10LT7xJGz0FDlwfcdIuOZ1TQ3Z6Cdn7ymiqbXWbzxzi066YwmL9XRRTY20NORm7yU7bxckD+7Fa3NfRht1uZ9ylcz0qh6L/0CtKSgjhK6VsPHtOt2VHANOA3Z3Sl6OtdxEXF3eBPVQoXKltreWB7Q/wReEX7Wkx/jE8P+95xoSc3VzZVlND2R//RNU//+kS40kfHkbkvfcScO21iFO74eX5ULTPtfDwabDkKYib6TF5LiZSSkpO1JKdXkr23lIaqt3v3/cL8nZM5UUQOSLARTFVFReRk7aL7PRdFGUeQ8pzs+wDMIcqo4jBjKc3816CZijhD8QJIaYAP5NS/uIcy/sD7wArpJQupjpSyrXAWoCUlJQzbJpQKHpGVlUWK7au4GRdx76aS4dfylOXP0Wgd+AZy0q7nZp336X0989iq6rqOGEwEPL97xN2+y/QW6vgnR/DoXdcC/sPgysfhcnfAp3OcwJdBKSUlJ2s0xRTRmmX8OttmAK8SErWFFNUQmC753EpJcXZx8lO30122ldd9zQ5ERoTR1LqLPQGI3vWv+3ib8/g5c2cW271rHCKfoWnR1LPAYuBDQBSyq+FEJefuYiGIxbVO2geK971cL8UCrdsytvEqh2raLI2taf9ZNJPuGPqHejPEm216eBBLcbTAVeLPL9LZhP50EN4xw6DHWtgxx/AqX703tpep8vuAu+BY30mpaSisJ4sh2KqLWtym8/H30jitHCSUiIZPioInUMx2awWTh08SHb6bnLSd1Ff2TWUBgBCED1mHEkps0hMnUXwsA6z+6DIYXzx1uvUVZRjDg1jzi23Mm7OPI/Lqug/eHy6T0p5qpObkbN6QRdagVeAo1LKZz3dJ4WiMza7jT/u+yOvHHqlPc1kMPH4pY+zaMSiM5a1VlZS9txzVL/9jstCviEqisgHHsB85QLEobfhj49CneueKCbcAFf+HwTHe1KcXqWyqIGsjBKy00upLnE/i+/tayBhWjhJ0yOIGROMTq+NDFsaGzmxP52c9N3k7k2jtcl9eYPRi7jJU0lKnUVi8gx8A91bUI6bM08ppSGGp5XUKceUn3SMjO4Ejp6lDMClaAESDwoh9jvSVkopPzpDGYXivKhpqeH+7fezo6hj31KsOZY189YwKnhUt+Wk1UrVv/9N2Zo/YK/tmI0WXl6E/PhHhC1fjq7iMPxtERS6Gk4QNUXb7xR/icfl6Q2qSxrJzighK72UyqIGt3m8fPSMnKoppthxIe0RbusrKzoMHw4dwG5zb27u428mITmVpNRZjJicjNFn8HpvV5w/nlZSPwfWoFnmFQKbgNvPVkhK+SVaaA+FolfJrMxkxdYVFNQXtKddFn0ZT8558ozrT43p6RQ/9jgtmZku6f7z5hH54AN4BRlg451w4N+uBf0iYMGvYep34CzTh31NbXkT2RmlZKWXuI3NBGDw1jNychhJ0yOImxCCwahHSkll4SmyHYYPxdnHu20jIDySpJSZJKXOInrsBGUyrjgrnt7MWw5815N1KhSe4uMTH/Prnb92WX9aPnk5v5jyi27XnywlpZQ+8wy177/vkm6MiyNy5YOYL5kBO/8IO54Hi9NUlt4LZt8Oc+4Bb3OvyOMJ6iqbydlbSlZ6KaV5tW7zGIw64ieFkjQ9kvhJoRi99NjtNk4fzyQ7fRc56buoOl3ktixAxIhEbRovZSbh8SOV13FFj/C0dd9I4H+BEc51Symv82Q7CkVPsNqt/GHvH3j18Kvtab4GX5647AkWxC9wW0a2tlL5jzcof+EF7M4xnkwmwn72M0J++AN0WR/An1KhtsC18LhrYeFjEDKyV+S5UBpqWsjOKCU7vZTi3Bq3efQGHXETQkhKiWDEpDC8fAxYWls4eTCD7LRd5O7dQ2NN1zDtAEKnI3b8RBJTZpOUMpOA8IjeFEcxyPH0dN97aAYQ7wPnvtFBoeglqpuruXf7vew6vas9bUTACJ6f9zyJQYluy9Tv2EHJ6idozc11STdftYTI++7DaD8Nby6FU7tcC0ZOgiVPwMhzMmi9qDTWtpK7TxsxFWVXg5tNHDqdIHZ8CKNSIhgxJRxvk4Gm+jqy92wnO20XeV/vxdLi3tTc6O3DiKnJJKXOZuS0FEz+/Xf0qBhYeFpJNUsp/+DhOhWK8+JY5TFWbF1BYX1he9oVMVfwxJwnMHt1fYlaCgspefIp6jZvdkn3Skpk2KpV+I2Phy2PaZ7KnfENg/mrIPnWfrXu1NxgIXdfGVnpJRRmVrnzKITQCWLGBpM0PYKEqeH4+BmpLSvl8OcfkZ22i4Kjh7qEWm/DNzCIxOkzSEqdTdzEKRi8VFQehefxtJJaI4R4BM1gon3HnZRyr4fbUSjOyIe5H/LozkdptnX85/+LKb/gZ1N+hk64bpy1t7RQ8corVKz9K7K5I7/Oz4+w/72DkG/eiEj/C/xxGTjFk0JnhFk/h8vvBZ8zb/q9WLRFsc1KL6XgaCV2uzvNBNGjg0iaHknitHB8/I2U5Z9g70f/JTt9F2V5uV3LOAiOinasL80iatRodP1IKSsGJ55WUpPQTMnn0zHdJx3HCkWvY7VbeTbjWf5x5B/taf5Gf5647Anmxbnur5FSUr91KyW/fRLLqVMu5wKvv56Iu+/CULYT1l4C1Z08Ioy5GhY9DqHupwwvJucaxTYqKVBTTMnhmPwNFBw9zK53PiQ7fRe1ZaVuy2jlxpCYOouk1FkqaKDiouNpJXUzkCClPLNHSIWiF6hsruTebfeyp3hPe9rIwJGsmbeGkYGuRgyteXkUP/EEDdu/cEn3Hj+OYasexjdKBx/+EPJdY0ARPg6W/BYS+3ZD6blGsY0cGaD5y5segbcJ8r7ey/Z//Jvcfek019e5LaM3GIidOEXz+DB9Bv4hob0pikJxRjytpA4BQUD3/5YpFL3AkYojrNi6gtMNp9vT5sfOZ/Vlq/H36nA9ZG9spPylv1D56qtIpxhPusBAIu5aQdBVcxGfr4YNb+BiXWAKgfkPQfIPQd83wQOsFhsnD1WSlVFC3oFyrK3uFVN4opJrTwAAHUxJREFUnJmklAiSkiMwGFvIydjD5rWvkn9wPzaL+3AZ3r5+jJyWom2snTIdb1/f3hRFoThnPP20BQHHhBBpuK5JKRN0Ra/xfs77/N9X/0eLIy6TQHD71Nv56eSftq8/SSmp27iRkt89jbXYKUSGEAR985uE3/FzDJlvwQup0Oo0wtAZYMZymHsfmIIvpliAI4rtEU0xnfi6/AxRbP01xTQ9AmmrIjt9Nx/+YRdFx4+6jcEE4B8S2r6+FDt+InqDsTdFUSjOC08rqUc8XJ9C0S0Wu4Xfp/+eN492WNuZjWaevPxJLo/pMANvPn6cksdX07hnj0t505QpRD68CpP+BPxzMVTluTYwarG27hQ+ujfF6EJbFNusjFJOnC2KbUokidPCaG0qIjttG+89tfuMHsXDYuPbFVNkQpLaWKvo93ja48Q2T9anULjjw9wPeS7jOUoaS1zSEwMTWTN/DfEBmvNWW10d5X/6E5VvvAm2jhGIPjSUiF/9isCZCYhND0Ce67oUYWO0/U5JV/a6LG30NIptwtRQGqpyyEn/iLcf2019VaXb/ELoiB47nsSUmSSlzCJoWFRviqFQeByPKCkhxJdSysuEEHW4bhMUgJRSBniiHYXinePvsHr3aix215f4xNCJvLz4ZfyMflqMp/UbKH3mGWwVTuEg9HpCvvddwm77Nvq05+Cvt4FzcD2fIJi3ElJ+BPren/o65yi2oT4kTY8gfoKZ2vJj5KS9Tdq6jDN6FI+fkkxSykwSps/AN6B/mMcrFOeDp0ZSfgBSSrXNXOFxpJTsLd3Luqx1bMjZgHTjLqGiuQI/ox9Nhw9T8tjjNO3f73Led+ZMIh+4F5+qLfDqHGhx8lMn9JD6E7jiAfAN6XVZSk7UkpVeQk5GKQ017hVTWxTb4aMM1JYcITt9I7vfPti9R3FzAInJM0hMncmISdOUR3HFoMFTSkpFylV4nNLGUjbkbOC97PfIr80/Y976stOcfuRRqv/zH9cYT5GRRN5/H+YEEJu+D5U5rgUTF8DiJyBibG+IAHREsdWCBZZQX+k+vLpvgBcJyeFExFqoKTlMTvrb7H47q9t6AyMi29eXoseMVx7FFYMSTympCCHE3d2dVIEMFeeKxWZhe8F21mWv44vCL7DLM7uAFHbJlfsl39kO1U1OYTKMRkJvu42wZXPRbf8N7NnqWjA0SVNOoxZBLxgP9CSKbcK0MILDa6guPkT2V6+Rvu6027wAkQlJ2vpS6mzCYuOV4YNi0OMpJaUH/FExoRTnSU51Duuy1vF+7vtUNnc1AvA3+nPVyKsIN4Vz9K2/cNOWFsJqwaoDYyc95jdnDpF3/Q/eJ96A164E6WS27R2oTeul/gQMnvc1d65RbEdMDsIcWEZVcQZHP0+jqda9N3KdXk/M+EkkpcwkMWUmAWHKo7hiaOEpJXVaSvkbD9WlGCLUt9bzcd7HrMtax4HyA27zpA5L5YakG7gy/kpMBhPVGzZQ+KENncNuwllBGWNiiLz/Xvz9sxEblkKz04tf6GD6bTDvIfDzrAeFc41iGzveF5NfEVVFBzm8ZR/WFvfTfkYfEyOnTicpZSYjp6Xi4+/vNp9CMRTwlJK6oBGUEOJvwDeAUinlRM90SdEfkVKSUZLBuux1bMrb5OIAto0I3wiWJi7lhqQbiA2IRVqtWmTcTZupeustdG68cuvMZhKeuxPd5yuhotM6zsi5miujyAkek+Nco9hGj9Lh5X2SqsKDHNl65IwexZNSNP94sRMmK4/iCoUDTykp95Hjzp2/A38CXr/wrij6IyUNJbyf+z7rstZxsq7rZlODzsC82HksG7WM2VGzERYrDTt2UrT5z9Rv2YKtxv10WBv2ujp073QKCh2SAItWw5irPLLuVFfZ7AgWWEJpfnd+7wSRI1sxGvKpKDhI5hcnuq0veHgMSamzSEqZRVTSaIRO121ehWKo4hElJaV0v5Pw3MtvF0KM8ERfFP0Hi83CtoJtvJv1LjuKdrg1gkgKSmLZqGV8I+EbBFiN1G/bxumnf0X99u3IRvdrOu4w+DqZZnsHaOEzZv4MDN4XJMO5RLHV6SVh0bXodXmUnzxA7p4y95UJQdSoMZrj1pSZyqO4QnEO9I2nTMWgJrsqm3ez3+WDnA+oaqnqct7f6M/VI69m2ahljBbDqN+yhbo1D1Cy8ysXp6/OGCIjMS9ciGjIp2rDdqStY2Qk9HYiJtcBQgs8OP9h8A8/7/6fSxRbobMQHFGOkCcoP3WIk1+7n/LTGwzETZqqmYpPn4lf0MX3/6dQDGQGjJISQiwHlgPExcX1cW8UnalrrWPjiY28l/0eB8sPus0zY9gMbhh1A3ON47Fs/ZK6F58iOyMDulmn8RoxAvPChZivXIBPOIicz+Dz9/BJ1VF6wIy1UY/B10bE5DoCE+3wk+0QNfm8+n8uUWyhkYCQYqQth8qCYxRVdO9RPCE51eFRPBkvk/IorlCcLwNGSUkp1wJrAVJSUtTm4X6AlJL0knTWZa1jc/5mt0YQkb6RXJ90PdcapuG34xB1f32NgkOHuq3Te/w4AhYuxHz5bLzIQ2R/Cp+uhbqOvUOBIyBwRKd9RzbRYwV1LlFspb0Kv8AC7K25VJfk0lzl/k/PHBru2L80i5hxE9EbBsyjpVD0a9STpOgxxQ3F7Z4gTtWd6nLeoDMwP2YeNzOdkftKqH91I425L+B2hUkITMnJmK+8EvO0eLzqv4asD+CdVWB37wLILYEx55SttdnKia/Lyc5wH8VWSom0FWPyO4W1JZuGmmJaurHZCI8boUWsTZlFxMhEtbFWoegF+oWSEkL8C7gCCBNCFACPSClf6dteKZyx2CxsPbWVddnr2Fm0060RxJjAUXzfksLUoy20vvoF1qKNuLWoMRrxmzkT87w5mBMMGMp2Qfbv4L3C7jvgEwRJC8AnEPb/E6xOozajCRb8uvu+t9jIO6gppnw3UWyltGK3nsLb5yStjdm0Nta4hJRqQwgd0ePGOwwfZhEUOaz7/ioUCo/QL5SUlPLbfd0HhXuOVx1nXdY6Psz90K0RRLDw59amqVySrcewYx+2yn+4HTEJkwn/OXMwzxiHf2Qd+sJtkHM3ZLlf1wEgaiqMWqi5LoqeDjqHb7q42fDZb6CmQBtBLfg1TP6mS9GzRbGV9mbs1jwMhnwsjTnYLM1Y3Ng+GLy8GTFlGkmpsxk5LUV5FFcoLjJCdhO1sz+TkpIi09PT+7obg5ba1lo+PqF5gjhU0XX9yLtV8s3KJOblmjCnZyEb3HtZ0AUEYJ47B/OEcPzMBehOboWartOD7fgEQuJ8TSklLgBzZI/6fbYottJeh601B70uj9bGPGQ3fgFN5gASps8gKXU28ZOmYPRWHsUVgwMhRIaUMqWv+9ET+sVIStH32KWd9OJ01mVrRhBtodjb8G+UzD8VwOL8QMIPFkJrJtDVOlsfFob5shTMiV746Y8gCl+DPPfhKAAYNtlptJQC+rP/SR7fXcxX63Oor2zBP9ibpNQImuutXaLYSimR9grsrdkITmBp1owv3K10BUYOa/f4MHzMOHQ65VFcoegPKCU1xCluKGZ99nrey36PgvoCl3PBdZLZWToW5QUQlVWFsFcBXaf8jDHRmFNGYY5pxtSahqj9G3S3vOQdCIlXaEop6Uow92xd5/juYra8cax9Xam+qoX9mzpGZ1LakdYibJYcsOdis3TtbxuRCaMcHh9mEqo8iisU/RKlpIYgrbbWDiOIwp0uQQSHVUpmZkouz/Um9mQTYAMqutThnTgC88QIzOEleDdmIOxpUN5Ng5ETtdFS0kKInXHOUW+lXVJT1kR5QT0VhfWUF9STf6gcS9NRrM1fgr0OdGb03rPR6UzYLdnYrblIu/uwGDq9ntgJk9s9PphDw86pHwqFou9QSmoIkVmZyXvZ7/FB7gdUt1RriVISXwozM+3MyhLElLat03R90ZvGjsCc5IPZnI2X3KklunO04GV2HS0FDD9r31qbrO2KqLywngqHYups8GBtOYq1cTPtk3b2OmxNm7B1rVLrisnEiKkpJKXOYuTU6fj4KY/iCsVAQimpQU5tay0bczfybva7HKk4AoCQkjGFMCPTzsxMSUT7PqBOK0x6HX5jo/GPacXsexSjd5HbbABEjHcaLc3sNlaTtEtqK5qoKGigvKCufZRUW951I7CUEmQTdlsF0l6JtFVgazkI3aokDb/gEEf8JYdHceO5jdwUCkX/QympQYhd2kkrTmNd9jo+zf+UFlsLeptkSr5kxnFJ6nFJkHuDPISXEb/RIZjDKzAHnULvXeA+o5c/JFzhUExXut1M29pspbKoQVNEBdooqaKovqvVnZRgr8Nur0DaNGVkt1Ui7ZUguyqv7phx/c0kpcxiWOIo5VFcoRgkKCU1iDhdf5r1OZoRRGF9Id6tkiknJDMyJdOzJX7uY+yhM3nhn2DCHFKAf0Q9OmO++4zhYztGS3Gz20dLUkrqKpo6FJHju6a8yWXUJaUNaa9B2jRl1KGUKnFvc3fumMPCmfPtH1xQHQqFov+hlNQA5MPcD1mzdw3FDcVE+kUyL3YeJ2tPsrNoJ75NdpKzJbccl0zNlXh38+7X+3thjrNhDi/FN6IFtxbXRj9ImNsxWgqKw9Jqo7KogYpd5dr6UUEdFYUNtDY5m35bkLYqbUTkmKaTtkqkvRpwvzepO4zePoRExxIaHUNIdCyNtTV8vXkjNkuHWbvBy5s5t9zao3oVCsXAQCmpAcaHuR/y8dqVrNrSSmgtVAQUsH7WG/gKwcpMyYSTEkM3esAYaMQcVYd5eD2msFaEuxmxsNEwahEycQH1gSlUFLdqo6N9tZQX7KKmtLHdQ7i0NyPtldqakWOaTtorkfbaHstlMgc4lFFsh1KKicMcGtbFNHxYQhJfvPU6dRXlmEPDmHPLrYybM6/HbSoUiv6P8jjRj7HYLOTW5HK86jiZlZlkVmVi/PQrln9kcxkhSaC7HT5ewQJzVC3mmGZ8gi1dA9QafbHGz6MqdAnlhmmUV3lr03WF9bQ0WB3GCw3aGpGTAYPdVgny3IMStmEODSckOsZJGcUSEhOr3A0pFBcB5XFCcd5UNleSWZnpopBKinMYVmolpkISUy6ZXw6T8iT6Tv9XdNY7PqFWzNGNmGOa8Q5wnoaDBlsw5aZLqPCfS7k9ifJqX6p3NmO32ZD2LMe0nGPNyDFCgjN4jHCD0OkIioxymabTlFKMiq2kUCh6hFJSFxmr3UpeTR6ZVZoiOl6RyelTRzEVVhBTDjHlkmkVcG159xZ47pDAsOQazNFNGP3s2KSBKmsMJ5pGUG5LosIwhfLmKJqaJNJe7Zie2+WwoqtA2qo4m2l3ZwxGL4KHR3eZpguKilZm3wqFwiMoJdWL1LTUtI+KMiuOUZx3GGtOHsPKLERXSEaWS+aUg/+5W1kDUByRQk7CdbR4h+DdUkl8/ia8KKUlOpByywgqykdQaYnEZqtxsqLLcEzX1eB+o1P3ePv6EeIyItK+AyIilI87hULRqygl5QFsdhv5dfkcrzxOZsVRTmcdoDn7OH5F1cSUa1N1yyrA1LNZM9BLDAEgg32xB/pj9TOTb51ODuFYWt6FpjqahT+HYycg9BOQJRWO9aLjIN25gjgzfkHBhDgbLjiUkV9wiPJrp1Ao+gSlpHpIbWutpozKjnD6+D4ajh9Dl1fUPjq6ogK8zrLlRyKwGP1o9QqgxSuAVpMZu9kfq6+ZVh8/mgy+NOFFs/Si1S41X3SyCdnahM1SBPZ9tI+GZD22lt3nLoAQBIRFEBoT22Vk5OOvXAYpFIr+hVJS3WCXdk7VnSKz+BCFR/ZQm3kEeeIkAcV1xJRLJldCcidTb5vOixavABp9AzQF5B1Ak5cfzd4mWozetBgNWAw6LAKkbNYUj2xyKKFapCyBxm523PYQnV5P0LDhhMZ0KKKQ6FhChker+EgKhWLAMKSU1Oev/Abj2v8QVGOjOlCPZfk3ueLHv6bB0sDxwgOcPPQVVZkHsObkYSqoIKrMyvBqQYTRTKtXAM1e0TT6mGnwN3EoxESr0UiLQY9FL7DqJHZanRRPI8hiXIwRbPTUNqFHXHbLre1rR0GRUegNQ+r2KoYA7+0r5OlPMimqbmJ4kIl7F4/h+mnRfd2ti8JQlb3f7JMSQiwB1gB64GUp5ZPd5T2ffVKfv/IbSv57iMIggZQNCPyIrPfGy16FFL7o7CZavByjHb0Oiw5sOhs2LO2KB+mZUc6ZEXj7+uNjDsA3IBC/4EB8zYGYAgIxmQPY8Z9/YWnuut5kMofwi5dfvwj963s6P6y/WjSapVOjkTgCHTrySQkS2bH52HHc8bsjf/tj4FSm7bwjuaO8xKVM5/zOj5Q8S304neuSv1N/3dVHFxmc5G3L73S+/eo4ySC7lHHk6ny9urtmLvK5v8Zur3+n+txf/45rvO9kNev3F2KxdVxgo15wzaQoJkYHYpcSu6Neu5TI7o7Rvu1Stl+fbo8d/bPbO47b8p3p2+503V2O289raTgda3nb2m/Lqx1XN7ZSUtviYvJkMur57bJJPVJUA3GfVL9QUkIIPXAcWAgUAGnAt6WUR9zlPx8l9cY3vkWJXyM9tWy7UKQwYjeasBl9sRp9sXj7YvH2w+KtHVuNvliMvli8TFgMvlgNPkghOl5CnR5a77x9TM3fjF52LHzZhIG9cQtpiJ3S6QXX8YIA9y9M15dD1xeQc310KtO1PkdF0KWNtoe/PYebPnR5Abt54dnsff/3qlD0F6KDTOx4YP455x+ISqq/zAfNALKllLkAQoi3gKWAWyV1PpT64/yv33khASm8sepMNOtNNOtMNOp9qNf7UKv3oV5voknvQ5Pju1nng1XXzX4hq+PjErapqXNCV8QIykLncknVbsy2eur0/uwMnkmWbgQU1py5rEKhGFQUVZ/lfTEI6C9KKho45XRcAMx0ziCEWA4sB4iLi+txA1J2vzNWpw9A6r2x6L1o1Jmo1vtTpg+kxuBDtd6HRofSadF5I906vLu4ZJlHk2Ue3dfd6HcIoXnfEEK0e+HQ0rQTwnEMWlqX/O3nHGlO5zvSHaXPUp9Ln4TreTq12V2f2+vu1L67+nCU767Pztegcxud23Pun7s+d74G3dXX1gHRpWzX6+4ir5v6ANbvL6Kxteuirp+3nltS49CJjvumE0I7RrSn69rPdT3WORppK6fTifY+ttcluh5rdbhrq+O7rR2XY5yOdZ2OnWRo+/7B3/ZQWtd1uWF4kKlL2mCjvyipsyKlXAusBW26r6flhfBzq6iE8CPyziddXjquLzWnh9XNy6PLA4W7F1nXOtzW7/zgd6lT+/0/b2RQXt91w1W4vzev/FAbxbv2sftj5752nHP3QulUvpsXqnO/u6vP5eXV6fyZ+tv2e+7TWymq7rr7uafTHoqBx8yRoTz47kGaLB2KymTUs/r6nq3LDERWXj3Orez3Lh7Th726OPQXJVUIxDodxzjSPEbU2MkUHU3DNW6Rgaixk/n2zJ6PzPqKVdeMd/vH+tA145gcE9SHPbs43Ld47JB9WIc6bYpoKFq4DWXZ+4vhhAHNcGIBmnJKA74jpTzsLv/5ekH/16OrKc48gN3egE7nx7Axk/n2ow9dSNf7hKFqitrGUJdfoThfBqLhRL9QUgBCiKuB59FM0P8mpVzdXd6hEqpDoVAoPMlAVFL9ZboPKeVHwEd93Q+FQqFQ9B/63lRNoVAoFIpuUEpKoVAoFP0WpaQUCoVC0W9RSkqhUCgU/ZZ+Y93XE4QQZUC+4zAMKO/D7vQlQ1l2GNryD2XZYWjLfyGyx0spwz3Zmd5mQCopZ4QQ6QPNpNJTDGXZYWjLP5Rlh6Et/1CTXU33KRQKhaLfopSUQqFQKPotg0FJre3rDvQhQ1l2GNryD2XZYWjLP6RkH/BrUgqFQqEYvAyGkZRCoVAoBilKSSkUCoWi3zJglZQQYokQIlMIkS2EeKCv+9NbCCHyhBAHhRD7hRDpjrQQIcRmIUSW4zvYkS6EEH9wXJMDQojkvu19zxBC/E0IUSqEOOSU1mNZhRA/cOTPEkL8oC9kOR+6kf9RIUSh4/7vd0QLaDv3oEP+TCHEYqf0AfdsCCFihRBbhRBHhBCHhRB3OtIH/f0/g+xD4t6fFSnlgPughfPIARIAL+BrYHxf96uXZM0Dwjql/Q54wPH7AeApx++rgY1oAW9nAbv7uv89lPVyIBk4dL6yAiFAruM72PE7uK9luwD5HwV+5SbveMffvTcw0vE86AfqswFEAcmO32a0+HLjh8L9P4PsQ+Len+0zUEdSM4BsKWWulLIVeAtY2sd9upgsBV5z/H4NuN4p/XWpsQsIEkJE9UUHzwcp5XagslNyT2VdDGyWUlZKKauAzcCS3u/9hdON/N2xFHhLStkipTwBZKM9FwPy2ZBSnpZS7nX8rgOOAtEMgft/Btm7Y1Dd+7MxUJVUNHDK6biAM9/UgYwENgkhMoQQyx1pkVLK047fxUCk4/dgvC49lXUwXoM7HFNaf2ub7mIQyy+EGAFMA3YzxO5/J9lhiN17dwxUJTWUuExKmQxcBdwuhLjc+aTUxv9DYh/BUJLViT8DicBU4DTw+77tTu8ihPAH3gFWSClrnc8N9vvvRvYhde+7Y6AqqUIg1uk4xpE26JBSFjq+S4F1aEP6krZpPMd3qSP7YLwuPZV1UF0DKWWJlNImpbQDf0W7/zAI5RdCGNFe0m9KKd91JA+J++9O9qF078/EQFVSacAoIcRIIYQXcAuwoY/75HGEEH5CCHPbb2ARcAhN1jarpR8A6x2/NwC3OiyfZgE1TlMlA5WeyvoJsEgIEeyYHlnkSBuQdFpTvAHt/oMm/y1CCG8hxEhgFLCHAfpsCCEE8ApwVEr5rNOpQX//u5N9qNz7s9LXlhvn+0Gz7jmOZs3yUF/3p5dkTECz0PkaONwmJxAKfAZkAZ8CIY50AbzguCYHgZS+lqGH8v4LbVrDgjaf/uPzkRX4EdpicjZwW1/LdYHy/8Mh3wG0F06UU/6HHPJnAlc5pQ+4ZwO4DG0q7wCw3/G5eijc/zPIPiTu/dk+yi2SQqFQKPotA3W6T6FQKBRDAKWkFAqFQtFvUUpKoVAoFP0WpaQUCoVC0W9RSkqhUCgU/RalpBQKhULRb1FKSqFQKBT9FqWkFAMSIUSoU5yd4k5xd7yEEDt7oc0RwinW03nW4fF+OdX9E6drYHf6/VxvtalQ9DZqM69iwCOEeBSol1I+08vtjAA+kFJOPI+yAu15s3u6X27aigZ2Sinje7sthaK3USMpxaBECFHvGPkcE0L8XQhxXAjxphDiSiHEDkfU1hlO+b8nhNjjGHn8RQih76ZqvRDir44IqpuEECZH+buFEIccnxWOtBGOKKmvo/ldixVC1DvO/dxppHNCCLH1LPUcddduN0xEc6ejUAx4lJJSDHaS0EIcjHV8voPmK+1XwEoAIcQ44FvApVLKqYAN+G439Y0CXpBSTgCqgRuFENOB24CZaFFifyqEmOaU/0Up5QQpZX5bJVLKlxxtpaL56Xv2HOpxafcMMk+iwxmpQjGgUUpKMdg5IaU86JhmOwx8JrU57oPACEeeBcB0IE0Isd9xnHCG+vY7fmc46rgMWCelbJBS1gPvAnMcefKlFjm2O9YAW6SU75+lHnftdocaSSkGDYa+7oBC0cu0OP22Ox3b6fj7F8BrUsoHe1ifDTjTtBtAQ3cnhBA/BOKBOzzc7iRAGUsoBgVqJKVQaKEgbhJCRAAIIUKEED0xOvgCuF4I4euI+3WDI61bHFN7vwK+52RM0eN63NSrQ5saPNqTcgpFf0WNpBRDHinlESHEKmCT4yVvAW4H8s9csr38XiHE39ECzwG8LKXc57AG7I47gBBgq2b4R7qU8ifnUU9nkoACKWVrD8ooFP0WZYKuUCgUin6Lmu5TKBQKRb9FKSmFQqFQ9FuUklIoFApFv0UpKYVCoVD0W5SSUigUCkW/RSkphUKhUPRblJJSKBQKRb/l/wGgH+eHiYAUOwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "for i, alg in enumerate(all_CD_algorithms):\n", " plt.plot(data_X, data_Y[i], 'o-', label=alg.__name__, lw=3)\n", "plt.legend()\n", "plt.xlabel(\"Time horizon $T$\")\n", "plt.ylabel(\"Time complexity in seconds\")\n", "plt.title(\"Comparison of time complexity efficiency of different CD algorithms\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fit time complexity $C^{Algorithm}(T)$ as a function of $T$ in the form $C(T) \\simeq a T^b + c$. Using the function [`scipy.optimize.curve_fit`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html), it is very easy:" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import curve_fit" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [], "source": [ "def time_complexity_general_shape(T, a, b, c):\n", " return a * T**b + c" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For algorithm Monitored,\n", "\ta = 1.96e-06, b = 1.28, c = 0.00103 is the best fit for C(T) = a T^b + c\n", "For algorithm CUSUM,\n", "\ta = 1.21e-06, b = 1.96, c = 0.00036 is the best fit for C(T) = a T^b + c\n", "For algorithm PHT,\n", "\ta = 5.1e-06, b = 1.82, c = -0.0397 is the best fit for C(T) = a T^b + c\n", "For algorithm GaussianGLR,\n", "\ta = 1.44e-05, b = 1.65, c = -0.053 is the best fit for C(T) = a T^b + c\n", "For algorithm BernoulliGLR,\n", "\ta = 2.04e-06, b = 1.83, c = 0.00612 is the best fit for C(T) = a T^b + c\n", "For algorithm SubGaussianGLR,\n", "\ta = 7.92e-07, b = 1.94, c = 0.0144 is the best fit for C(T) = a T^b + c\n" ] } ], "source": [ "for i, alg in enumerate(all_CD_algorithms):\n", " popt, _ = curve_fit(time_complexity_general_shape, data_X, data_Y[i])\n", " a, b, c = popt\n", " print(f\"For algorithm {alg.__name__},\\n\\ta = {a:.3g}, b = {b:.3g}, c = {c:.3g} is the best fit for C(T) = a T^b + c\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check indeed that (roughly) $C(T^{1.3}) = \\mathcal{O}(T)$ for `Monitored`, and $C(T) = \\mathcal{O}(T^2)$ for all the other algorithms!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking detection delay" ] }, { "cell_type": "code", "execution_count": 224, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "def detection_delay(data, tau, CDAlgorithm, *args, **kwargs):\n", " horizon = len(data)\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " for t in range(tau, horizon + 1):\n", " if eval_CDAlgorithm(CDAlgorithm, data, t, *args, **kwargs):\n", " return t - tau\n", " return horizon - tau" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can check the detection delay for our different algorithms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For examples:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a32bd12c12364b3bbe621329344d5502", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Mean detection delay |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $49.4$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $50$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $50$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $50$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $50$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $14.1$ |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Mean detection delay |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $49.4$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $50$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $50$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $50$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $50$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $14.1$ |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = check_onemeasure(detection_delay, \"Mean detection delay\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lot of detection delay are large (ie. it was detected too late), with not enough data! `SubGaussian-GLR` seems to be the only one \"fast enough\", but it triggers false alarms and not correct detections!" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 500 and horizon = 1000...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b6800f66c6f4eb1aad674eaa36ccf75", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Mean detection delay |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $31.7$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $37.9$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $41.8$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $30.3$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $22.7$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $3.86$ |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Mean detection delay |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $31.7$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $37.9$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $41.8$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $30.3$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $22.7$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $3.86$ |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 20.1 s, sys: 149 ms, total: 20.2 s\n", "Wall time: 20.1 s\n" ] } ], "source": [ "%%time\n", "_ = check_onemeasure(detection_delay, \"Mean detection delay\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A very small detection delay, with enough data (a delay of 40 is small when there is $500$ data of $\\nu_1$ and $\\nu_2$) !" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking false alarm probabilities" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [], "source": [ "def false_alarm(data, tau, CDAlgorithm, *args, **kwargs):\n", " horizon = len(data)\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " for t in range(0, tau):\n", " if eval_CDAlgorithm(CDAlgorithm, data, t, *args, **kwargs):\n", " return True\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can check the false alarm probabilities for our different algorithms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For examples:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "17a07b9abe1b450d9bd5b5a82c4b789a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Mean false alarm rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.1$ |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Mean false alarm rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.1$ |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = check_onemeasure(false_alarm, \"Mean false alarm rate\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lot of false alarm for `BernoulliGLR` but not the others, with not enough data!" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 500 and horizon = 1000...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b3186e02e7a04ca4b3b9425743ab80a1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Mean false alarm rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.48$ |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Mean false alarm rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0.48$ |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 1s, sys: 198 ms, total: 3min 1s\n", "Wall time: 3min 1s\n" ] } ], "source": [ "%%time\n", "_ = check_onemeasure(false_alarm, \"Mean false alarm rate\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No false alarm, with enough data!\n", "Only `Sub-Gaussian-GLR` has a lot of false alarms, even with enough data!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking missed detection probabilities" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [], "source": [ "def missed_detection(data, tau, CDAlgorithm, *args, **kwargs):\n", " horizon = len(data)\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " for t in range(tau, horizon + 1):\n", " if eval_CDAlgorithm(CDAlgorithm, data, t, *args, **kwargs):\n", " return False\n", " return True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can check the false alarm probabilities for our different algorithms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For examples:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.4, tau = 50 and horizon = 100...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ce293c2d125342b19562c784dc1ca2e1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Mean missed detection rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.96$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $1$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $1$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $1$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $1$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0$ |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Mean missed detection rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0.96$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $1$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $1$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $1$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $1$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0$ |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = check_onemeasure(missed_detection, \"Mean missed detection rate\", firstMean=0.1, secondMean=0.4, tau=0.5, horizon=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lot of missed detection, with not enough data!" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generating toy Bernoulli data for mu^1 = 0.1, mu^2 = 0.9, tau = 500 and horizon = 1000...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6da846a4ada5469298dcbb54a2f1ac71", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', max=50, style=ProgressStyle(description_width='…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "| Algorithm | Mean missed detection rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0$ |\n", " \n" ] }, { "data": { "text/markdown": [ "\n", "| Algorithm | Mean missed detection rate |\n", "|------|------|\n", "| Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) | $0$ |\n", "| CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') | $0$ |\n", "| Gaussian-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| Bernoulli-GLR($h_0=1$, $\\delta=auto$) | $0$ |\n", "| SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) | $0$ |\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 20.7 s, sys: 200 ms, total: 20.9 s\n", "Wall time: 20.7 s\n" ] } ], "source": [ "%%time\n", "_ = check_onemeasure(missed_detection, \"Mean missed detection rate\", firstMean=0.1, secondMean=0.9, tau=0.5, horizon=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No missed detection, with enough data!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "# More simulations and some plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run a check for a grid of values\n", "\n", "Fix an algorithm, e.g., `Monitored`, then consider one of the quantities defined above (time efficiency, delay, false alarm or missed detection probas).\n", "Now, a piecewise stationary problem is characterized by the parameters $\\mu_1$, $\\Delta = |\\mu_2 - \\mu_1|$, and $\\tau$ and $T$.\n", "\n", "- Fix $\\mu_1=\\frac12$, and so $\\Delta$ can be taken anywhere in $[0, \\frac12]$.\n", "- Fix $T=1000$ for instance, and so $\\tau$ can be taken anywhere in $[0,T]$.\n", "\n", "Of course, if any of $\\tau$ or $\\Delta$ are too small, detection is impossible.\n", "I want to display a $2$D image view, showing on $x$-axis a grid of values of $\\Delta$, on $y$-axis a grid of values of $\\tau$, and on the $2$D image, a color-scale to show the detection delay (for instance)." ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [], "source": [ "mu_1 = 0.5\n", "max_mu_2 = 1\n", "nb_values_Delta = 20\n", "values_Delta = np.linspace(0, max_mu_2 - mu_1, nb_values_Delta)" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [], "source": [ "horizon = T = 1000\n", "min_tau = 10\n", "max_tau = T - min_tau\n", "step = 50\n", "values_tau = np.arange(min_tau, max_tau + 1, step)\n", "nb_values_tau = len(values_tau)" ] }, { "cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This will give a grid of 20 x 20 = 400 values of Delta and tau to explore.\n" ] } ], "source": [ "print(f\"This will give a grid of {nb_values_Delta} x {nb_values_tau} = {nb_values_Delta * nb_values_tau} values of Delta and tau to explore.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now the function:" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [], "source": [ "def check2D_onemeasure(measure,\n", " CDAlgorithm,\n", " values_Delta,\n", " values_tau,\n", " firstMean=mu_1,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=True,\n", " gaussian=False,\n", " n_jobs=1,\n", " *args, **kwargs,\n", " ):\n", " print(f\"\\nExploring {measure.__name__} for algorithm {str_of_CDAlgorithm(CDAlgorithm, *args, **kwargs)} mu^1 = {firstMean} and horizon = {horizon}...\")\n", " nb_values_Delta = len(values_Delta)\n", " nb_values_tau = len(values_tau)\n", " print(f\"with {nb_values_Delta} values for Delta, and {nb_values_tau} values for tau, and {repetitions} repetitions.\")\n", " results = np.zeros((nb_values_Delta, nb_values_tau))\n", " \n", " for i, delta in tqdm(enumerate(values_Delta), desc=\"Delta s\", leave=False):\n", " for j, tau in tqdm(enumerate(values_tau), desc=\"Tau s\", leave=False):\n", " secondMean = firstMean + delta\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " \n", " # now the random Monte Carlo repetitions\n", " for rep in tqdm(range(repetitions), desc=\"Repetitions\", leave=False):\n", " data = get_toy_data(firstMean=firstMean, secondMean=secondMean, tau=tau, horizon=horizon, gaussian=gaussian)\n", " result = measure(data, tau, CDAlgorithm, *args, **kwargs)\n", " results[i, j] += result\n", " results[i, j] /= repetitions\n", " if verbose: print(f\"For delta = {delta} ({i}th), tau = {tau} ({j}th), mean result = {results[i, j]}\")\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A version using `joblib.Parallel` to use multi-core computations\n", "\n", "I want to (try to) use [`joblib.Parallel`](https://joblib.readthedocs.io/en/latest/parallel.html) to run the \"repetitions\" for loop in parallel, for instance on 4 cores on my machine." ] }, { "cell_type": "code", "execution_count": 234, "metadata": {}, "outputs": [], "source": [ "from joblib import Parallel, delayed" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Info: using 4 jobs in parallel!\n" ] } ], "source": [ "# Tries to know number of CPU\n", "try:\n", " from multiprocessing import cpu_count\n", " CPU_COUNT = cpu_count() #: Number of CPU on the local machine\n", "except ImportError:\n", " CPU_COUNT = 1\n", "print(f\"Info: using {CPU_COUNT} jobs in parallel!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can rewrite the `check2D_onemeasure` function to run some loops in parallel." ] }, { "cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [], "source": [ "def check2D_onemeasure_parallel(measure,\n", " CDAlgorithm,\n", " values_Delta,\n", " values_tau,\n", " firstMean=mu_1,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=1,\n", " gaussian=False,\n", " n_jobs=CPU_COUNT,\n", " *args, **kwargs,\n", " ):\n", " print(f\"\\nExploring {measure.__name__} for algorithm {str_of_CDAlgorithm(CDAlgorithm, *args, **kwargs)} mu^1 = {firstMean} and horizon = {horizon}...\")\n", " nb_values_Delta = len(values_Delta)\n", " nb_values_tau = len(values_tau)\n", " print(f\"with {nb_values_Delta} values for Delta, and {nb_values_tau} values for tau, and {repetitions} repetitions.\")\n", " results = np.zeros((nb_values_Delta, nb_values_tau))\n", "\n", " def delayed_measure(i, delta, j, tau, rep):\n", " secondMean = firstMean + delta\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " data = get_toy_data(firstMean=firstMean, secondMean=secondMean, tau=tau, horizon=horizon, gaussian=gaussian)\n", " return i, j, measure(data, tau, CDAlgorithm, *args, **kwargs)\n", "\n", " # now the random Monte Carlo repetitions\n", " for i, j, result in Parallel(n_jobs=n_jobs, verbose=int(verbose))(\n", " delayed(delayed_measure)(i, delta, j, tau, rep)\n", " for i, delta in tqdm(enumerate(values_Delta), desc=\"Delta s ||\", leave=False)\n", " for j, tau in tqdm(enumerate(values_tau), desc=\"Tau s ||\", leave=False)\n", " for rep in tqdm(range(repetitions), desc=\"Repetitions||\", leave=False)\n", " ):\n", " results[i, j] += result\n", " results /= repetitions\n", "\n", " if verbose:\n", " for i, delta in enumerate(values_Delta):\n", " for j, tau in enumerate(values_tau):\n", " print(f\"For delta = {delta} ({i}th), tau = {tau} ({j}th), mean result = {results[i, j]}\")\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking on a small grid of values" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "__main__.Monitored" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Monitored" ] }, { "cell_type": "code", "execution_count": 233, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring time_efficiency for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 3 values for Delta, and 5 values for tau, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s', max=1, style=ProgressStyle(descri…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s', max=1, style=ProgressStyle(descript…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 100 (0th), mean result = 0.018056981563568116\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 250 (1th), mean result = 0.017168304920196532\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 500 (2th), mean result = 0.01913909912109375\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 750 (3th), mean result = 0.027340266704559326\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 900 (4th), mean result = 0.023344976902008055\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s', max=1, style=ProgressStyle(descript…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 100 (0th), mean result = 0.023643691539764405\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 250 (1th), mean result = 0.03245354175567627\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 500 (2th), mean result = 0.032449700832366944\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 750 (3th), mean result = 0.018729052543640136\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 900 (4th), mean result = 0.026870133876800536\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s', max=1, style=ProgressStyle(descript…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 100 (0th), mean result = 0.02412700653076172\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 250 (1th), mean result = 0.025472092628479003\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 500 (2th), mean result = 0.019556210041046143\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 750 (3th), mean result = 0.01786205768585205\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 900 (4th), mean result = 0.019401206970214843\n", "CPU times: user 45.5 s, sys: 1.88 s, total: 47.4 s\n", "Wall time: 46.6 s\n" ] } ], "source": [ "%%time\n", "_ = check2D_onemeasure(time_efficiency,\n", " Monitored,\n", " values_Delta=[0.05, 0.25, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=100)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring time_efficiency for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 3 values for Delta, and 5 values for tau, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 110 tasks | elapsed: 1.9s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 1010 tasks | elapsed: 11.4s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 0.1 (0th), mean result = 0.034742271900177\n", "For delta = 0.05 (0th), tau = 0.25 (1th), mean result = 0.03206165313720703\n", "For delta = 0.05 (0th), tau = 0.5 (2th), mean result = 0.03520160436630249\n", "For delta = 0.05 (0th), tau = 0.75 (3th), mean result = 0.03601207733154297\n", "For delta = 0.05 (0th), tau = 0.9 (4th), mean result = 0.036164679527282716\n", "For delta = 0.25 (1th), tau = 0.1 (0th), mean result = 0.03190211772918701\n", "For delta = 0.25 (1th), tau = 0.25 (1th), mean result = 0.03288561344146729\n", "For delta = 0.25 (1th), tau = 0.5 (2th), mean result = 0.032886896133422855\n", "For delta = 0.25 (1th), tau = 0.75 (3th), mean result = 0.03128063201904297\n", "For delta = 0.25 (1th), tau = 0.9 (4th), mean result = 0.03194632768630981\n", "For delta = 0.5 (2th), tau = 0.1 (0th), mean result = 0.03349523544311524\n", "For delta = 0.5 (2th), tau = 0.25 (1th), mean result = 0.03411170482635498\n", "For delta = 0.5 (2th), tau = 0.5 (2th), mean result = 0.03451453685760498\n", "For delta = 0.5 (2th), tau = 0.75 (3th), mean result = 0.03463083505630493\n", "For delta = 0.5 (2th), tau = 0.9 (4th), mean result = 0.03331227302551269\n", "CPU times: user 1.05 s, sys: 121 ms, total: 1.17 s\n", "Wall time: 16.5 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 1500 out of 1500 | elapsed: 16.4s finished\n" ] } ], "source": [ "%%time\n", "_ = check2D_onemeasure_parallel(time_efficiency,\n", " Monitored,\n", " values_Delta=[0.05, 0.25, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=100,\n", " n_jobs=4)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 3 values for Delta, and 5 values for tau, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s', max=1, style=ProgressStyle(descri…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s', max=1, style=ProgressStyle(descript…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 100 (0th), mean result = 900.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 250 (1th), mean result = 750.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", 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"application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s', max=1, style=ProgressStyle(descript…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 100 (0th), mean result = 900.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 250 (1th), mean result = 750.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 500 (2th), mean result = 500.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 750 (3th), mean result = 250.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.25 (1th), tau = 900 (4th), mean result = 100.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s', max=1, style=ProgressStyle(descript…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 100 (0th), mean result = 753.63\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 250 (1th), mean result = 671.52\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 500 (2th), mean result = 458.34\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 750 (3th), mean result = 228.66\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions', style=ProgressStyle(description_width='initial'…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.5 (2th), tau = 900 (4th), mean result = 93.58\n", "\r", "CPU times: user 19.3 s, sys: 1.25 s, total: 20.5 s\n", "Wall time: 18.9 s\n" ] } ], "source": [ "%%time\n", "_ = check2D_onemeasure(detection_delay,\n", " Monitored,\n", " values_Delta=[0.05, 0.25, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=100)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 3 values for Delta, and 5 values for tau, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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(1th), tau = 0.1 (0th), mean result = 900.0\n", "For delta = 0.25 (1th), tau = 0.25 (1th), mean result = 750.0\n", "For delta = 0.25 (1th), tau = 0.5 (2th), mean result = 500.0\n", "For delta = 0.25 (1th), tau = 0.75 (3th), mean result = 250.0\n", "For delta = 0.25 (1th), tau = 0.9 (4th), mean result = 100.0\n", "For delta = 0.5 (2th), tau = 0.1 (0th), mean result = 813.76\n", "For delta = 0.5 (2th), tau = 0.25 (1th), mean result = 657.41\n", "For delta = 0.5 (2th), tau = 0.5 (2th), mean result = 439.97\n", "For delta = 0.5 (2th), tau = 0.75 (3th), mean result = 233.06\n", "For delta = 0.5 (2th), tau = 0.9 (4th), mean result = 94.97\n", "CPU times: user 975 ms, sys: 42.1 ms, total: 1.02 s\n", "Wall time: 19.2 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 1500 out of 1500 | elapsed: 19.2s finished\n" ] } ], "source": [ "%%time\n", "_ = check2D_onemeasure_parallel(detection_delay,\n", " Monitored,\n", " values_Delta=[0.05, 0.25, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=100\n", " )" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring false_alarm for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 3 values for Delta, and 5 values for tau, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, 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}, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 0.1 (0th), mean result = 0.0\n", "For delta = 0.05 (0th), tau = 0.25 (1th), mean result = 0.0\n", "For delta = 0.05 (0th), tau = 0.5 (2th), mean result = 0.0\n", "For delta = 0.05 (0th), tau = 0.75 (3th), mean result = 0.0\n", "For delta = 0.05 (0th), tau = 0.9 (4th), mean result = 0.0\n", "For delta = 0.25 (1th), tau = 0.1 (0th), mean result = 0.0\n", "For delta = 0.25 (1th), tau = 0.25 (1th), mean result = 0.0\n", "For delta = 0.25 (1th), tau = 0.5 (2th), mean result = 0.0\n", "For delta = 0.25 (1th), tau = 0.75 (3th), mean result = 0.0\n", "For delta = 0.25 (1th), tau = 0.9 (4th), mean result = 0.0\n", "For delta = 0.5 (2th), tau = 0.1 (0th), mean result = 0.0\n", "For delta = 0.5 (2th), tau = 0.25 (1th), mean result = 0.0\n", "For delta = 0.5 (2th), tau = 0.5 (2th), mean result = 0.0\n", "For delta = 0.5 (2th), tau = 0.75 (3th), mean result = 0.0\n", "For delta = 0.5 (2th), tau = 0.9 (4th), mean result = 0.0\n", "CPU times: user 820 ms, sys: 41.8 ms, total: 861 ms\n", "Wall time: 11.5 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 1500 out of 1500 | elapsed: 11.4s finished\n" ] } ], "source": [ "%%time\n", "_ = check2D_onemeasure_parallel(false_alarm,\n", " Monitored,\n", " values_Delta=[0.05, 0.25, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=100,\n", " )" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 3 values for Delta, and 5 values for tau, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 348 tasks | elapsed: 3.5s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', style=ProgressStyle(description_width='initia…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For delta = 0.05 (0th), tau = 0.1 (0th), mean result = 1.0\n", "For delta = 0.05 (0th), tau = 0.25 (1th), mean result = 1.0\n", "For delta = 0.05 (0th), tau = 0.5 (2th), mean result = 1.0\n", "For delta = 0.05 (0th), tau = 0.75 (3th), mean result = 1.0\n", "For delta = 0.05 (0th), tau = 0.9 (4th), mean result = 1.0\n", "For delta = 0.25 (1th), tau = 0.1 (0th), mean result = 1.0\n", "For delta = 0.25 (1th), tau = 0.25 (1th), mean result = 1.0\n", "For delta = 0.25 (1th), tau = 0.5 (2th), mean result = 1.0\n", "For delta = 0.25 (1th), tau = 0.75 (3th), mean result = 1.0\n", "For delta = 0.25 (1th), tau = 0.9 (4th), mean result = 1.0\n", "For delta = 0.5 (2th), tau = 0.1 (0th), mean result = 0.93\n", "For delta = 0.5 (2th), tau = 0.25 (1th), mean result = 0.91\n", "For delta = 0.5 (2th), tau = 0.5 (2th), mean result = 0.89\n", "For delta = 0.5 (2th), tau = 0.75 (3th), mean result = 0.92\n", "For delta = 0.5 (2th), tau = 0.9 (4th), mean result = 0.83\n", "CPU times: user 930 ms, sys: 44.8 ms, total: 974 ms\n", "Wall time: 11.6 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 1500 out of 1500 | elapsed: 11.6s finished\n" ] } ], "source": [ "%%time\n", "_ = check2D_onemeasure_parallel(missed_detection,\n", " Monitored,\n", " values_Delta=[0.05, 0.25, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=100,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the result as a 2D image" ] }, { "cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [], "source": [ "import matplotlib as mpl\n", "FIGSIZE = (19.80, 10.80) #: Figure size, in inches!\n", "mpl.rcParams['figure.figsize'] = FIGSIZE\n", "import matplotlib.ticker as ticker\n", "import matplotlib.pyplot as plt " ] }, { "cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [], "source": [ "#: List of formats to use for saving the figures, by default.\n", "#: It is a smart idea to save in both a raster and vectorial formats\n", "FORMATS = ('png', 'pdf')\n", "\n", "import pickle\n", "from datetime import datetime\n", "from os.path import getsize, getatime\n", "\n", "\n", "def show_and_save(showplot=True, savefig=None, formats=FORMATS, pickleit=False, fig=None):\n", " \"\"\" Maximize the window if need to show it, save it if needed, and then show it or close it.\n", "\n", " - Inspired by https://tomspur.blogspot.fr/2015/08/publication-ready-figures-with.html#Save-the-figure\n", " \"\"\"\n", " if savefig is not None:\n", " if pickleit and fig is not None:\n", " form = \"pickle\"\n", " path = \"{}.{}\".format(savefig, form)\n", " print(\"Saving raw figure with format {}, to file '{}'...\".format(form, path)) # DEBUG\n", " with open(path, \"bw\") as f:\n", " pickle_dump(fig, f)\n", " print(\" Saved! '{}' created of size '{}b', at '{:%c}' ...\".format(path, getsize(path), datetime.fromtimestamp(getatime(path))))\n", " for form in formats:\n", " path = \"{}.{}\".format(savefig, form)\n", " print(\"Saving figure with format {}, to file '{}'...\".format(form, path)) # DEBUG\n", " plt.savefig(path, bbox_inches=None)\n", " print(\" Saved! '{}' created of size '{}b', at '{:%c}' ...\".format(path, getsize(path), datetime.fromtimestamp(getatime(path))))\n", " try:\n", " plt.show() if showplot else plt.close()\n", " except (TypeError, AttributeError):\n", " print(\"Failed to show the figure for some unknown reason...\") # DEBUG" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the function:" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "code_folding": [ 6 ] }, "outputs": [], "source": [ "def view2D_onemeasure(measure, name,\n", " CDAlgorithm,\n", " values_Delta,\n", " values_tau,\n", " firstMean=mu_1,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " n_jobs=CPU_COUNT,\n", " savefig=None,\n", " *args, **kwargs,\n", " ):\n", " check = check2D_onemeasure_parallel if n_jobs > 1 else check2D_onemeasure\n", " results = check(measure, CDAlgorithm,\n", " values_Delta, values_tau,\n", " firstMean=firstMean, \n", " horizon=horizon,\n", " repetitions=repetitions,\n", " verbose=False,\n", " gaussian=gaussian,\n", " n_jobs=n_jobs,\n", " *args, **kwargs,\n", " )\n", " fig = plt.figure()\n", "\n", " plt.matshow(results)\n", " plt.colorbar(shrink=0.7)\n", "\n", " plt.locator_params(axis='x', nbins=1+len(values_tau))\n", " plt.locator_params(axis='y', nbins=len(values_Delta))\n", "\n", " ax = plt.gca()\n", " # https://stackoverflow.com/a/19972993/\n", " loc = ticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals\n", " ax.xaxis.set_major_locator(loc)\n", " ax.xaxis.set_ticks_position('bottom')\n", " def y_fmt(tick_value, pos): return '{:.3g}'.format(tick_value)\n", " ax.yaxis.set_major_formatter(ticker.FuncFormatter(y_fmt))\n", " ax.yaxis.set_major_locator(loc)\n", "\n", " # hack to display the ticks labels as the actual values\n", " if np.max(values_tau) <= 1:\n", " values_tau = np.floor(np.asarray(values_tau) * horizon)\n", " values_tau = list(np.asarray(values_tau, dtype=int))\n", " values_Delta = np.round(values_Delta, 3)\n", " ax.set_xticklabels([0] + list(values_tau)) # hack: the first label is not displayed??\n", " ax.set_yticklabels([0] + list(values_Delta)) # hack: the first label is not displayed??\n", "\n", " plt.title(fr\"{name} for algorithm {str_of_CDAlgorithm(CDAlgorithm, *args, **kwargs)}, for $T={horizon}$, {'Gaussian' if gaussian else 'Bernoulli'} data and $\\mu_1={firstMean:.3g}$ and ${repetitions}$ repetitions\")\n", " plt.xlabel(r\"Value of $\\tau$ time of breakpoint\")\n", " plt.ylabel(r\"Value of gap $\\Delta = |\\mu_2 - \\mu_1|$\")\n", "\n", " \n", " show_and_save(savefig=savefig)\n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First example\n", "#### For `Monitored`" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 5 values for Delta, and 5 values for tau, and 50 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', max=50, style=ProgressStyle(description_width…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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'Detection_delay_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus.png' created of size '24517b', at 'Sun Dec 16 22:41:23 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus.pdf'...\n", " Saved! 'Detection_delay_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus.pdf' created of size '25149b', at 'Sun Dec 16 22:42:43 2018' ...\n" ] }, { "data": { "text/plain": [ "
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07ufsS/P9GOa+q33nttt+bFW9v6qezF3Nnt+yyl2AMZKBJPdL8hyaH7D/r5qza3wRuClNx4UfSbIuyaPahAGaI7YP6XmYlZb/Is0K8+Yk906yc5In9dy3/7F6rfZjO4rPA3cAv90+1vO564d5tdfb6940H8h3AZK8nGZnC9iqU9j7gS9W1beGjG8tXuuKsfVYbSdlW2NZ6TPd3o6lufDSLQPmreX61P8aR33slda5ldbVJYN2JK+nabu5dHaS79H0C3lXVW3uD6CqnlV3ndWhf3hW37LX0CRav9GuM7vQdNI+v51/C81O9xvb7/mTaC56dSrc2THxvcu8F49uX+9Lktwjyc/StJe82ykvR4m55z7fpil7P62d9DM0VY7lDLx414DH/SHNZ/wsmjawfwO8tKq+2jP/+TTr5A00lcJPAktnyTkFeHaSH4E7f5R/jWbn+Ds9Owe/2PfUS0cgh/Vc4Kzqubjakv5EYZXPqd9afZ9WTW5G/dzbdXRnmv5D69rfnR3ax1pxXV1p/mr3bZ97qPcwjSNpKmxLp9Qd5bdoGKt9RufRfO/WJTmCu29TRrXatmu136j+7eqwv2mjLjtq3P0eAzwoyY+3398/odlZu3SYJxt1fR5mvVuSZJckP7O0zrfbj6ew/DZvIu/banGN8hqXMcx35xVJ9k6yK/Ba4AND3ne1fZrtsh+b5rouz0hzmuXbaFpIbFnlbsBoycAnktxE88P3WuAvgJdDc8SC5qj2wTQ7ANfQtOG9f3vf/wm8Lk055TUrLd/Oey7wUJoOyptoOqgw6LF6A1ztx3YUPT/ML6NpCvULtEePh3i9vY9zEfB/aL4EV9HsyPQfZTy5nT7sSrwmr3XI2FbdSVmDWJb9TLe3qvpGVW1YZt6arU/c/Tsw0mOv8p1Zdl3tsdWOZOt6mr4gS2e/+R5NW/C/HeP1DfJ8mv4H36Xpk3A7TfKx5DdpOnJdTdOR9jeqaumozj4sczSeZj09BXhS+xreABxZVV9fo7ihqYwsNRW6T1UNOpMQAFV1ZQ2+eNfS/P2q6l/a2xdW1VOr6v5V9ciq+mjfshuq6uD2R/6FNNXXTe28a2he96+145e1FYid+3YO3tf3mIdW1ShnhnoNTce4QfoThZU+p62s4fdp1ORmGK+j+eE8nmb79v122pKV1tXV5q9239Xew08kuZnm+/mnwDFL9x/lt2gYQ3xGr2rn30BzKse7VRDHeL5lt11D/Eb1b1eH+k0b8rHHjnuAR9P0RTqLZlt4E833+rXDPN+Yll3v0lQa/rBdbkeajvNLHXV/i6Zp2n8OetAJvm/DxLXad2ulWIb57ryf5uxvlwDfaOMZeZ93wNNvr/3Ye9Kc+v8ammZEDwL+YIj7NWet0HSlOb3jV2maa/R3iJlJSc4G3llV75l2LBpOkj8Drq6qv5x2LCtJ0772P4DH1OA28JOI4ZE0P97PAp5UVaterK09Un8a8JRx407yVJqzXF1Ds7P1TpqzAPVf+Xgq2u/9sVV1wSx8TvPO97Ab0jTl+buq+vAKy7wX+POqumBigWlZSS6lORPav0w7lknwwgtTlqZd2KuB02Y5ERiwk/IYVm46oRlTVX+4+lLT1x4ZecSUY7goyW3AH9MckVpRBly8a0wHAqfTlOIvAV4wK4kAQFU9oef21D+need72BmPZoWrpSc5g+Yo84FJ3lVV751UYBKYDExVmotmXEXTU/yIKYezmpneSZG2g48BTx3Udr5XVr5410iq6kSaU+hJWgBpLn75IJp+SAPVgJOPSJNkMyFJGqDtkPbkqlrxbAxJfhn4S5oLtwGcUFufs1+SpJllMiBJkiR11LZedEySJEnSnDIZkCRJkjrKZECSJEnqKJMBSZIkqaNMBiRJkqSOMhmQJEmSOspkQJIkSeookwFJkiSpo0wGJEmSpI4yGZAkSZI6ymRAkiRJ6iiTAUmSJKmjTAYkSZKkjjIZkCRJkjrKZECSJEnqKJMBSZIkqaNMBiRJkqSOMhmQJEmSOspkQJIkSeookwFJkiSpo0wGJEmSpI4yGZAkSZI6ymRAkiRJ6iiTAUmSJKmjTAYkSZKkjtph2gFovu2+67rab58dpx1GZ115x87TDqHzrrnpvtMOofPueUNNO4Ruu+nWaUfQeTdx/TVV9cBpx6H5ZDKgbbLfPjvyxU/tM+0wOuvPrjlw2iF03klnPn3aIXTe/h//4bRD6LR1//rlaYfQef+y5YOXTTsGzS+bCUmSJEkdZTIgSZIkdZTJgCRJktRRJgOSJElSR5kMSJIkSR1lMiBJkiR1lMmAJEmS1FEmA5IkSVJHmQxIkiRJHWUyIEmSJHWUyYAkSZLUUSYDkiRJUkeZDEiSJEkdZTIgSZIkdZTJgCRJktRRJgOSJElSR5kMSJIkSR1lMiBJkiR1lMmAJEmS1FEmA5IkSVJHmQxIkiRJHWUyIEmSJHWUyYAkSZLUUSYDkiRJUkeZDEiSJEkdZTIgSZKkuZDkVUkuSHJhkt9pp+2a5NNJvt7+f0A7PUnenmRjkvOTPHa60c8mkwFJkiTNvCSPAv4rcBhwEPCcJA8FjgfOrKoDgDPbcYBnAQe0w3HACRMPeg6YDEiSJGkePAI4u6purao7gH8Fng8cCZzcLnMycFR7+0jglGp8AdglyZ6TDnrW7TDtACRJkjR/fubp965rr9u8po957vk/uBC4rWfSiVV1Ynv7AuBPk+wGfB94NrAB2KOqrmyX+Q6wR3t7L+Dynsfa1E67Et3JZECSJEkju/a6zXzxU/uu6WOu2/Prt1XVoYPmVdXFSd4C/DNwC3AesLlvmUpSaxrUgrOZkCRJkkZWwJY1/lv1OatOqqrHVdVTgOuB/wSuWmr+0/6/ul38CmCfnrvv3U5TD5MBSZIkzYUkD2r/70vTX+D9wHrgmHaRY4CPt7fXAy9tzyr0RODGnuZEatlMSJIkSWMoNtfqR/PX2IfbPgO3A6+oqhuSvBk4PcmxwGXAi9plz6DpV7ARuBV4+aSDnQcmA5IkSRpZ00xoss3zq+qnBky7Fjh8wPQCXjGJuOaZzYQkSZKkjrIyIEmSpLEM0+lXs83KgCRJktRRVgYkSZI0sqLYXJ7Sf96ZDEiSJGksk+5ArLVnMyFJkiSpo6wMSJIkaWQFbLYyMPesDEiSJEkdZWVAkiRJY7HPwPyzMrCAkhyR5GtJNiY5fsD8eyb5QDv/7CT7tdP3S/L9JOe1wzsnHbskSZoPBWyuWtNBk2dlYMEkWQe8A3gmsAk4J8n6qrqoZ7Fjgeur6qFJjgbeAvxCO+8bVXXwRIOWJEnSVFgZWDyHARur6pKq+iFwGnBk3zJHAie3tz8EHJ4kE4xRkiQtgC1rPGjyTAYWz17A5T3jm9ppA5epqjuAG4Hd2nn7J/lykn9N8lODniDJcUk2JNnw3Ws3r230kiRpLhTF5jUeNHkmA+p1JbBvVR0CvBp4f5L79S9UVSdW1aFVdegDd1s38SAlSZK0NuwzsHiuAPbpGd+7nTZomU1JdgDuD1xbVQX8AKCqzk3yDeBhwIbtHrUkSZovBZs9mD/3rAwsnnOAA5Lsn2Qn4Ghgfd8y64Fj2tsvAD5TVZXkgW0HZJI8BDgAuGRCcUuSJGnCrAwsmKq6I8krgU8B64B3V9WFSd4IbKiq9cBJwKlJNgLX0SQMAE8B3pjkdpp+PL9eVddN/lVIkqRZV9jpdxGYDCygqjoDOKNv2ut7bt8GvHDA/T4MfHi7ByhJkhZA2IwnI5x3NhOSJEmSOsrKgCRJkkZWwBY7EM89KwOSJElSR1kZkCRJ0ljsMzD/TAYkSZI0ssJkYBHYTEiSJEnqKCsDkiRJGsuWsjIw76wMSJIkSR1lZUCSJEkjs8/AYjAZkCRJ0siKsNlGJnPPT1CSJEnqKCsDkiRJGosdiOeflQFJkiSpo6wMSJIkaWR2IF4MJgOSJEkaQ9hcNjKZd36CkiRJUkdZGZAkSdLICtjiceW55ycoSZIkdZSVAUmSJI3FDsTzz2RAkiRJI6uyA/Ei8BOUJEmSOsrKgCRJksayxWZCc89kQJIkSSNrLjpmI5N55ycoSZIkdZSVAUmSJI3BDsSLwE9QkiRJcyHJf0tyYZILkvx9kp2T7J/k7CQbk3wgyU7tsvdsxze28/ebbvSzyWRAkiRJI1u6AvFaDitJshfw28ChVfUoYB1wNPAW4K1V9VDgeuDY9i7HAte309/aLqc+JgOSJEkay+bKmg5D2AH4kSQ7APcCrgSeAXyonX8ycFR7+8h2nHb+4Uk8/VEfkwFJkiTNit2TbOgZjluaUVVXAH8OfIsmCbgROBe4oaruaBfbBOzV3t4LuLy97x3t8rtN5mXMDzsQS5IkaWRFtsepRa+pqkMHzUjyAJqj/fsDNwAfBI5Y6wC6xsqAJEmS5sFPA9+squ9W1e3AR4AnAbu0zYYA9gauaG9fAewD0M6/P3DtZEOefSYDkiRJGsuWuseaDqv4FvDEJPdq2/4fDlwEfBZ4QbvMMcDH29vr23Ha+Z+pqlrTN2AB2ExIkiRJI5v0FYir6uwkHwK+BNwBfBk4EfgH4LQkb2qnndTe5STg1CQbgetozjykPiYDkiRJmgtV9QbgDX2TLwEOG7DsbcALJxHXPDMZkCRJ0siKoU8HqhlmnwFJkiSpo6wMSJIkaSyrXTVYs89kQJIkSSOrgs2rnwFIM85PUJIkSeooKwOSJEkaQ9iCHYjnnZUBSZIkqaOsDEiSJGlkhX0GFoHJgCRJksYyySsQa/vwE5QkSZI6ysqAJEmSRlaELV6BeO5ZGZAkSZI6ysqAJEmSxmKfgflnMiBJkqSRFbDFswnNPT9BSZIkqaOsDEiSJGkMYbNXIJ57JgOSJEkamc2EFoOfoCRJktRRVgYkSZI0FpsJzT8rA5IkSVJHWRmQJEnSyKpin4EFYDIgSZKksWw2GZh7foKSJElSR1kZkCRJ0sgK2GIH4rlnZUCSJEnqKCsDkiRJGkPsM7AATAYkSZI0suYKxDYTmnemc5IkSVJHWRmQJEnSWDZ7XHnu+QlKkiRJHWVlQJIkSSMrYp+BBWAyIEmSpLFssZHJ3PMTlCRJkjrKyoAkSZJGVgWbbSY096wMSJIkSR1lZUCSJEljsQPx/DMZkCRJ0siaswnZyGTe+QlKkiRJHWVlQJIkSWPZjM2E5p2VAUmSJKmjrAxIkiRpZIUdiBeByYAkSZLGYAfiReAnKEmSJHWUyYAkSZLGsoWs6bCaJAcmOa9n+F6S30mya5JPJ/l6+/8B7fJJ8vYkG5Ocn+Sx2/1NmTMmA5IkSRpZFWyurOmw+nPW16rq4Ko6GHgccCvwUeB44MyqOgA4sx0HeBZwQDscB5ywHd6KuWYyIEmSpHl0OPCNqroMOBI4uZ1+MnBUe/tI4JRqfAHYJcmekw91dtmBWJIkSWOZcgfio4G/b2/vUVVXtre/A+zR3t4LuLznPpvaaVciwMqAJEmSZsfuSTb0DMcNWijJTsDzgA/2z6uqojnzqYZgZUCSJEkjK7I9rjNwTVUdOsRyzwK+VFVXteNXJdmzqq5smwFd3U6/Atin5357t9PUsjIgSZKksUz6bEI9XsxdTYQA1gPHtLePAT7eM/2l7VmFngjc2NOcSFgZkCRJ0hxJcm/gmcCv9Ux+M3B6kmOBy4AXtdPPAJ4NbKQ589DLJxjqXDAZkCRJ0sgKtkczodWft+oWYLe+adfSnF2of9kCXjGh0OaSzYQkSZKkjjIZ6JAkRyT5WnsVvuMHzH9Kki8luSPJC6YRoyRJmh9b6h5rOmjybCbUEUnWAe+gaWO3CTgnyfqquqhnsW8BLwNeM/kIJUnSXKntcjYhTZjJQHccBmysqksAkpxGc1W+O5OBqrq0nbdlGgFKkiRpskwGumPQFfieMM4DtRcAOQ5g371chSRJ6qKCUU8Hqhlk4yyNrKpOrKpDq+rQB+62btrhSJIkaUwe1u0Or8AnSZLWlH0G5p/JQHecAxyQZH+aJOBo4CXTDUmSJM2raV1nQGvLZkIdUVV3AK8EPgVcDJxeVRcmeWOS5wEkeXySTcALgXcluXB6EUuSJGl7szLQIVV1Bs1luXunvb7n9jk0zYckSZJWZWVg/lkZkCRJkjrKyoAcBHgJAAAgAElEQVQkSZJGVnjRsUVgMiBJkqSxeJ2B+WczIUmSJKmjrAxIkiRpdGUH4kVgZUCSJEnqKCsDkiRJGpkXHVsMJgOSJEkai8nA/LOZkCRJktRRVgYkSZI0Mq8zsBhMBiRJkjSWMhmYezYTkiRJkjrKyoAkSZLG4hWI55+VAUmSJKmjrAxIkiRpZOUViBeCyYAkSZLGYgfi+WczIUmSJKmjrAxIkiRpDF5nYBFYGZAkSZI6ysqAJEmSxmKfgflnMiBJkqSRFZ5NaBHYTEiSJEnqKCsDkiRJGl011xrQfLMyIEmSJHWUlQFJkiSNZQv2GZh3JgOSJEkaWeHZhBaBzYQkSZKkjrIyIEmSpDF4BeJFYGVAkiRJ6igrAzMiyb5DLnpDVX1vuwYjSZI0BE8tOv9MBmbHyTR9cVaqtxXwXuCUSQQkSZK0EjsQzz+TgRlRVU+fdgySJEnqFpMBSZIkjazKysAisAPxnEjy+9OOQZIkSYvFysCMSnJ67yhwMPCWKYUjSZJ0N9M4tWiSXYC/Ax5F05/yV4CvAR8A9gMuBV5UVdcnCfA24NnArcDLqupLEw96hlkZmF3fq6oXtcMLgX+ZdkCSJEm9mqZCazcM6W3AP1XVw4GDgIuB44Ezq+oA4Mx2HOBZwAHtcBxwwhq+/IVgMjC7/rRv/LVTiUKSJGlGJLk/8BTgJICq+mFV3QAcSXNmRtr/R7W3jwROqcYXgF2S7DnhsGeazYRmTJKvAOcD5yc5H/gKcExV9ScHkiRJU7UdOhDvnmRDz/iJVXViz/j+wHeB9yQ5CDgXeBWwR1Vd2S7zHWCP9vZewOU999/UTrsSAVYGZtFTgb8Fvg8cDVxA085NkiRp0V1TVYf2DCf2zd8BeCxwQlUdAtzCXU2CAKiqoulLoCFYGZgxVXUdcFY7kOQA4HVTDEmSJOluikzj1KKbgE1VdXY7/iGaZOCqJHtW1ZVtM6Cr2/lXAPv03H/vdppaVgZmTJKH9Y5X1deBx0wpHEmSpGXVGg+rPl/Vd4DLkxzYTjocuAhYDxzTTjsG+Hh7ez3w0jSeCNzY05xIWBmYRe9K8uM0Wev5wM7ABUnuVVW3Tjc0SZKkqfst4H1JdgIuAV5Oc4D79CTHApcBL2qXPYOmufVGmlOLvnzy4c42k4EZU1VPB0iyL83psg5u/5+XZEt7Gi1JkqTpmtIViKvqPODQAbMOH7BsAa/Y7kHNMZOBGVVV3wK+BXxiaVqS+0wvIkmSpD5205179hmYI1V187RjkCRJ0uKwMiBJkqSxTKOZkNaWlYEZleS5045BkiRJi81kYHZ5xWFJkjTTqtZ20OTZTGh2WXeTJEkzq7CZ0CKwMjC7zI8lSZK0XVkZkCRJ0ugKsDIw96wMSJIkSR1lZWB2XTXtACRJklZip9/5ZzIwo6rqmdOOQZIkaUUmA3PPZkKSJElSR1kZkCRJ0hjiqUUXgJWBGZXkmUn+NsnB7fhx045JkiRJi8XKwOz6FeA3gNcl2RU4eMrxSJIkbc0+A3PPZGB23VRVNwCvSfJm4PHTDkiSJOlO5RWIF4HNhGbXPyzdqKrjgVOmGIskSZIWkJWBGVVVH+8b/6tpxSJJkjSQzYTmnsnAjEnyFeD8nuErwDFV9adTDUySJEkLx2ZCs+epwN8C3weOBi4Anj3ViCRJkgbKGg+aNCsDM6aqrgPOageSHAC8boohSZIkDWYzoblnZWDGJHlY73hVfR14zJTCkSRJ0gKzMjB73pXkx4EraPoM7AxckOReVXXrdEOTJEnqYWVg7pkMzJiqejpAkn2Bg2guNnYQcF6SLVX18GnGJ0mSpMVhMjAj2p3/fv/RDtD0qrlvkvtV1fcmF5kkSdIABXjRsblnMjA7Tqb5WvV/q/oLcO/FC5BJkqQZUDYTmnsmAzNiqXmQJEmSNCkmA5IkSRqPlYG5ZzIgSZKk8dhnYO55nYEZlOQZvf8lSZKk7cFkYDb9ed9/SZKkmZNa20GTZzIw26y9SZIkabuxz4AkSZJGV9iBeAGYDEiSJGkMsQPxArCZkCRJktRRVgZm083t/5umGoUkSdJKbCY096wMzKCqekrvf0mSJGl7sDIgSZKk8VgZmHsmA5IkSRqPycDcs5nQjEvy5CTvmHYckiRJWjxWBmZQkkOAlwAvAr4DPBx4xVSDkiRJ6lV4atEFYDIwI5I8DHgxTRJwE/BB4GlV9c0k35xqcJIkSVpIJgOz46vAOcALquorffNskSdJkmZO3EOZe/YZmB3PB74J/HOSU5M8N8mO0w5KkiRpWbXGwxCSXJrkK0nOS7KhnbZrkk8n+Xr7/wHt9CR5e5KNSc5P8tg1euULw2RgRlTVx6rqaOChwD8CxwGbkrwHuN8oj5XkiCRfa1f84wfMf3WSi9ovxZlJfqxn3ub2y3VekvXb+LIkSZK2h6dX1cFVdWg7fjxwZlUdAJzZjgM8CzigHY4DTph4pDPOZGDGVNUtVfX+qnouTcfhzwPnD3v/JOuAd9Cs/I8EXpzkkX2LfRk4tKoeA3wI+F89877ffrkOrqrnbctrkSRJmpAjgZPb2ycDR/VMP6UaXwB2SbLnNAKcVSYDM6yqrq+qE6vqGSPc7TBgY1VdUlU/BE6j+SL0Pu5nq+rWdvQLwN5rE7EkSdI22T3Jhp7huAHLFE2z6nN75u9RVVe2t78D7NHe3gu4vOe+m9ppatmBePEMWumfsMLyx9I0S1qyc9v+7g7gzVX1sbUPUZIkLYLt0IH4mp6mP8t5clVdkeRBwKeTfLV3ZlVVYtfmYZkMdFiSXwIOBZ7aM/nH2i/YQ4DPJPlKVX2j737H0bS7Y9+9XIUkSeqsKVxnoKquaP9fneSjNK0irkqyZ1Vd2TYDurpd/Apgn567791OU8tmQotnqJU+yU8DrwWeV1U/WJre8wW7BDgLOKT/vm3TpUOr6tAH7rZubaOXJElaRpJ7J7nv0m3gvwAXAOuBY9rFjgE+3t5eD7y0PavQE4Ebe5oTCSsDMyfJzsBvAk+maRP3OeCEqrptyIc4Bzggyf40ScDRNBcy632OQ4B3AUdU1dU90x8A3FpVP0iyO/Aktu5cLEmS1BjhdKBraA/go0mg2Y99f1X9U5JzgNOTHAtcBryoXf4M4NnARuBW4OUTj3jGmQzMnlNorkD8V+34S4BTgRcOc+equiPJK4FPAeuAd1fVhUneCGyoqvXA/wbuA3yw/TJ9qz1z0COAdyXZQlM1enNVXbR2L02SJGl8bcuFgwZMvxY4fMD0Al4xgdDmlsnA7HlUVfWeCvSzSUbaIa+qM2gy4d5pr++5/dPL3O/fgUeP8lySJKnD7KY79+wzMHu+1LZpAyDJE4ANU4xHkiRpoNTaDpo8KwOz53HAvyf5Vju+L/C1JF+hqXY9ZnqhSZIkaZGYDMyeI6YdgCRJ0lA8mj/3TAZmTFVd1p7V5wBg557p/za9qCRJkgYwGZh7JgMzJsmvAq+iuT7AecATgc8Dz5hmXJIkSVo8diCePa8CHg9cVlVPp7no1w3TDUmSJGlra9152A7E02EyMHtuW7rAWJJ7VtVXgQOnHJMkSZIWkM2EZs+mJLsAHwM+neR6mivpSZIkzZbKtCPQNjIZmDFV9XPtzT9K8lng/sA/TTEkSZKkwWzaM/dMBmZMklcPmPzLSc6tqvMmHpAkSZIWlsnA7Dm0HT7Rjj8HOB/49SQfrKr/NbXIJEmSetjpd/6ZDMyevYHHVtXNAEneAPwD8BTgXMBkQJIkSWvCZGD2PAj4Qc/47cAeVfX9JD9Y5j6SJEmTZ2Vg7pkMzJ73AWcn+Xg7/lzg/UnuDVw0vbAkSZJ6eG2AhWAyMGOq6k+S/CPwpHbSr1fVhvb2L04pLEmSJC0gk4EZ1O78b1h1QUmSpGmyMjD3vAKxJEmS1FFWBiRJkjQeKwNzz8rAjEhyavv/VdOORZIkaRiptR00eSYDs+NxSR4M/EqSByTZtXeYdnCSJElaPDYTmh3vBM4EHkJzcbH0zKt2uiRJkrRmrAzMiKp6e1U9Anh3VT2kqvbvGUwEJEmStOasDMyYqvqNJAcBP9VO+reqOn+aMUmSJA1kO/+5Z2VgxiT5bZqrED+oHd6X5LemG5UkSVKfNe48bAfi6bAyMHt+FXhCVd0CkOQtwOeBv5pqVJIkSVo4JgOzJ8DmnvHNbN2ZWJIkaTZ4NH/umQzMnvcAZyf5aDt+FHDSFOORJEnSgjIZmDFV9RdJzgKe3E56eVV9eYohSZIkDWZlYO6ZDMygqvoS8KVpxyFJkrScYKffReDZhCRJkqSOsjIgSZKk8VgZmHtWBmZMGr+U5PXt+L5JDpt2XJIkSVvxOgMLwWRg9vwN8JPAi9vxm4B3TC8cSZIkLSqbCc2eJ1TVY5N8GaCqrk+y07SDkiRJuhuP5s89KwOz5/Yk62i/XkkeCGyZbkiSJElaRCYDs+ftwEeBByX5U+BzwJ9NNyRJkqQBao0HTZzNhGZMVb0vybnA4TSn8D2qqi6ecliSJEl3Y6ff+WcyMIOq6qvAV6cdhyRJkhabycCMWTqlaL+qeuOkY5EkSVqRlYG5ZzIwe27pub0z8BzAZkKSJElacyYDM6aq/k/veJI/Bz41pXAkSZIGs9PvQjAZmH33AvaedhCSJEn97EA8/0wGZkySr3BXnr0OeCBgfwFJkiStOZOB2fOcntt3AFdV1R3TCmY1m9nCzVtum3YYnXXz5ntOO4TOy+ZpR6B73O51GaWpmUJloL046wbgiqp6TpL9gdOA3YBzgV+uqh8muSdwCvA44FrgF6rq0slHPNu86NiMqarLeoYrZjkRkCRJmoJXsfXJVd4CvLWqHgpcDxzbTj8WuL6d/tZ2OfUxGZgRSW5K8r0Bw01Jvjft+CRJkvql1nZY9fmSvYGfBf6uHQ/wDOBD7SInA0e1t49sx2nnH94urx42E5oRVXXfaccgSZI0krVvJrR7kg094ydW1Yk9438J/B6wtN+0G3BDT0uKTcBe7e29gMsBquqOJDe2y1+z5lHPMZOBGZTkAcABNNcZAKCq/m16EUmSJE3ENVV16KAZSZ4DXF1V5yZ52mTDWlwmAzMmya/StIXbGzgPeCLweZoSmCRJ0myY/HUGngQ8L8mzaQ6Y3g94G7BLkh3a6sDewBXt8lcA+wCbkuwA3J+mI7F62Gdg9rwKeDxwWVU9HTgEuGG6IUmSJE1XVf1BVe1dVfsBRwOfqapfBD4LvKBd7Bjg4+3t9e047fzPVJVXRuhjMjB7bquq2wCS3LOqvgocOOWYJEmStpLtMIzp94FXJ9lI0yfgpHb6ScBu7fRXA8eP/xSLy2ZCs2dTkl2AjwGfTnI9cNmUY5IkSbq7KR1nr6qzgLPa25cAhw1Y5jbghRMNbA6ZDMyIJO8A3l9VP9dO+qMkn6Vp3/ZP04tMkiRJi8pkYHb8J/DnSfYETgf+vqr+dcoxSZIkLWuYawNottlnYEZU1duq6ieBp9L0dH93kq8meUOSh005PEmSJC0gk4EZU1WXVdVbquoQ4MU0V9G7eJW7SZIkTV6t8aCJMxmYMUl2SPLcJO8D/hH4GvD8KYclSZJ0dyYDc88+AzMiyTNpKgHPBr4InAYcV1W3TDUwSZIkLSyTgdnxB8D7gf9eVddPOxhJkqQVlR2IF4HJwIyoqmdMOwZJkiR1i8mAJEmSxmNlYO6ZDEiSJGksNhOaf55NSJIkSeooKwOSJEkaj5WBuWcyIEmSpLHYTGj+2UxIkiRJ6igrA5IkSRqdVw1eCFYGJEmSpI6yMiBJkqTxWBmYeyYDkiRJGlmwA/EisJmQJEmS1FFWBiRJkjQeKwNzz8qAJEmS1FFWBiRJkjSWlKWBeWcyIEmSpNF5nYGFYDMhSZIkqaOsDEiSJGksnlp0/lkZkCRJkjrKyoAkSZLGY2Vg7pkMSJIkaSw2E5p/NhOSJEmSOsrKgCRJksZjZWDuWRmQJEmSOsrKgCRJkkZX9hlYBCYDkiRJGo/JwNyzmZAkSZLUUVYGJEmSNLJgM6FFYGVAkiRJ6iiTgQ5JckSSryXZmOT4FZb7+SSV5NBJxidJkuZM1doOmjibCXVEknXAO4BnApuAc5Ksr6qL+pa7L/Aq4OzJRylJkuaJzYTmn5WB7jgM2FhVl1TVD4HTgCMHLPcnwFuA2yYZnCRJkibPZKA79gIu7xnf1E67U5LHAvtU1T+s9EBJjkuyIcmGa6/dsvaRSpKk2VfbYdDE2UxIACS5B/AXwMtWW7aqTgROBDjkoJ386kqS1FHxmODcszLQHVcA+/SM791OW3Jf4FHAWUkuBZ4IrLcTsSRJ0uKyMtAd5wAHJNmfJgk4GnjJ0syquhHYfWk8yVnAa6pqw4TjlCRJ88L2AXPPykBHVNUdwCuBTwEXA6dX1YVJ3pjkedONTpIkaXVJdk7yxST/keTCJH/cTt8/ydnt6dM/kGSndvo92/GN7fz9phn/LLIy0CFVdQZwRt+01y+z7NMmEZMkSZpfUzi16A+AZ1TVzUl2BD6X5B+BVwNvrarTkrwTOBY4of1/fVU9NMnRNGdM/IWJRz3DrAxIkiRpdMXELzpWjZvb0R3boYBnAB9qp58MHNXePrIdp51/eJKs0TuwEEwGJEmSNCt2Xzp9eTsc179AknVJzgOuBj4NfAO4oW0SDVufPv3OU6u3828EdtveL2Ke2ExIkiRJY9kOzYSuqaoVz2RYVZuBg5PsAnwUePiaR9EhVgYkSZI0d6rqBuCzwE8CuyRZOsjde/r0O0+t3s6/P3DthEOdaSYDkiRJGs+Er0Cc5IFtRYAkPwI8k+YsiZ8FXtAudgzw8fb2+nacdv5nqobonNAhNhOSJEnSyMJUzia0J3ByknU0B7VPr6pPJrkIOC3Jm4AvAye1y58EnJpkI3AdzXWW1MNkQJIkSXOhqs4HDhkw/RLgsAHTbwNeOIHQ5pbJgCRJkkY35OlANdvsMyBJkiR1lJUBSZIkjWUKfQa0xkwGJEmSNB6TgblnMyFJkiSpo6wMSJIkaSw2E5p/VgYkSZKkjrIyIEmSpNEVsMXSwLwzGZAkSdJ4zAXmns2EJEmSpI6yMiBJkqSx2IF4/lkZkCRJkjrKyoAkSZLGU5YG5p3JgCRJksZiM6H5ZzMhSZIkqaOsDEiSJGl0hacWXQAmA5IkSRpZgNhnYO7ZTEiSJEnqKCsDkiRJGs+WaQegbWVlQJIkSeooKwOSJEkai30G5p/JgCRJkkbn2YQWgs2EJEmSpI6yMiBJkqQxFNhMaO5ZGZAkSZI6ysqAJEmSxhILA3PPZECSJEnjsZnQ3LOZkCRJktRRVgYkSZI0uoJ4BeK5Z2VAkiRJ6igrA5IkSRqPfQbmnsmAJEmSxmMuMPdsJiRJkiR1lJUBSZIkjSU2E5p7VgYkSZKkjrIyoG1yW4WLbl837TA661vf33XaIXTeTjd4TGXqtnhkcqo8Mtxtfv5zz2RAkiRJoyvA6wzMPQ9pSZIkSR1lZUCSJEkjC2UH4gVgZUCSJEnqKCsDkiRJGo+VgblnMiBJkqTxmAzMPZsJSZIkSR1lMiBJkqTRLZ1adC2HVSTZJ8lnk1yU5MIkr2qn75rk00m+3v5/QDs9Sd6eZGOS85M8du3egMVgMiBJkqSxpGpNhyHcAfz3qnok8ETgFUkeCRwPnFlVBwBntuMAzwIOaIfjgBPW+j2YdyYDkiRJmgtVdWVVfam9fRNwMbAXcCRwcrvYycBR7e0jgVOq8QVglyR7TjjsmWYHYkmSJI1nih2Ik+wHHAKcDexRVVe2s74D7NHe3gu4vOdum9ppVyLAZECSJEmzY/ckG3rGT6yqE/sXSnIf4MPA71TV95LcOa+qKomnORqSyYAkSZLGUNujMnBNVR260gJJdqRJBN5XVR9pJ1+VZM+qurJtBnR1O/0KYJ+eu+/dTlPLPgOSJEkaXdEkA2s5rCJNCeAk4OKq+oueWeuBY9rbxwAf75n+0vasQk8EbuxpTiSsDEiSJGl+PAn4ZeArSc5rp/0h8Gbg9CTHApcBL2rnnQE8G9gI3Aq8fLLhzj6TAUmSJI1niGsDrKWq+hyQZWYfPmD5Al6xXYOaczYTkiRJkjrKyoAkSZLGMuSFwjTDTAYkSZI0HpOBuWczIUmSJKmjrAxIkiRpdAVssTIw76wMSJIkSR1lZUCSJElj2C5XINaEmQxIkiRpPCYDc89mQpIkSVJHWRmQJEnSeKwMzD0rA5IkSVJHWRmQJEnS6Dy16EIwGZAkSdIYCmrLtIPQNrKZkCRJktRRVgYkSZI0HjsQzz0rA5IkSVJHWRmQJEnS6OxAvBBMBiRJkjQemwnNPZsJSZIkSR1lZUCSJEnjsTIw96wMSJIkSR1lZUCSJEljKCsDC8BkQJIkSaMrYItXIJ53NhOSJEmSOsrKgCRJksZjM6G5ZzIgSZKk8ZgMzD2bCUmSJEkdZWVAkiRJYyjYYmVg3lkZ6JAkRyT5WpKNSY4fMP9lSb6b5Lx2+NVpxClJkqTJsDLQEUnWAe8AnglsAs5Jsr6qLupb9ANV9cqJByhJkuZLQZWnFp13JgPdcRiwsaouAUhyGnAk0J8MSJIkDcdmQnPPZkLdsRdwec/4pnZav59Pcn6SDyXZZzKhSZIkaRpMBtTrE8B+VfUY4NPAyYMWSnJckg1JNtxw3eaJBihJkmZI1doOmjiTge64Aug90r93O+1OVXVtVf2gHf074HGDHqiqTqyqQ6vq0F12XbddgpUkSdL2Z5+B7jgHOCDJ/jRJwNHAS3oXSLJnVV3Zjj4PuHiyIUqSpLlRBVvsQDzvTAY6oqruSPJK4FPAOuDdVXVhkjcCG6pqPfDbSZ4H3AFcB7xsagFLkqTZZ9OeuWcy0CFVdQZwRt+01/fc/gPgDyYdlyRJkqbDZEC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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.63 s, sys: 1.23 s, total: 3.86 s\n", "Wall time: 9.77 s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(false_alarm, \"False alarm probability\",\n", " Monitored,\n", " values_Delta=[0.05, 0.1, 0.25, 0.4, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=50,\n", " savefig=\"False_alarm_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus\"\n", " )" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 5 values for Delta, and 5 values for tau, and 50 repetitions.\n" ] }, { "data": { 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'Missed_detection_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus.png' created of size '24349b', at 'Sun Dec 16 22:47:50 2018' ...\n", "Saving figure with format pdf, to file 'Missed_detection_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus.pdf'...\n", " Saved! 'Missed_detection_of_Monitored__Bernoulli_T1000_N50__5deltas__5taus.pdf' created of size '24377b', at 'Sun Dec 16 22:47:50 2018' ...\n" ] }, { "data": { "text/plain": [ "
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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.42 s, sys: 1.1 s, total: 3.52 s\n", "Wall time: 8.94 s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(false_alarm, \"False alarm probability\",\n", " Monitored,\n", " values_Delta=[0.05, 0.1, 0.25, 0.4, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=50,\n", " gaussian=True,\n", " savefig=\"False_alarm_of_Monitored__Gaussian_T1000_N50__5deltas__5taus\"\n", " )" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 5 values for Delta, and 5 values for tau, and 50 repetitions.\n" ] }, { "data": { 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'Missed_detection_of_Monitored__Gaussian_T1000_N50__5deltas__5taus.png' created of size '23787b', at 'Sun Dec 16 22:51:03 2018' ...\n", "Saving figure with format pdf, to file 'Missed_detection_of_Monitored__Gaussian_T1000_N50__5deltas__5taus.pdf'...\n", " Saved! 'Missed_detection_of_Monitored__Gaussian_T1000_N50__5deltas__5taus.pdf' created of size '24291b', at 'Sun Dec 16 22:51:04 2018' ...\n" ] }, { "data": { "text/plain": [ "
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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.77 s, sys: 813 ms, total: 3.58 s\n", "Wall time: 14 s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(missed_detection, \"Missed detection probability\",\n", " Monitored,\n", " values_Delta=[0.05, 0.1, 0.25, 0.4, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=50,\n", " gaussian=True,\n", " savefig=\"Missed_detection_of_Monitored__Gaussian_T1000_N50__5deltas__5taus\"\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### For `CUSUM`" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm CUSUM($\\varepsilon=0.5$, $M=50$, $h=$'auto') mu^1 = 0.5 and horizon = 1000...\n", "with 5 values for Delta, 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7P7bgso50Wy/yeg96ZyYf1ufW5IbHs3Povnqksczbp0bV3X+XSXviC6rqGVV11yHmp1TVL0zN+sYkLx7e7zvU5KbKpyV5c1V9XVX9RFWdmEw6NMmkLP6uRZ4/I7R556CLk3zz1PDDMzmJfnxY/8m1TocU3f2U/nJvRWsfT1kz7y2ZJGfnD8f8N2bSq99hFc2qumdVfdtwXjiuqr4nyTcl+Z9T87y61umudXB6hgvf7r4pkzb65w7TDja92/Jr2SjGRV7rBvE/Msn711y4n551konNxj1lkePz8iTPHs7fZ+XQfWTtMbWpY7QmNwJ/Q5I/HIbnbY+1xvocWHdfmLaF/eK44bXtSrLr4P4xLGvufjFv+gj71HSMNZxj75UvV1IX+YzfjI3eo3n71lZsdL2z0f6+dn/ezOfXZubdbNxrPTLJ/arqa6rqH2Ryn+UDM7mXZEOb3Z/HPm+vsWPb7UiOywUscuzMurbb6LkbXbtsy/Vyzc875tp0gtTdH+ruPRvMduckP5/JzW3/O8n9krxozvh09/5MqgKnZfIN8Q1Jfi3JV3b3FzO52f05SW7M5Aa4mRWsefPPW8/UIv5dJhdsN2Vy49zM+YflPS3J12byTfi+YX2HLKeqXrhOjE/L5Mb1GzLpOvn7hm/Yt+r1Sf4gk2+bP5TJTXzrWnD96y6vu69O8h8yObA/nkk74j/baiwzzNp2rxnWt+gBf8TbesHXO72uZ2ZyEX9TJk0BfzdDt9YjvO8z96nt0N3/IZPmXC/O5OR/XSYX6G+dmu38JH+e5H9l0sTtF5J8T0/u9bk5yeOSvLuqbskkMboyyU8s+Pz1Ypp3DnptkqcOH7hJ8huZdJJx3XA8vyWTe8SO1L/K5KbWTyR5Q5If6u7bvqWrybebP51JU8Gfy5dv9v2RTJoH/dXUsk7KjP0pay58u/vS7r5hGFy3ErMFG8U497VuEP8jMpUM1eR3rP5hJvvAKBY8Pp+fyXF3UyZd307vv4ccU1s4Rp+W5NLuPvhN6rztsTb2sT4HDjZNGtOLM7mYOC+T89jnhnEHbbRfzJt+JPtUMvR2lcn9Fi9L8v0Hn7/gZ/zCFniP5u1bW13fzOudBfb3tfvzZj6/Fp53s3Gv4xFJLsmkM4S9mXxW7Mvkonu7zNzvps7ZyWLn7dvs8HY70uNyXiyLHDuzrgs3eu5G1y7bdb08M+/YSPXsijwrpKo+kknPSH+07Fi2U1WdnMk9Jf+wJx0AHPWq6t1JfqW7f2PZsRwLqurfJvlEd//nDWdespq0w//LJI/s7vXuuTqqrXr8YxiO7x/o7ittjyNnGx4batIM7te6+7fnzPPqJL8468sydtaxcp15kB+GYmXUpP3pC5JcdDQnR1X1zZl0R31DJt8oPjKzS/OMrLt/euO5jg7DN2MPXXYcW7Xq8Y+hux839fcxvz2OlG14zHhE5nQwU1UXZ1KNeEhVvbK7X71TgUEiQWJF1OQH0D6eyY37Zy05nI08JMmbMmmXfG2S7+pJN+UAcEyryX2g98ukK+x19aEdA8CO08QOAABgsJVe7AAAAG6XJEgAAAADCRIAAMBAggQAADCQIAEAAAwkSAAAAAMJEgAAwECCBAAAMJAgAQAADCRIAAAAAwkSAADAQIIEAAAwkCABAAAMJEgAAAADCRIAAMBAggQAADCQIAEAAAwkSAAAAAMJEgAAwECCBAAAMJAgAQAADCRIAAAAAwkSAADAQIIEAAAwkCABAAAMJEgAAACD45YdAIzl+Hvv6geddMdlhwEAx6SPXPel3HDj/lp2HHCkJEjcbjzopDvmPZectOwwAOCY9Nhvu27ZIcAoNLEDAAAYSJAAAAAGEiQAAICBBAkAAGAgQQIAABhIkAAAAAYSJAAAgIEECQAAYCBBAgAAGEiQAAAABhIkAACAgQQJAABgIEECAAAYSJAAAAAGEiQAAICBBAkAAGAgQQIAABhIkAAAAAYSJAAAgIEECQAAYCBBAgAAGEiQAAAABhIkAACAgQQJAABgIEECAIBNqKpXVdUnqurKGdOrql5RVXur6oqqevROx8jWSZAAAGBzXp3krDnTn5Lk1OHxvCT/bQdiYiQSJAAA2ITufkeSG+fMcnaS1/bEu5Lcs6ruvzPRcaQkSAAAMK4Tklw3NbxvGMcKOG7ZAQAAwFZ925Pu1p+6cf+oy3zvFV+4Ksnnp0Zd2N0XjroSjloSJAAAVtanbtyf91xy8qjL3HX/v/58d59xBIu4PslJU8MnDuNYAZrYAQCwsjrJgZH/jWB3ku8berN7fJK/6+6/HWPBbD8VJAAA2ISqekOSJyY5vqr2JfnZJHdMku7+lSQXJ3lqkr1JPpvkny8nUrZCggQAwArr7O9Rqj6Lr7H7WRtM7yQ/vEPhMDIJEgAAK2vSxK6XHQa3I+5BAgAAGKggAQCw0kbqWAGSqCABAADcRgUJAICV1ensb/cgMR4JEgAAK00nDYxJEzsAAICBChIAACurk+xXQWJEKkgAAAADFSQAAFaae5AYkwoS26aqzqqqa6pqb1Wdt870O1fVG4fp766qBw3jH1RVn6uqy4fHr+x07ADAaugk+7tHfXBsU0FiW1TVriQXJDkzyb4kl1XV7u6+emq2H0jy6e7+2qo6J8nLk/zTYdqHuvu0HQ0aAIBjngoS2+WxSfZ297Xd/cUkFyU5e808Zyd5zfD3m5N8S1XVDsYIANwOHBj5wbFNgsR2OSHJdVPD+4Zx687T3bcm+bsk9xmmnVJV76uqP62qfzxrJVX1vKraU1V7Pvmp/eNFDwCshE5n/8gPjm0SJI5Gf5vk5O4+PckLkry+qu6x3ozdfWF3n9HdZ9z3Prt2NEgAAG5/3IPEdrk+yUlTwycO49abZ19VHZfkK5N8qrs7yReSpLvfW1UfSvLgJHu2PWoAYLV0sl/RhxGpILFdLktyalWdUlV3SnJOkt1r5tmd5PuHv78ryZ90d1fVfYdOHlJVX53k1CTX7lDcAAAcw1SQ2BbdfWtVnZvkkiS7kryqu6+qqvOT7Onu3Ul+PcnrqmpvkhszSaKS5JuSnF9VX8rkXskf7O4bd/5VAABHu46OFRiXBIlt090XJ7l4zbiXTP39+STfvc7zfjvJb297gADA7UBlf3SCy3g0sQMAABioIAEAsLI6yQGdNDAiFSQAAICBChIAACvNPUiMSYIEAMDK6kiQGJcmdgAAAAMVJAAAVtqBVkFiPCpIAAAAAxUkAABWlnuQGJsECQCAldWp7NcoihHZmwAAAAYqSAAArDSdNDAmFSQAAICBChIAACtLJw2MTYIEAMAKq+xvjaIYj70JAABgoIIEAMDK6iQHfOfPiOxNAAAAAxUkAABWmk4aGJMECQCAldWtkwbGZW8CAAAYqCABALDSDmhix4gkSAAArKzJD8VqFMV47E0AAAADFSQAAFaYThoYl70JAABgoIIEAMDK6iQHfOfPiCRIAACstP2tFzvGI90GAAAYqCABALCyOqWbb0ZlbwIAABioIAEAsNIO6OabEUmQAABYWZ1oYseo7E0AAAADFSQAAFZWp3TzzahUkAAAAAYqSAAArLQDvvNnRBIkAABWVneyXy92jMjeBAAAMFBBAgBghVUORCcNjEcFCQAAYKCCBADAyuq4B4lxSZAAAFhp+zWKYkT2JgAAgIEKEgAAK6tTOdA6aWA8KkgAAAADFSQAAFaae5AYkwQJAICV1UkO6MWOEdmbAAAABipIAACssMr+6KSB8UiQAABYWZrYMTZ7EwAAwEAFCQCAlaaJHWNSQQIAABioIAEAsLK6yz1IjEqCBADAStsvQWJE9iYAAICBBAkAgJXVSQ6kRn0soqrOqqprqmpvVZ23zvQHVtUfV9UVVXVpVZ049mtne0iQAABgE6pqV5ILkjwlycOSPKuqHrZmtl9M8trufmSS85P8u52Nkq1yDxIAACuslnEP0mOT7O3ua5Okqi5KcnaSq6fmeViSFwx/vz3JW3c0QrZMBQkAgJXVSQ50jfpIcnxV7Zl6PG/Nak9Ict3U8L5h3LS/TPLM4e/vSHL3qrrPNmwCRqaCBAAAh7qhu884wmW8MMkvV9VzkrwjyfVJ9h9pYGw/CRIAACtt/843iro+yUlTwycO427T3X+ToYJUVV+R5Du7+6Ydi5At08QOAAA257Ikp1bVKVV1pyTnJNk9PUNVHV9VB6+1X5TkVTscI1skQQIAYGV1xr3/aLgHaf46u29Ncm6SS5J8IMmbuvuqqjq/qp4+zPbEJNdU1V8l+aokL9ueLcDYNLEDAGClHVjCd/7dfXGSi9eMe8nU329O8uadjosjp4IEAAAwUEECAGBldSf7F2gWB4tSQQIAABioIAEAsNIW6VgBFiVBAgBgZU16sdMoivHYmwAAAAYqSAAArLT90cSO8aggAQAADFSQAABYWR2dNDAuCRIAACtMJw2My94EAAAwUEECAGClHdBJAyOSIAEAsLK6k/3uQWJEmtgBAAAMVJAAAFhpOmlgTPYmAACAgQoSAAArq1N+B4lRSZAAAFhperFjTJrYAQAADFSQAABYWZ1oYseoVJAAAAAGEiSWoqrOqqprqmpvVZ23zvRvqqq/qKpbq+q7lhEjALAaDvQdRn1wbNPEjh1XVbuSXJDkzCT7klxWVbu7++qp2T6W5DlJXrjzEQIAK6P1Yse4JEgsw2OT7O3ua5Okqi5KcnaS2xKk7v7IMO3AMgIEAODYJEFiGU5Ict3U8L4kj9vKgqrqeUmelyQnn2B3BoBjTUc334xLI0tWWndf2N1ndPcZ973PrmWHAwDAivOVO8twfZKTpoZPHMYBAGyae5AYkwSJZbgsyalVdUomidE5SZ693JAAgFXkd5AYmyZ27LjuvjXJuUkuSfKBJG/q7quq6vyqenqSVNVjqmpfku9O8sqqump5EQMAcKxQQWIpuvviJBevGfeSqb8vy6TpHQDAXCpIjEkFCQAAYKCCBADAyur4oVjGJUECAGCl+R0kxqSJHQAAwEAFCQCA1dU6aWBcKkgAAAADFSQAAFaWH4plbBIkAABWmgSJMWliBwAAMFBBAgBgZfkdJMYmQQIAYKW1BIkRaWIHAAAwUEECAGClHYgKEuNRQQIAABioIAEAsLK6dfPNuCRIAACsNJ00MCZN7AAAAAYqSAAArDC/g8S4VJAAAAAGKkgAAKw09yAxJgkSAAArq6MXO8aliR0AAMBABQkAgNXVk99CgrGoIAEAAAxUkAAAWGkH4h4kxiNBAgBgZXX0Yse4NLEDAAAYqCABALDCSjffjEoFCQAAYKCCxGGq6uQFZ72pu/9+W4MBANiAbr4ZkwSJ9bwmk3se59WrO8mrk7x2JwICAJhFJw2MSYLEYbr7ScuOAQAAlkGCBADAyupWQWJcOmlg06rqp5YdAwAAbAcVJDZUVW+aHkxyWpKXLykcAIBD6OabMUmQWMTfd/dzDw5U1X9bZjAAANP0YseYNLFjES9bM/wzS4kCAAC2mQoSM1XV+5NckeSKqroiyfuTfH93r02YAACWRicNjEkFiXm+OcmvJvlcknOSXJnkqUuNCAAAtpEKEjN1941JLh0eqapTk7x4iSEBAByiUypIjEoFiZmq6sHTw93910keuaRwAADW1SM/OLapIDHPK6vqa5Jcn8m9SHdJcmVV3bW7P7vc0AAAYHwSJGbq7iclSVWdnORRmfz+0aOSXF5VB7r765YZHwBAWicNjEuCxIa6+2NJPpbkbQfHVdVXLC8iAIAp2sUxIvcgsSXd/ZllxwAAAGOTIAEAsNK6a9THIqrqrKq6pqr2VtV560w/uareXlXvq6orqspPpawICRIbqqqnLTsGAICjRVXtSnJBkqckeViSZ1XVw9bM9uIkb+ru0zP5Pcn/urNRslUSJBbxsmUHAAAwS/e4jwU8Nsne7r62u7+Y5KIkZ68NK8k9hr+/MsnfjPV62V46aWARuoYBAI5KnaX0YndCkuumhvcledyaeV6a5A+q6keS3C3Jt+5MaBwpFSQWoW8YAOBYcnxV7Zl6PG8Ly3hWkld394lJnprkdVXl2nsFqCABALC6Osn4FaQbuvuMOdOvT3LS1PCJw7hpP5DkrCTp7ndW1V2SHJ/kE2MGyvhksQAAsDnokzaQAAAeDElEQVSXJTm1qk6pqjtl0gnD7jXzfCzJtyRJVT00yV2SfHJHo2RLVJBYxMeXHQAAwCwLdqww4vr61qo6N8klSXYleVV3X1VV5yfZ0927k/xEkl+tqh/PpM71nO6djpStkCCxoe4+c9kxAADMtIS0o7svTnLxmnEvmfr76iTfuNNxceQ0sQMAABioIAEAsMJqGd18czumgsSGqurMqvrVqjptGN5KV5cAAHDUU0FiEf8iyQ8leXFV3TvJaUuOBwDgy3R9wIgkSCzi5u6+KckLq+rnkzxm2QEBACRJOprYMSpN7FjE7x38o7vPS/LaJcYCAADbRgWJDXX3/1gz/EvLigUA4DCa2DEiCRIzVdX7k1wx9Xh/ku/v7pctNTAAANgmmtgxzzcn+dUkn0tyTpIrkzx1qREBABymRn5wLFNBYqbuvjHJpcMjVXVqkhcvMSQAgMNpYseIVJCYqaoePD3c3X+d5JFLCgcAALadChLzvLKqvibJ9Zncg3SXJFdW1V27+7PLDQ0AYKCCxIgkSMzU3U9Kkqo6OcmjMvmB2EclubyqDnT31y0zPgAAGJsEicMMCdFafzk8ksndi3evqnt099/vXGQAAGt0Ej8Uy4gkSKznNZmcbtaebdYWsF8dPxoLACxZa2LHiCRIHOZg0zoAADjWSJAAAFhtKkiMSIIEAMBqcw8SI/I7SMxVVU+e/h8AAG7PJEhs5BfX/A8AcFSpHvfBsU2CxKLUrgEAuN1zDxIAAKuro5MGRiVBAgBghZVOGhiVJnYAAAADFSQ28pnh/5uXGgUAwCya2DEiFSTm6u5vmv4fAABuz1SQAABYbSpIjEiCBADAapMgMSJN7FhYVT2hqi5YdhwAALBdVJCYq6pOT/LsJP8kyf9O8nVJfnipQQEAHNTRzTejkiBxmKp6cJJnZZIY3Zzkt5I8sbs/XFUfXmpwAACwjSRIrOeDSS5L8l3d/f4107TyBQCOKuXqhBG5B4n1PDPJh5P8QVW9rqqeVlV3XHZQAADr6pEfHNMkSBymu9/a3eck+dokv5/keUn2VdVvJLnHosupqrOq6pqq2ltV560z/QVVdXVVXVFVf1xVD5yatr+qLh8eu0d4WQAAsCFN7Jipu29J8vokr6+qeyX57iQPnP+siaraleSCJGcm2Zfksqra3d1XT832viRndPdnq+qHkvxCkn86TPtcd5820ksBAICFqCCxkO7+dHdf2N1PXvApj02yt7uv7e4vJrkoydlrlvn27v7sMPiuJCeOFzEAAGyeBIntckKS66aG9w3jZvmBTJrzHXSXqtpTVe+qqmdsR4AAwO1D9bgPjm2a2LF0VfW9Sc5I8s1Tox/Y3ddX1Vcn+ZOqen93f2id5z4vk3ukcvIJdmcAOCb5HSRGpILEdrk+yUlTwycO4w5RVd+a5GeSPL27v3BwfHdfP/x/bZJLk5y+3kqGZn9ndPcZ973PrvGiBwDgmCRBYqaqusvQ09xbquq3q+rHq+ouCz79siSnVtUpVXWnJOckOaQ3uqo6PckrM0mOPjE1/l5Vdefh7+OTfGOS6c4dAAAmxu7iWxO7Y542Sczz2iQ3J/mlYfjZSV6XSW92c3X3rVV1bpJLkuxK8qruvqqqzk+yp7t3J/n3Sb4iyW9VVZJ8rLufnuShSV5ZVQcySeJ/fk3vdwAAsC0kSMzz8O5+2NTw26tq4USluy9OcvGacS+Z+vtbZzzvz5M8YpOxAgDHKlUfRqSJHfP8RVU9/uBAVT0uyZ4lxgMAcBi92DEmFSTm+YYkf15VHxuGT05yTVW9P0l39yOXFxoAAIxPgsQ8Zy07AACADan6MCIJEjN190er6l5JTk1yl6nx71heVAAAa0iQGJEEiZmq6rlJnp/JbxhdnuTxSd6Z5MnLjAsAALaLThqY5/lJHpPko939pEx+rPWm5YYEAPBlY3fQoJMGJEjM8/nu/nySVNWdu/uDSR6y5JgAAGDbaGLHPPuq6p5J3prkD6vq00k+uuSYAAAO1bXsCLgdkSAxU3d/x/DnS6vq7Um+Msn/XGJIAACH0yyOEUmQmKmqXrDO6H9WVe/t7st3PCAAANhmEiTmOWN4vG0Y/vYkVyT5war6re7+haVFBgAw0LECY5IgMc+JSR7d3Z9Jkqr62SS/l+Sbkrw3iQQJAIDbFQkS89wvyRemhr+U5Ku6+3NV9YUZzwEA2FkqSIxIgsQ8v5nk3VX1P4bhpyV5fVXdLcnVywsLAGDgt4sYmQSJmbr731TV7yf5xmHUD3b3nuHv71lSWAAAsG0kSMw1JER7NpwRAGBZVJAY0R2WHQAAAMDRQgUJAIDVpoLEiFSQOExVvW74//nLjgUAYCPV4z44tkmQWM83VNUDkvyLqrpXVd17+rHs4AAAYLtoYsd6fiXJHyf56kx+ELampvUwHgAAbndUkDhMd7+iux+a5FXd/dXdfcrUQ3IEAMDtlgoSM3X3D1XVo5L842HUO7r7imXGBABwGPcNMSIVJGaqqh9N8ptJ7jc8frOqfmS5UQEATBm5gwadNKCCxDzPTfK47r4lSarq5UnemeSXlhoVAABsEwkS81SS/VPD+3Nohw0AAMun6sOIJEjM8xtJ3l1VvzMMPyPJry8xHgAA2FYSJGbq7v9YVZcmecIw6p939/uWGBIAwOFUkBiRBIm5uvsvkvzFsuMAAFhPRccKjEsvdgAAAAMVJAAAVpsKEiNSQWKmmvjeqnrJMHxyVT122XEBANzG7yAxMgkS8/zXJP8oybOG4ZuTXLC8cAAAYHtpYsc8j+vuR1fV+5Kkuz9dVXdadlAAAIdQ9WFEKkjM86Wq2pXhtFNV901yYLkhAQAsX1WdVVXXVNXeqjpvnen/qaouHx5/VVU3LSNONk8FiXlekeR3ktyvql6W5LuSvHi5IQEArLHDFaThC+QLkpyZZF+Sy6pqd3dffVtI3T8+Nf+PJDl9Z6NkqyRIzNTdv1lV703yLZn8zMAzuvsDSw4LAOAQS+hY4bFJ9nb3tUlSVRclOTvJ1TPmf1aSn92h2DhCEiTm6u4PJvngsuMAANhBx1fVnqnhC7v7wqnhE5JcNzW8L8nj1ltQVT0wySlJ/mT0KNkWEiRmOti991rdff5OxwIAMNP4FaQbuvuMkZZ1TpI3d/f+kZbHNtNJA/PcMvXYn+QpSR60zIAAAI4C1yc5aWr4xGHces5J8oZtj4jRqCAxU3f/h+nhqvrFJJcsKRwAgMN1ltHN92VJTq2qUzJJjM5J8uy1M1XV1yW5V5J37mx4HAkJEptx10y+IQEAOGrsdCcN3X1rVZ2byRfHu5K8qruvqqrzk+zp7t3DrOckuai7/VLTCpEgMVNVvT9f/k5mV5L7JnH/EQBwzOvui5NcvGbcS9YMv3QnY2IcEiTm+fapv29N8vHuvnVZwXB0O/XS5yw7hGPeXz/x1csOAZbqxZ94xLJDOKZdf+sNy1u5+gwjkiAxU3d/dNkxAADATpIgcZiqujnrfxdTSbq777HDIQEAzLSEH4rldkyCxGG6++7LjgEAYGESJEYkQWKuqrpXklOT3OXguO5+x/IiAgCA7SNBYqaqem6S52fStfflSR6fST/+T15mXAAAt1nO7yBxO3aHZQfAUe35SR6T5KPd/aQkpye5abkhAQDA9lFBYp7Pd/fnqypVdefu/mBVPWTZQQEAHFTDA8YiQWKefVV1zyRvTfKHVfXpJLr+BgCOLprYMSIJEoepqguSvL67v2MY9dKqenuSr0zyP5cXGQAAbC8JEuv5qyS/WFX3T/KmJG/o7j9dckwAAOvyO0iMSScNHKa7/0t3/6Mk35zkU0leVVUfrKqfraoHLzk8AADYNhIkZuruj3b3y7v79CTPSvKMJB9YclgAAIfqkR8c0yRIzFRVx1XV06rqN5P8fpJrkjxzyWEBABxKgsSI3IPEYarqzEwqRk9N8p4kFyV5XnffstTAAABgm0mQWM+Lkrw+yU9096eXHQwAwEytkwbGJUHiMN395GXHAAAAyyBBAgBgtakgMSIJEgAAK00TO8akFzsAAICBChIAAKtNBYkRSZAAAFhpmtgxJk3sAAAABipIAACsro4mdoxKBQkAAGCgggQAwGpTQWJEEiQAAFZWRScNjEsTOwAAgIEKEgAAq00FiRGpIAEAAAxUkAAAWGnVSkiMR4IEAMDq8jtIjEwTOwAAgIEKEgAAK00334xJBQkAAGCgggQAwGpTQWJEEiQAAFaaJnaMSRM7AACAgQoSAACrTQWJEakgAQAADFSQAABYXe0eJMYlQQIAYLVJkBiRJnYAAAADFSQAAFZWRRM7xqWCBAAAMJAgsRRVdVZVXVNVe6vqvDnzfWdVdVWdsZPxAQArpHvcB8c0TezYcVW1K8kFSc5Msi/JZVW1u7uvXjPf3ZM8P8m7dz5KAGBVaGLHmFSQWIbHJtnb3dd29xeTXJTk7HXm+zdJXp7k8zsZHAAAxy4JEstwQpLrpob3DeNuU1WPTnJSd//evAVV1fOqak9V7fnkp/aPHykAcHTrbXhwTNPEjqNOVd0hyX9M8pyN5u3uC5NcmCRnPOouTmkAcAyqA8uOgNsTFSSW4fokJ00NnziMO+juSR6e5NKq+kiSxyfZraMGAAC2mwoSy3BZklOr6pRMEqNzkjz74MTu/rskxx8crqpLk7ywu/fscJwAwCrQhoQRqSCx47r71iTnJrkkyQeSvKm7r6qq86vq6cuNDgCAY5kKEkvR3RcnuXjNuJfMmPeJOxETALCadPPNmCRIAACsro4fd2VUmtgBAAAMVJAAAFhpmtgxJhUkAACAgQoSAACrTQWJEUmQAABYWRVN7BiXJnYAAAADFSQAAFZXt26+GZUKEgAAwEAFCQCAleYeJMYkQQIAYLVJkBiRJnYAAAADFSQAAFaaJnaMSQUJAABgoIIEAMDq6iQHlJAYjwQJAIDVJj9iRJrYAQAADFSQAABYaTppYEwqSAAAAAMVJAAAVlsrITEeCRIAACtNEzvGpIkdAABsUlWdVVXXVNXeqjpvxjz/pKqurqqrqur1Ox0jW6OCBADA6urseDffVbUryQVJzkyyL8llVbW7u6+emufUJC9K8o3d/emqut/ORslWSZAAAFhZlaR2/h6kxybZ293XJklVXZTk7CRXT83zfye5oLs/nSTd/YmdDpKt0cQOAAAOdXxV7Zl6PG/N9BOSXDc1vG8YN+3BSR5cVX9WVe+qqrO2M2DGo4IEAMBqOzD6Em/o7jOOcBnHJTk1yROTnJjkHVX1iO6+6UiDY3upIAEAwOZcn+SkqeETh3HT9iXZ3d1f6u4PJ/mrTBImjnISJAAAVlp1j/pYwGVJTq2qU6rqTknOSbJ7zTxvzaR6lKo6PpMmd9eO96rZLprYAQCwupbQi11331pV5ya5JMmuJK/q7quq6vwke7p79zDt/6yqq5PsT/KT3f2pnY2UrZAgAQDAJnX3xUkuXjPuJVN/d5IXDA9WiAQJAIAV1snOd/PN7Zh7kAAAAAYqSAAArLRSQGJEEiQAAFabJnaMSBM7AACAgQoSAACrq5M6sOwguD1RQQIAABioIAEAsNrcg8SIJEgAAKw2+REj0sQOAABgoIIEAMBKK03sGJEKEgAAwEAFiduNqz5x3zz8v/yrZYdxzPrql//5skM45j38p+z/y/ZVl31h2SEc0/74v//6skM4pv3BcZ9b3spVkBiRBAkAgNXVSfwOEiPSxA4AAGCgggQAwMqqtE4aGJUKEgAAwEAFCQCA1aaCxIgkSAAArDYJEiPSxA4AAGCgggQAwOrSzTcjkyABALDS9GLHmDSxAwAAGKggAQCw2lSQGJEKEgAAwEAFCQCAFdYqSIxKggQAwOrqSJAYlSZ2AAAAAxUkAABWm99BYkQqSAAAAAMVJAAAVpofimVMEiQAAFabBIkRaWIHAAAwUEECAGB1dZIDKkiMRwUJAABgoIIEAMAKa/cgMSoJEgAAq02CxIg0sQMAABioIAEAsNpUkBiRChIAAMBABQkAgNWlm29GJkECAGCFddIHlh0EtyOa2AEAAAxUkAAAWG06aWBEKkgAAAADFSQAAFaXThoYmQQJAIDVpokdI9LEDgAAYKCCBADAalNBYkQqSAAAAAMVJAAAVlirIDEqCRIAAKurkxw4sOwouB3RxA4AAGCgggQAwGrTxI4RSZAAAFhtEiRGpIkdAADAQAUJAIAV1skBFSTGo4LEUlTVWVV1TVXtrarz1pn+nKr6ZFVdPjyeu4w4AQA4tqggseOqaleSC5KcmWRfksuqand3X71m1jd297k7HiAAsDo66dbNN+ORILEMj02yt7uvTZKquijJ2UnWJkgAABvTxI4RaWLHMpyQ5Lqp4X3DuLW+s6quqKo3V9VJOxMaAADHMgkSR6u3JXlQdz8yyR8mec16M1XV86pqT1XtufWzt+xogADAUaJ73AfHNAkSy3B9kumK0InDuNt096e6+wvD4K8l+Yb1FtTdF3b3Gd19xnF3vdu2BAsAwLHDPUgsw2VJTq2qUzJJjM5J8uzpGarq/t39t8Pg05N8YGdDBABWQndyQCcNjEeCxI7r7lur6twklyTZleRV3X1VVZ2fZE93707yo1X19CS3JrkxyXOWFjAAcHTTLI4RSZBYiu6+OMnFa8a9ZOrvFyV50U7HBQDAsU2CBADASmtN7BiRThoAAAAGKkgAAKwwXXMzLgkSAACrq5MckCAxHk3sAAAABipIAACsttZJA+NRQQIAABioIAEAsLI6SbsHiRFJkAAAWF3dmtgxKk3sAABgk6rqrKq6pqr2VtV560x/TlV9sqouHx7PXUacbJ4KEgAAK22nm9hV1a4kFyQ5M8m+JJdV1e7uvnrNrG/s7nN3NDiOmAoSAABszmOT7O3ua7v7i0kuSnL2kmNiJBIkAABWWx8Y97GxE5JcNzW8bxi31ndW1RVV9eaqOmmMl8r208QOAICVdXM+fckf9ZuPH3mxd6mqPVPDF3b3hZtcxtuSvKG7v1BV/zLJa5I8ebQI2TYSJAAAVlZ3n7WE1V6fZLoidOIw7jbd/ampwV9L8gs7EBcj0MQOAAA257Ikp1bVKVV1pyTnJNk9PUNV3X9q8OlJPrCD8XEEVJAAAGATuvvWqjo3ySVJdiV5VXdfVVXnJ9nT3buT/GhVPT3JrUluTPKcpQXMpkiQAABgk7r74iQXrxn3kqm/X5TkRTsdF0dOEzsAAICBBAkAAGAgQQIAABhIkAAAAAYSJAAAgIEECQAAYCBBAgAAGEiQAAAABhIkAACAgQQJAABgIEECAAAYSJAAAAAGEiQAAICBBAkAAGAgQQIAABhIkAAAAAYSJAAAgIEECQAAYCBBAgAAGEiQAAAABhIkAACAgQQJAABgIEECAAAYSJAAAAAGEiQAAIBBdfeyY4BRVNUnk3x02XEcgeOT3LDsII5x3oPlsv2Xy/ZfvlV/Dx7Y3fdddhBwpCRIcJSoqj3dfcay4ziWeQ+Wy/ZfLtt/+bwHcHTQxA4AAGAgQQIAABhIkODoceGyA8B7sGS2/3LZ/svnPYCjgHuQAAAABipIAAAAAwkS7JCqelVVfaKqrpwad++q+sOq+uvh/3sN46uqXlFVe6vqiqp69PIiv32oqpOq6u1VdXVVXVVVzx/Gv7Sqrq+qy4fHU6ee86LhPbimqr5tedHfPlTVR6rq/cN23jOMcwzskKp6yNR+fnlV/X1V/ZhjYOdU1fOr6srhHPRjwzjHABxlJEiwc16d5Kw1485L8sfd/f+3d68xdlVlHMaffyggN0WKEEAQQaIghgKxKXIRA2hQQoEYUxAhhogJNQSUD2pMJH7CK99AgxAgQg1yqxLEGoKKmAJpaWxLATGAQKAkiEhpgrR9/bDXyOEwMz2d6ZyZ1OeXrJy11z5r77drz8rsd846u4cA97ZtgFOBQ1q5ELh6SDFuyzYA36iqw4B5wMIkh7V9V1bVnFbuBmj7FgAfpbtuVyXZbjoC38Z8qo3zyKOMnQNDUlWPj/ycA0cD64E72m7nwBRLcjjwFWAucARwWpIP4RyQZhwTJGlIqupPwD/7mucDN7T6DcAZPe03VmcpsHuSfYYT6bapql6oquWt/hqwBthvnC7zgV9W1RtV9RTwJN2NjbYu58D0OAn4e1WN959rOwe2rkOBB6tqfVVtAP4InIVzQJpxTJCk6bV3Vb3Q6i8Ce7f6fsCzPe97jvFv5rUFkhwIHAk82Jq+1pawXDeyvAWvwVQoYEmSZUkubG3OgemxAFjUs+0cmHqrgOOTzE6yM/BZYH+cA9KMY4IkzRDVPVLSx0pOsSS7ArcBl1TVv+mWrRwMzAFeAH48jeFt646rqqPolg4tTHJC707nwHAk2QE4HfhVa3IODEFVrQG+DywB7gFWABv73uMckGYAEyRpeq0dWTLRXl9q7c/T/WVxxPtbmyYhyfZ0ydFNVXU7QFWtraqNVbUJuIa3lhB5Dbayqnq+vb5E992XuTgHpsOpwPKqWgvOgWGqqmur6uiqOgF4BXgC54A045ggSdPr18D5rX4+sLin/bz2FKN5wKs9SzA0AUkCXAusqaqf9LT3ruk/k24ZDHTXYEGSHZN8kO6L0g8NK95tTZJdkuw2Ugc+TTfWzoHhO5ue5XXOgeFJsld7PYDu+0c34xyQZpxZ0x2A9P8iySLgRGDPJM8B3wWuAG5JcgHwDPCF9va76danP0n3pKkvDz3gbc+xwJeAlUlWtLZvA2cnmUO3rOVp4KsAVbU6yS3Ao3RPwFtYVRvfcVQNam/gji5PZRZwc1Xdk+RhnAND05LTU2g/580PnANDc1uS2cCbdOP5ryT+HpBmmHTLXSVJkiRJLrGTJEmSpMYESZIkSZIaEyRJkiRJakyQJEmSJKkxQZIkSZK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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.75 s, sys: 986 ms, total: 3.74 s\n", "Wall time: 1min 26s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(missed_detection, \"Missed detection probability\",\n", " CUSUM,\n", " values_Delta=[0.05, 0.1, 0.25, 0.4, 0.5],\n", " values_tau=[1/10, 1/4, 2/4, 3/4, 9/10],\n", " firstMean=0.5,\n", " horizon=1000,\n", " repetitions=10,\n", " savefig=\"Missed_detection_of_CUSUM__Bernoulli_T1000_N10__5deltas__5taus\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second example" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values of delta: [0.15 0.16763158 0.18526316 0.20289474 0.22052632 0.23815789\n", " 0.25578947 0.27342105 0.29105263 0.30868421 0.32631579 0.34394737\n", " 0.36157895 0.37921053 0.39684211 0.41447368 0.43210526 0.44973684\n", " 0.46736842 0.485 ]\n" ] } ], "source": [ "firstMean = mu_1 = 0.5\n", "max_mu_2 = 1\n", "nb_values_Delta = 20\n", "min_delta = 0.15\n", "max_delta = max_mu_2 - mu_1\n", "epsilon = 0.03\n", "values_Delta = np.linspace(min_delta, (1 - epsilon) * max_delta, nb_values_Delta)\n", "print(f\"Values of delta: {values_Delta}\")" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values of tau: [ 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900\n", " 950]\n" ] } ], "source": [ "horizon = T = 1000\n", "min_tau = 50\n", "max_tau = T - min_tau\n", "step = 50\n", "values_tau = np.arange(min_tau, max_tau + 1, step)\n", "nb_values_tau = len(values_tau)\n", "print(f\"Values of tau: {values_tau}\")" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This will give a grid of 20 x 19 = 380 values of Delta and tau to explore.\n" ] } ], "source": [ "print(f\"This will give a grid of {nb_values_Delta} x {nb_values_tau} = {nb_values_Delta * nb_values_tau} values of Delta and tau to explore.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### For `Monitored`" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 20 values for Delta, and 19 values for tau, and 50 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 19.1 s, sys: 2.27 s, total: 21.4 s\n", "Wall time: 2min 49s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(missed_detection, \"Missed detection probability\",\n", " Monitored,\n", " values_Delta=values_Delta,\n", " values_tau=values_tau,\n", " firstMean=firstMean,\n", " horizon=horizon,\n", " repetitions=50,\n", " savefig=\"Missed_detection_of_CUSUM__Bernoulli_T1000_N50__20deltas__19taus\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### For `Monitored` for Gaussian data" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm Monitored($w=80$, $b=\\sqrt{\\frac{w}{2} \\log(2 T^2)}$) mu^1 = 0.5 and horizon = 1000...\n", "with 20 values for Delta, and 19 values for tau, and 50 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, style=ProgressStyle(descr…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Repetitions||', max=50, style=ProgressStyle(description_width…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 23.5 s, sys: 2.69 s, total: 26.2 s\n", "Wall time: 27min 56s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(detection_delay, \"Detection delay\",\n", " CUSUM,\n", " values_Delta=values_Delta,\n", " values_tau=values_tau,\n", " firstMean=firstMean,\n", " horizon=horizon,\n", " repetitions=10,\n", " savefig=\"Detection_delay_of_CUSUM__Bernoulli_T1000_N10__20deltas__19taus\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### For `PHT`" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm PHT($\\varepsilon=0.5$, $M=50$, $h=$'auto') mu^1 = 0.5 and horizon = 1000...\n", "with 20 values for Delta, and 19 values for tau, and 5 repetitions.\n" ] }, { "data": { 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TmO+585o0N9LfD3gz8Kl5zjtXnNFJbJnm3RIHp3ms7AaaX/U3zTHbgoLzL+buvqdvBt4LvBya1gOaVt/9aX7WvA74e2DHdt7/C7wlzc8Nb5gtfzvtOTQ3VV0BrKO5wYHpyuqtYFXd0c57WFvm3wG/V1XfX8B69pb1PJrHxd3Q1uGz81zf3nIuounX+k2anf9Ymp+nen20TZ/vQdyXdZ1n3Xq3xStoWhuPoulvdXuf6jLjPu3QF5PcQhMwvh14aVVduJB9Ox/z3Dava/PcRPP4qc9v0Rr98rLfBbye5tnWP2uHk2j6Mn6jJ+tqmgvG5PDnM5R3LfAxmptuaD8/s/1yg+b4uasFN81jAR9I04pzE3f/5LkjzTnVD29p63wszXF5W5sGTQvLLx3PrQ/Q3EB8Cc2F/bvAK/tQn0/Q9OG8geYcuRS4qD0Ovk4TiH8f+Lc2z7qquhHu+nL7AE03oHVt2hqa4/PrNNfCRwMHT7b40Rw3Z9bm3V5mW+/N9PGauVlf+T6Zbd/+Ac0fMtcA/wi8uqp6W+i2Zvp8tt+wXD/6fu2Y47tvPt8ZU7/v5/U9s4DyF1zvaTyW5ulTZ9L80nczzfn15vksawvNekymaYn/s3Z0W5obHSdvCP1Dmq5Uv/Q0qQFuM5j9nJxzHeeoy3zPnU/S/Ap7GfBDmptX5zPvXHFGV7HldjSPH7+OpuvLbmz+SOJpZYZfITVASfak+cJ+YFVNvaFiKCX5FvCBqvrwYtdFiyfJ/wGuqar3zZFvf+D1VfV77RfQj6rqHzus1zKaPxT2rao7u1rOYmvPw1dU1QXt+D1ivbvi9rtnSNPt5O+r6p/myPcR4D2T55cWV5LLaZ429W+LXZeu+dD3Rdb+vP164NRhDsyTHETTyngdTQvNvkz/E5vuQarqz+bOBVV1bprnfv8nzS9i75lrnq2s1x00rcxjraqeMGX8HrHeXXH73WM8ljneYpxkNU1L7KOSnFRVHxlExSQwOF9UaR7a/zOau34PXeTqzOVRwKdp+rddBjy/mkdBSvNSVX+y2HWQdM+W5uV0u9F0OZvRNDciSgNjtxZJkiRpSGztc84lSZIk9YnBuSRJkjQkDM4lSZKkIWFwLkmSJA0Jg3NJkiRpSBicS5IkSUPC4FySJEkaEgbnkiRJ0pAwOJckSZKGhMG5JEmSNCQMziVJkqQhYXAuSZIkDQmDc0mSJGlIGJxLkiRJQ8LgXJIkSRoSBueSJEnSkDA4lyRJkoaEwbkkSZI0JAzOJUmSpCFhcC5JkiQNCYNzSZIkaUgYnEuSJElDwuBckiRJGhIG55IkSdKQMDiXJEmShoTBuSRJkjQkli52BTS6drnfknroHtt2Vv7tNdFZ2U353R7+G6q7bTNp/cSyTsufqG7/ft+wsdt9sOnOrtsf0m3x1W3xANnUcfkbOy6/28tE59tnm4lud3JGvHwANna8kzd2d5DeVrdyR23o+EKhcWNwri320D225dtn7NFZ+T+685bOyga49M6duy3/jgd2Wj7Aml88tNPyb75zu07Lv+S63Tot/5af7dBp+dnY8Xdux4EhwLY3d/sHzLIbu91G2/282+Bw6W3dlr/dz7vdyctuurPT8pfeeFun5QNsc9PNnZa/6brrOyv7rA2rOytb48tuLZIkSdKQMDiXJEmShoTBuSRJkjQkDM4lSZKkIWFwLkmSJA0Jg3NJkiRpSBicj5Ekhya5JMnaJMdOM/0pSb6bZGOS50+ZNpHk3HZYNbhaS5IkaZLPOR8TSZYAJwJPB9YBZydZVVUX9WS7AngZ8IZpiritqvbvvKKSJEmakcH5+DgQWFtVlwEkORU4ArgrOK+qy9tpA3i1iSRJkhbKbi3jY3fgyp7xdW3afC1PsibJWUme29+qSZIkaT5sOdekh1TVVUkeBnw1yflV9cOpmZIcDRwNsOfuHj6SJEn9ZMv5+LgK2KNnfEWbNi9VdVX7/2XAmcABM+Q7uapWVtXKXe+/ZMtrK0mSpF9icD4+zgb2TrJXkmXAkcC8nrqSZOck27WfdwGeSE9fdUmSJA2GwfmYqKqNwDHAGcDFwKer6sIkxyc5HCDJ45OsA14AnJTkwnb2RwNrknwP+BrwjilPeZEkSdIA2Gl4jFTVamD1lLTjej6fTdPdZep83wAe23kFJUmSNCtbziVJkqQhYXAuSZIkDQmDc0mSJGlIGJxLkiRJQ8LgXJIkSRoSBueSJEnSkPBRitIIO/vqPTstf/2V9+m0/G1vSaflb7N9dVp+1/XfNIAr9JIN3a5Dut0F1Ig3MWVTxxuoY6nRrr80jEb8siZJkiSND4NzSZIkaUgYnEuSJElDwuBckiRJGhIG55IkSdKQMDiXJEmShoSPUpQkSRoDz3jqvev6Gyb6WuZ3zrv9jKo6tK+FalYG55IkSWPg+hsm+PYZ/X3/xZIHXbpLXwvUnOzWMoKSHJrkkiRrkxw7zfSnJPluko1Jnj9l2p5JvpTk4iQXJXlom/60dp5zk3w9ySMGszaSJKkfCtjU538aPIPzEZNkCXAicBiwD/CiJPtMyXYF8DLgk9MU8THg3VX1aOBA4Jo2/f3AS6pq/3a+t/S/9pIkSZqN3VpGz4HA2qq6DCDJqcARwEWTGarq8nbaZn/ytkH80qr6cpvvlp7JBdy3/bwjcHVH9ZckSZ0oJsrW7lFncD56dgeu7BlfBzxhnvM+ErgpyWeBvYB/A46tqgnglcDqJLcBvwB+vX9VliRJXWu6tdRiV0NbyW4t9yxLgScDbwAeDzyMpvsLwB8Dz6yqFcCHgfdOV0CSo5OsSbLm2uv7e0e4JEnSPZ3B+ei5CtijZ3xFmzYf64Bzq+qyqtoIfB54XJJdgf2q6lttvk8BvzldAVV1clWtrKqVu95/yZatgSRJ6oQ3hI4+g/PRczawd5K9kiwDjgRWLWDendpgHOBgmr7qNwI7Jnlkm/504OI+1lmSJEnzYJ/zEVNVG5McA5wBLAFOqaoLkxwPrKmqVUkeD3wO2Bl4TpK/qKpfraqJJG8AvpIkwHeAD7Zl/g/gn9qbSG8Efn9RVlCSJG2Ropgo+5yPOoPzEVRVq4HVU9KO6/l8Nk13l+nm/TKw7zTpn6MJ6CVJ0ojyhtDRZ7cWSZIkaUjYci5JkjQGCpiw5Xzk2XIuSZIkDQlbziVJksaEfc5Hn8G5JEnSGCjwaS1jwG4tkiRJ0pCw5VzqyGeuelzny9hnt592Wv531i/rtPylP92+0/Kr45fYbrxXty1Uy6/rvv1kmzu7LX/Zz7vdRvf+2USn5Xd9DHXdAyGbul1AJZ2WD9D9EsaL7/QcfQbnkiRJY6Aon9YyBuzWIkmSJA0JW84lSZLGQcGEDecjz5ZzSZIkaUjYci5JkjQGCm8IHQcG55IkSWMhTPh8m5FntxZJkiRpSNhyLkmSNAYK6PjR9hoAW84lSZKkIWFwPoKSHJrkkiRrkxw7zfSnJPluko1Jnj9l2ruSXJjk4iQnJM3r3ZKc2ZZ5bjvsNqj1kSRJ/THR9jvv16DBs1vLiEmyBDgReDqwDjg7yaqquqgn2xXAy4A3TJn3N4EnAvu2SV8HDgLObMdfUlVrOqu8JEnqTIEB9RgwOB89BwJrq+oygCSnAkcAdwXnVXV5O23qE5UKWA4sAwJsC/ys+ypLkiRpPuzWMnp2B67sGV/Xps2pqr4JfA34STucUVUX92T5cNul5X9PdneZKsnRSdYkWXPt9RNbtgaSJKkTmyp9HTR4Buf3IEkeATwaWEET0B+c5Mnt5JdU1WOBJ7fDf5+ujKo6uapWVtXKXe+/ZBDVliRJuscwOB89VwF79IyvaNPm478BZ1XVLVV1C3A68BsAVXVV+//NwCdpus9IkqQRMdnn3BtCR5vB+eg5G9g7yV5JlgFHAqvmOe8VwEFJlibZluZm0Ivb8V0A2vRnAxd0UHdJktSRIkywTV8HDZ5bfcRU1UbgGOAM4GLg01V1YZLjkxwOkOTxSdYBLwBOSnJhO/tngB8C5wPfA75XVV8EtgPOSHIecC5NS/wHB7lekiRJ8mktI6mqVgOrp6Qd1/P5bJruLlPnmwD+5zTptwK/1v+aSpKkQfImztFny7kkSZI0JGw5lyRJGgO+hGg8GJxLkiSNhTBRdooYde5BSZIkaUjYci5JkjQGCthku+vIMzjX0Lps446dln/ObQ/ttPwdl23otHyAs3+wV7cL2NDtRf623Sc6LX+3s7rte3nHfbotf/mNmzotH2DbW7tdxsbl3W6jrsvf/ro7Oy0/G6vT8tnUbfmpjusv3QMZnEuSJI0JbwgdfQbnkiRJY6DKG0LHgXtQkiRJGhK2nEuSJI2JTXZrGXkG55IkSWOgeQmRnSJGnXtQkiRJGhK2nEuSJI0FbwgdB+5BSZIkaUjYci5JkjQGfEPoeHAPjqAkhya5JMnaJMdOM/31SS5Kcl6SryR5SM+0lya5tB1e2pP+r0m+l+TCJB9IsmRQ6yNJkvpjotLXQYNncD5i2qD5ROAwYB/gRUn2mZLtHGBlVe0LfAZ4Vzvv/YC3Ak8ADgTemmTndp7frar9gMcAuwIv6HpdJEmStDmD89FzILC2qi6rqjuAU4EjejNU1deqan07ehawov38DODLVXVDVd0IfBk4tJ3nF22epcAyml/HJEnSiCjCBNv0ddDgudVHz+7AlT3j69q0mbwCOH0+8yY5A7gGuJmmxf2XJDk6yZoka669fmLhtZckSdKMDM7HWJKjgJXAu+eTv6qeATwI2A44eIY8J1fVyqpauev97ZYuSdIw2VTb9HXQ4LnVR89VwB494yvatM0kOQR4M3B4Vd0+33mragPwBaZ0lZEkScNt8g2hdmsZbW710XM2sHeSvZIsA44EVvVmSHIAcBJNYH5Nz6QzgN9OsnN7I+hvA2ck2SHJg9p5lwLPAr4/gHWRJElSD59zPmKqamOSY2gC7SXAKVV1YZLjgTVVtYqmG8sOwGlJAK6oqsOr6oYkf0kT4AMc36Y9AFiVZDuaP9i+BnxgwKsmSZK2QuHjD8eBwfkIqqrVwOopacf1fD5klnlPAU6ZkvYz4PF9rqYkSZIWyOBckiRpTPiG0NFncC5JkjQGqmDCJ6yMPPegJEmSNCRsOZckSRoLYRPeEDrqbDmXJEmShoTBuSRJ0hgomj7n/RzmI8kfJ7kwyQVJ/jHJ8vZ9LN9KsjbJp9p3s5Bku3Z8bTv9od1tkdFktxYNravu3LnT8k8658mdlr/dZcs7LR9gp+u6Lf+WParT8u9zeafFs/S2TZ2WP7Gs25+Pl9+wsdPyAW594Ladlr/Nnd0eQ4PYRl3Kxm6PUbbpuItDdbt/tXCDfqtnkt2B1wL7VNVtST5N84LEZwJ/VVWnJvkA8Arg/e3/N1bVI5IcCbwTeOFAKz3kbDmXJEnS1lgKbN++ZfxewE+Ag4HPtNM/Cjy3/XxEO047/Wlp35iohi3nkiRJY6AIm/r/htBdkqzpGT+5qk6+a5lVVyV5D3AFcBvwJeA7wE1VNfnT1jpg9/bz7sCV7bwbk/wcuD/Q8W/Bo8PgXJIkSTO5rqpWzjQxyc40reF7ATcBpwGHDqhuY8ngXJIkaUwMus85cAjwo6q6FiDJZ4EnAjslWdq2nq8ArmrzXwXsAaxru8HsCFw/6EoPM/ucS5IkjYECNtU2fR3m4Qrg15Pcq+07/jTgIuBrwPPbPC8FvtB+XtWO007/apV3FvcyOJckSdIWqapv0dzY+V3gfJrY8mTgjcDrk6yl6VP+oXaWDwH3b9NfDxw78EoPObu1SJIkjYUwsQhvCK2qtwJvnZJ8GXDgNHk3AC8YRL1GlcG5JEnSGJjs1qLR5h4cQUkOTXJJ+3atX/o5KMnrk1yU5LwkX0nykDZ9/yTfbN/idV6SF/bMc0xbXiXZZZDrI0mSpIbB+YhJsgQ4ETgM2Ad4UZJ9pmQ7B1hZVfvS9AN7V5u+Hvi9qvpVmsccvS/JTu20/6K54/rHHa+CJEnqyETbtaVfgwbP4Hz0HAisrarLquoO4FSa54vepaq+VlXr29GzaB5hRFX9oKoubT9fDVwD7NqOn1NVlw9mFSRJkjQd+5yPnrverNVaBzxhlvyvAE6fmpjkQGAZ8MOFLDzJ0cDRAHvu7uEjSdLE8/uLAAAgAElEQVSwqIp9zseA0dUYS3IUsBI4aEr6g4CPAy+tqk0LKbN9Ze/JACv3W+5zSSVJGiITBucjz+B89Ey+WWtS71u37pLkEODNwEFVdXtP+n2BfwHeXFVndVxXSZIkLYDB+eg5G9g7yV40QfmRwIt7MyQ5ADgJOLSqrulJXwZ8DvhYVX1mcFWWJEldK2CTN3GOPH/7GDFVtRE4BjgDuBj4dFVdmOT4JIe32d4N7ACcluTcJKva9N8FngK8rE0/N8n+AElem2QdTUv8eUn+fpDrJUmSJFvOR1JVrQZWT0k7rufzITPM9w/AP8ww7QTghD5WU5IkDVTscz4GDM4lSZLGQPOGULu1jDr/vJIkSZKGhC3nkiRJY2LCdteR5x6UJEmShoQt55IkSWOgiH3Ox4DBuSRJ0pjYZKeIkWdwrqG105L1nZa/5CfbdVr+9j+rTssHuNc1mzotPxPdXuTvf9GGTsvv2pLbt+20/Nqm+xaw+/64232wzW0bOy2/lox2ILLNHd1un2zs9hpBbKWV+s3gXJIkaQxUwYTdWkbeaDc5SJIkSWPElnNJkqQx4Q2ho8/gXJIkaQw0T2uxU8Socw9KkiRJQ8KWc0mSpDExgd1aRp0t55IkSdKQsOVckiRpDBTeEDoObDkfQUkOTXJJkrVJjp1m+uuTXJTkvCRfSfKQNv0hSb6b5NwkFyZ5Vc88L0pyfjvPvybZZZDrJEmStlZzQ2g/Bw2eW33EJFkCnAgcBuwDvCjJPlOynQOsrKp9gc8A72rTfwL8RlXtDzwBODbJg5MsBf4aeGo7z3nAMd2vjSRJknoZnI+eA4G1VXVZVd0BnAoc0Zuhqr5WVevb0bOAFW36HVV1e5u+HXfv/7TDvZMEuC9wdberIUmS+m0T6eugwbPP+ejZHbiyZ3wdTSv4TF4BnD45kmQP4F+ARwB/UlVXt+mvBs4HbgUuBV4zXWFJjgaOBthzdw8fSZKGRRVM2Od85NlyPsaSHAWsBN49mVZVV7ZdVx4BvDTJA5JsC7waOAB4ME23ljdNV2ZVnVxVK6tq5a73X9L5OkiSJN2T2PQ5eq4C9ugZX9GmbSbJIcCbgYN6urLcpaquTnIB8GTgx23aD9t5Pw380o2mkiRpuHkT5+hzD46es4G9k+yVZBlwJLCqN0OSA4CTgMOr6pqe9BVJtm8/7ww8CbiEJrjfJ8mubdanAxd3viaSJEnajC3nI6aqNiY5BjgDWAKcUlUXJjkeWFNVq2i6sewAnNbc38kVVXU48Gjg/yUpmhtA31NV5wMk+QvgP5LcSdOS/rIBr5okSdoKRXzO+RgwOB9BVbUaWD0l7biez4fMMN+XgX1nmPYB4AN9rKYkSRown7Ay+uzWIkmSJA0JW84lSZLGQIHdWsaALeeSJEnSkLDlXJIkaUz4KMXRZ3AuSZI0DsqntYwD/7ySJEmShoQt5xpar/vqSzotf9eOX7O0cXm35QP8/GFLOi1/px9OdFr+Nrd3W/7E8m4vcdv/dH2n5dfS7ttPMlGdlt91I97S62/pdgGbNnVafC3frtPyM9HtOZb1GzotH4Dq9hgdJ4WPUhwHtpxLkiRJQ8KWc0mSpDFhn/PRZ3AuSZI0BnzO+XiwW4skSZI0JGw5lyRJGhO2nI8+W84lSZKkIWHLuSRJ0hgofAnRODA4lyRJGhM+53z02a1lBCU5NMklSdYmOXaa6a9PclGS85J8JclDeqZNJDm3HVb1pCfJ25P8IMnFSV47qPWRJElSw5bzEZNkCXAi8HRgHXB2klVVdVFPtnOAlVW1PsmrgXcBL2yn3VZV+09T9MuAPYBfqapNSXbrbCUkSVL/lTeEjgNbzkfPgcDaqrqsqu4ATgWO6M1QVV+rqsn3ip8FrJhHua8Gjq+qTW0Z1/SxzpIkSZoHg/PRsztwZc/4ujZtJq8ATu8ZX55kTZKzkjy3J/3hwAvbaacn2Xu6wpIc3eZZc+31E1u6DpIkqc8mX0LUz0GDZ7eWMZbkKGAlcFBP8kOq6qokDwO+muT8qvohsB2woapWJnkecArw5KllVtXJwMkAK/dbXp2vhCRJmjcD6tFny/nouYqmb/ikFW3aZpIcArwZOLyqbp9Mr6qr2v8vA84EDmgnrQM+237+HLBvvysuSZKk2Rmcj56zgb2T7JVkGXAksKo3Q5IDgJNoAvNretJ3TrJd+3kX4InA5I2knwee2n4+CPhBp2shSZL6avI553ZrGW12axkxVbUxyTHAGcAS4JSqujDJ8cCaqloFvBvYATgtCcAVVXU48GjgpCSbaP4we0fPU17eAXwiyR8DtwCvHOiKSZKkrVYG1CPP4HwEVdVqYPWUtON6Ph8yw3zfAB47w7SbgGf1sZqSJElaIINzSZKkMeEbQkeffc4lSZKkIWHLuSRJ0hgo3xA6FgzOJUmSxoQ3hI4+u7VIkiRJQ8KWc0mSpLHgs8nHgcG5hlY2LOm0/Htds7HT8ie26/6HqSVXbuq0/O2vurXT8nPbHZ2Wv2RTt9uHbbu9hGb9hk7LB6il3Z5nXcud3Z7Htd2yTsvf5qabOy2fZdt2W/6m6rb8Aajqbh1Gf+toMRicS5IkjQn7nI8+g3NJkqQxUPi0lnHgDaGSJEnSkLDlXJIkaRxU86xzjTZbziVJkqQhYcu5JEnSmNiEfc5HncG5JEnSGCh8Wss4sFuLJEmSNCQMzkdQkkOTXJJkbZJjp5n++iQXJTkvyVeSPKRNf2qSc3uGDUme2077UJLvtfN8JskOg14vSZK0NZo3hPZz0OAZnI+YJEuAE4HDgH2AFyXZZ0q2c4CVVbUv8BngXQBV9bWq2r+q9gcOBtYDX2rn+eOq2q+d5wrgmO7XRpIkSb3scz4gSfacZ9abquoXs0w/EFhbVZe15Z4KHAFcNJmhqr7Wk/8s4Khpynk+cHpVrW/n+UVbXoDt8a3DkiSNHB+lOPoMzgfnozQB72y/ERXwEeBjs+TZHbiyZ3wd8IRZ8r8COH2a9COB9/YmJPkw8EyaQP9/TVdYkqOBowH23N3DR5KkYeINoaPP6GpAquqpg15mkqOAlcBBU9IfBDwWOKM3vape3nab+RvghcCHp5ZZVScDJwOs3G+5f59LkiT1kX3OR89VwB494yvatM0kOQR4M3B4Vd0+ZfLvAp+rqjunzldVE8CpwO/0rcaSJKlzVU3LeT8HDZ7B+ZBI8sZ5Zj0b2DvJXkmW0XRPWTWlrAOAk2gC82umKeNFwD/25E+SR0x+Bg4Hvr/wtZAkSdLWsFvLIkny6d5RYH/gnXPNV1UbkxxD0yVlCXBKVV2Y5HhgTVWtAt4N7ACc1sTaXFFVh7fLfShNy/u/T1n+R5Pct/38PeDVW7WCkiRp4Hz84egzOF88v6iqV06OJHn/fGesqtXA6ilpx/V8PmSWeS+nuam0N20T8MT5Ll+SJA0nn9Yy+uzWsnjePmX8zYtSC0mSpK2QZKf2BYbfT3Jxkt9Icr8kX05yafv/zm3eJDmhfZHieUket9j1HzYG5wOW5PwknwB+N8lhSVYkeXNV3bDYdZMkSaNtkW4I/WvgX6vqV4D9gIuBY4GvVNXewFfacWheorh3OxwNzLvnwD2FwfngHQR8ELiN5mbOC2ieLS5JkjRSkuwIPAX4EEBV3VFVN9G8IPGjbbaPAs9tPx8BfKwaZwE7tY94Vss+5wPWtpCf2Q4k2Rt4yyJWSZIkjYFiUR5/uBdwLfDhJPsB3wFeBzygqn7S5vkp8ID283QvU9wd+AkCbDkfuCSP7B2vqkuBfRepOpIkaYxUnwdglyRreoajpyxyKfA44P1VdQBwK3d3YWnqVNVTnOZiy/ngnZTk4TQvDjoPWA5ckOReVbV+casmSZK0meuqauUs09cB66rqW+34Z2iC858leVBV/aTttjL53pV5vUzxnszgfMCq6qkASfakuWli//b/c5Nsam+mGAlXb1zOX1y7T2fl7/mlic7KBrhx7207Lf8B37610/IBtl13faflTzxw507Lz22dFk823NFp+dXxM8vqXss7LR8gN/6i0/Jrh3t1Wn7Xsn5DtwuY6PY6x20dl7+0+zCiNm7stPz2fSDdlN1ZyTNo3xA60EVW/TTJlUkeVVWXAE8DLmqHlwLvaP//QjvLKuCYJKcCTwB+3tP9RRicL5qqugK4AvjiZFqSHRavRpIkaeQtTueRPwQ+0b65/DLg5TRdpz+d5BXAj4HfbfOupnkQxlpgfZtXPQzOh0hV3bLYdZAkSVqIqjoXmK7ry9OmyVvAazqv1AgzOJckSRoTi/C0FvWZT2tZJEmes9h1kCRJ0nAxOF88b1/sCkiSpPFS1d9Bg2e3lsXj706SJKlvCru1jANbzhePf49KkiRpM7acS5IkjYMCbDkfebacj6Akhya5JMnaJMdOM/31SS5Kcl6SryR5SM+0dya5oB1e2JN+TFteJdllUOsiSZKkuxmcL56fbclMSZYAJwKHAfsAL0oy9TWd5wArq2pfmtfovqud91nA42jeSvoE4A1J7tvO81/AITQvCpAkSSPIG0JHn8H5Iqmqp2/hrAcCa6vqsqq6AzgVOGJK2V+rqvXt6FnAivbzPsB/VNXGqroVOA84tJ3nnKq6fAvrJEmShkH1edDAGZyPnt2BK3vG17VpM3kFcHr7+XvAoUnu1XZdeSqwRye1lCRJ0oJ5Q+gYS3IUzet0DwKoqi8leTzwDeBa4JvAxALLPBo4GmDHB23f1/pKkqStER+lOAZsOV8kSZ6e5INJ9m/Hj57nrFexeWv3ijZtavmHAG8GDq+q2yfTq+rtVbV/260mwA8WUu+qOrmqVlbVynvtvGwhs0qSJGkOtpwvnt8HXg28Jcn9aG7SnI+zgb2T7EUTlB8JvLg3Q5IDgJOAQ6vqmp70JcBOVXV9kn2BfYEvbfWaSJKk4WA/8ZFny/niubmqbqqqNwC/DTx+PjNV1UbgGOAM4GLg01V1YZLjkxzeZns3sANwWpJzk6xq07cF/jPJRcDJwFFteSR5bZJ1NC3x5yX5+z6tpyRJGoRq3hDaz0GDZ8v54vmXyQ9VdWySP5zvjFW1Glg9Je24ns+HzDDfBpontkw37QTghPnWQZIkSf1ncL5IquoLU8b/ZrHqIkmSxoTdWkaewfmAJTmf5vnik8P5wEur6u2LWjFJkiQtOvucD95BwAeB22hu5rwAeOai1kiSJI2J9HnQoNlyPmBVdQNwZjuQZG/gLYtYJUmSNC7s1jLybDkfsCSP7B2vqktpHmkoSZKkezhbzgfvpCQPp3lG+XnAcuCCJPeqqvWLWzVJkjTSbDkfeQbnA1ZVTwVIsiewH83Lh/YDzk2yqap+ZTHrt1CbOuyPdtPDtu2sbIAVn76s0/JZsqTb8gGWdPvj15Jrbuq0/Fq/odvyN27stHxu3tRt+dtt1235ANsv77b8G3/eafFdxyHZfvuOlzDaasPtc2eStCAG5wPSBuNTfa8doLnr4j5J7ltVvxhczSRJ0lgowBcHjTyD88H5KM1pM/Wsmdrw8xHgY4OokCRJGi9lt5aRZ3A+IJPdWSRJkqSZGJxLkiSNC1vOR57BuSRJ0riwz/nI8znniyDJwb3/S5IkSWBwvljeM+V/SZKkrZbq76DBMzhfXP72JEmSpLsYnI+gJIcmuSTJ2iTHTjP9VUnOT3Jukq8n2adn2pva+S5J8oye9NcluSDJhUn+aFDrIkmS+qQ6GDRwBucjJskS4ETgMGAf4EW9wXfrk1X12KraH3gX8N523n2AI4FfBQ4F/i7JkiSPAf4HcCDN20qfneQRA1khSZLUJ2luCO3noIEzOB89BwJrq+qyqroDOBU4ojfDlDeM3pu7//Y9Aji1qm6vqh8Ba9vyHg18q6rWV9VG4N+B53W8HpIkSZrC4Hxx3NL+f/MWzLs7cGXP+Lo2bTNJXpPkhzQt56+dY94LgCcnuX+SewHPBPbYgrpJkqTFZLeWkWdwvgiq6im9/3e0jBOr6uHAG4G3zJH3YuCdwJeAfwXOBSamy5vk6CRrkqy59cY7+lxrSZKkezaD89FzFZu3aq9o02ZyKvDcueatqg9V1a+1fzDcCPxgusKq6uSqWllVK++987ItXAVJktQJW85HnsH56Dkb2DvJXkmW0dzguao3Q5K9e0afBVzafl4FHJlkuyR7AXsD327n2a39f0+a/uaf7HQtJElS/xmcj7yli12Be7okTwJeVFWvmU/+qtqY5BjgDGAJcEpVXZjkeGBNVa0CjklyCHAnTSv4S9t5L0zyaeAiYCPwmqqa7L7yT0nu387zmqq6qY+rKUmSpHkwOF8ESQ4AXgz8LvBT4FeAeQXnAFW1Glg9Je24ns+vm2XetwNvnyb9yfNdviRJGkKFjz8cAwbnA5LkkcCLaILym4HTgN+qqh8l+dGiVk6SJElDweB8cL5P01/8+VV1/pRp9uqSJElbLUYUI88bQgfnecCPgC8l+XiS5yTZdrErJUmSxog3hI48g/MBqarPV9WRwCOA04GjgXVJPgzcd1ErJ0mSpKFgcD5gVXVrVX2yqp5DcyPoN4HzFrlakiRJGgIG54uoqm5sX+pz8GLXRZIkSYvPG0K1xW648T58+rMHdVb+gy+8vbOyAVi+XafF102/6LR8gNx3h07Lr/UbOi2fbTp+5Neyjm/r2NRxh8yJibnzbKW65dZuF7BkSbflb9zYafF1xx2dls8dd3ZbfteWDiCMqE3dL2OMeEPo6DM4lyRJGhc+53zk2a1FkiRJGhK2nA9YkuXAHwBPonlI0deB91dVx/0HJEnSWPPxh2PB4HzwPkbzhtC/acdfDHwceMGi1UiSJElDweB88B5TVfv0jH8tyUWLVhtJkjQ+bDkfefY5H7zvJvn1yZEkTwDWLGJ9JEnSmEj1d9Dg2XI+eL8GfCPJFe34nsAlSc4Hqqr2XbyqSZIkaTEZnA/eoYtdAUmSNKZs7R55dmsZsKr6MfAL4AHAQyaHqvpxO21OSQ5NckmStUmOnWb6q5Kcn+TcJF9Psk+b/vQk32mnfSfJwT3zLEtycpIfJPl+kt/pywpLkqTBqT4PGjhbzgcsySuB1wErgHOBXwe+CRw823w98y8BTgSeDqwDzk6yqqp6byr9ZFV9oM1/OPBemhb764DnVNXVSR4DnAHs3s7zZuCaqnpkkm2A+23dmkqSJGmhbDkfvNcBjwd+XFVPBQ4AblrA/AcCa6vqsqq6AzgVOKI3Q1X1vjf+3rR/+1bVOVV1dZt+IbB9ksl32P8+8H/bfJuq6rqFrZYkSVpM/b4Z1BtCF4fB+eBtmHzhUJLtqur7wKMWMP/uwJU94+u4u/X7Lklek+SHwLuA105Tzu8A362q25Ps1Kb9ZZLvJjktyQMWUCdJkiT1gcH54K1rg+HPA19O8gVgXn3NF6KqTqyqhwNvBN7SOy3JrwLvBP5nm7SUppvNN6rqcTTdbN4zXblJjk6yJsmaifW39rvakiRpa1T6O2jg7HM+YFX139qPf57ka8COwL8uoIirgD16xle0aTM5FXj/5EiSFcDngN+rqh+2ydcD64HPtuOnAa+Yof4nAycDLH/wHv7gJUnSMPGbeeQZnA9YktdPk/zfk3ynqs6dRxFnA3sn2YsmKD8SePGUZexdVZe2o88CLm3TdwL+BTi2qv5rMn9VVZIvAr8FfBV4GuBbSyVJkgbM4HzwVrbDF9vxZwPnAa9KclpVvWu2matqY5JjaJ60sgQ4paouTHI8sKaqVgHHJDkEuBO4EXhpO/sxwCOA45Ic16b9dlVdQ9P95eNJ3gdcC7y8T+srSZIGxJs4R5/B+eCtAB5XVbcAJHkrTWv2U4Dv0NzAOauqWg2snpJ2XM/n180w39uAt80w7cdtHSRJkrRIDM4Hbzfg9p7xO4EHVNVtSW6fYR5JkqS52XI+8gzOB+8TwLfap7QAPAf4ZJJ7Yz9vSZK0pXw2+VgwOB+wqvrLJKcDT2yTXlVVa9rPL1mkakmSJGkIGJwvgjYYXzNnRkmSpIWw5Xzk+RIiSZIkaUjYci5JkjQubDkfebacD0iSj7f/T/uYQ0mSpK2V6u+gwbPlfHB+LcmDgd9P8jEgvROr6obFqdaWW3bzJlaceVt35V9+XWdlA9St6zstn2Xbdls+UDf+vNsFLFnSbfkTE92Wv03mzrM1NnX8zbXtAC7RXe+DjRu7LT8dtzHdtqHb8rvex3d2vP27PseAuuPObsuv7s5jY1ttCYPzwfkA8BXgYTQvG+q9olWbLkmSpHswu7UMSFWdUFWPBk6pqodV1V49g4G5JEmSbDkftKp6dZL9gCe3Sf9RVectZp0kSdKYsC/NyLPlfMCSvJbmLaG7tcMnkvzh4tZKkiSNvD7fDOoNoYvDlvPBeyXwhKq6FSDJO4FvAn+zqLWSJEnSojM4H7wAvY9HmGDKk1skSZK2iK3dI8/gfPA+DHwryefa8ecCH1rE+kiSJGlIGJwPWFW9N8mZwJPapJdX1TmLWCVJkjQubDkfed4Qugiq6rvtoxVP2JLAPMmhSS5JsjbJsdNMf1WS85Ocm+TrSfaZMn3PJLckecOU9CVJzknyzwtfK0mStJiCN4SOA4PzEZNkCXAicBiwD/CiqcE38MmqemxV7Q+8C3jvlOnvBU6fpvjXARf3ucqSJEmaJ4Pz0XMgsLaqLquqO4BTgSN6M1TVL3pG703Pj1xJngv8CLiwd54kK4BnAX/fUb0lSVLXqs+DBs7gfMDSOCrJce34nkkOXEARuwNX9oyva9OmLuc1SX5I03L+2jZtB+CNwF9MU+77gD8FNs1R/6OTrEmy5o47b11AtSVJUqd8zvlYMDgfvL8DfgN4UTt+M003lb6qqhOr6uE0wfhb2uQ/B/6qqm7pzZvk2cA1VfWdeZR7clWtrKqVy7a9d7+rLUmSRtDU+9aS7JXkW+39cZ9KsqxN364dX9tOf+hi1nsYGZwP3hOq6jXABoCquhFYtoD5rwL26Blf0abN5FSaxzUCPAF4V5LLgT8C/izJMcATgcPb9FOBg5P8wwLqJEmShsHidWuZet/aO2kaBB8B3Ai8ok1/BXBjm/5XbT71MDgfvDvbmzoLIMmuzNGVZIqzgb3bv0iXAUcCq3ozJNm7Z/RZwKUAVfXkqnpoVT2UphvL/6mqv62qN1XVijb9SOCrVXXUlq2eJEm6J5l631qSAAcDn2mzfJS7GwqPaMdppz+tza+WzzkfvBOAzwG7JXk78Hzu7nYyp6ra2LZ2nwEsAU6pqguTHA+sqapVwDFJDgHupPlr9aX9XglJkjSE+t9PfJcka3rGT66qk6fkmbxv7T7t+P2Bm6pqYzvee3/cXffOtTHNz9v81/W95iPK4HzAquoTSb4DPI3mkaTPraoFPb6wqlYDq6ekHdfz+XXzKOPPZ0g/EzhzIfWRJEnDoYObOK+rqpUzLq/nvrUkv9X3pd8DGZwvgqr6PvD9xa6HJEnSVpq8b+2ZwHLgvsBfAzslWdq2nvfeHzd579y6JEuBHYHrB1/t4WVwPmCTj1CcqqqOH3RdJEnSmBnw4w+r6k3AmwDalvM3VNVLkpxG03X3VJrutV9oZ1nVjn+znf7VqvKhjT28IXTwbu0ZJmje9PnQxayQJElSn70R+P/t3X20JHV95/H3Z2Z4GhxFQQyBSUB3NCEYkYwEE2NcEYOagMZsFk2M7snuHFc5oq4nYh5cg7vnhMQQkxNWlyjRRAVdNTrRMWgIRE0WZMDhYRiQETEMixIQeZDAPH33j64rzbXvnbl3qnq6mvfrnDq361dV3/pWd9e93/vrX1W/OclmBmPK39+0vx84uGl/M3DmXspvYtlzPmZV9cfD80nexeDiTkmSpMXby9/qOXzdWlXdzOBbzWev8yDwH8aaWM9YnO99yxmMxZIkSdojfqtn/1mcj1mSa3n4/9qlwBMBx5tLkiTJ4nwv+MWhx9uBbw/dB7RXdhy2k/veel9n8Z/whm7fntlnn07j17/9W6fxAdhvv27jP/RQp+E7vwZo245Ow2f58k7js21rt/GBerDb1zgHHNBpfGoh3+E2eerfHuw0fpZ1+3u06/fPYCd2BS+IT1fvWZyPWVV9c2/nIEmSpMlkcT4mSe5j9P+zAaqqHjvmlCRJ0pRxzHn/WZyPSVWt2PVakiRJe8DivPcszveCJI8HVjH4Ji0AquqLey8jSZIkTQKL8zFL8p+BMxjcPnEDcAKDb8l6/t7MS5Ik9dxevs+52uE3hI7fGcCzgG9W1b8Hngl8d++mJEmSpElgz/n4PVhVDyYhyX5VdUOSp+3tpCRJUr+lmdRvFufjtyXJQcCngC8kuRvw9oqSJGnPOayl9xzWMiZJzk3ys1X1sqr6blW9A/g94P3ASxcY6+QkNybZnOTMEctfm+TaJBuSfDnJ0UPLfjLJ/02ysVln/yTLk3w2yQ1N+x/s6fFKkiRp4ew5H5+vAe9KchjwMeCCqvrHhQZJshQ4FzgJ2AJckWRtVV0/tNpHquq9zfqnAOcAJydZBnwIeFVVXZ3kYGAbsB/wrqq6JMm+wMVJXlRVn1v84UqSpHHzPuf9Z8/5mFTVn1bVs4GfB+4Czm96qv97kqcuINTxwOaqurmqtgIXAqfO2te9Q7MH8vCHXC8Erqmqq5v17qqqHVX1QFVd0rRtBa5icDcZSZIkjZHF+ZhV1Ter6uyqeibwCgZDWjYtIMThwK1D81uatkdI8vokXwf+EHhD0/xUoJJclOSqJL81YruDgF8CLh618yRrkqxPsn77PQ8sIG1JktS5annS2Fmcj1mSZUl+KcmHgc8BNwK/3PZ+qurcqnoK8Fbgd5vmZcBzgF9rfr4syYnDuQEXAH9WVTfPEfe8qlpdVauXPW5522lLkqQ9YXHee445H5MkJzHoKX8x8BUGw1HWVNX3FhjqNmDl0PwRTdtcLgTe0zzeAnyxqu5scloHHMfDveTnATdV1bsXmJMkSZJaYJW+T08AACAASURBVM/5+LwN+Gfgx6vqlKr6yCIKc4ArgFVJjmou3jwNWDu8QpJVQ7MvAW5qHl8EPL25O8syBuPfr2+2+R/A44A3LiInSZK0t9XggtA2J42fPedjUlXPbynO9iSnMyi0lwLnV9XGJGcB66tqLXB6khcwuBPL3cCrm23vTnIOgwK/gHVV9dkkRwC/A9wAXJUE4M+r6n1t5CxJkqTdY3HeQ1W1Dlg3q+3tQ4/PmGfbDzG4neJw2xb8UjFJkvrP3u7esziXJEmaEg5F6T/HnEuSJEkTwp5zSZKkaWHPee9ZnEuSJE0Jh7X0n8NaJEmSpAlhz7kkSdI08Fs9p4LFuRat7lnGQ+sO7W4Hd1zXXWxg59atncZnyRg+mHrwoW7jL13abfyun6Pq9q9UPfBAt/G3b+80PkDzvQadqY7Ps66fo3R9DnT9/Hd8DoxF16/Bjh3dxpcWyOJckiRpWkzB/2OPdhbnkiRJUyB4Qeg08IJQSZIkaULYcy5JkjQt7DnvPXvOJUmSpAlhz7kkSdKUyDTcoedRzuJckiRpGnif86ngsBZJkiRpQlic91CSk5PcmGRzkjNHLH9tkmuTbEjy5SRHN+2/1rTNTDuTHNss+49JrkmyMcnZ4z4mSZK051LtTho/i/OeSbIUOBd4EXA08IqZ4nvIR6rq6VV1LPCHwDkAVfXhqjq2aX8V8I2q2pDkYOCPgBOr6ieAH0py4riOSZIkSQMW5/1zPLC5qm6uqq3AhcCpwytU1b1DswcyegTaK5ptAZ4M3FRV/9rM/z3w8lazliRJ3auWJ42dF4T2z+HArUPzW4Cfnr1SktcDbwb2BZ4/Is5/5OGifjPwtCRHNvFe2mz3A5KsAdYA7LPi8YvJX5IkdcShKP1nz/mUqqpzq+opwFuB3x1eluSngQeq6rpm3buB/wp8FPgScAuwY46451XV6qpaveyAAzs8AkmSpEcfe8775zZg5dD8EU3bXC4E3jOr7TTgguGGqvpb4G/h+73jI4tzSZI0wew57z17zvvnCmBVkqOS7Mug0F47vEKSVUOzLwFuGlq2BPhVHh5vPtN+aPPz8cDrgPd1kr0kSZLmZM95z1TV9iSnAxcBS4Hzq2pjkrOA9VW1Fjg9yQuAbcDdwKuHQjwXuLWqbp4V+k+TPKN5fFZVfa3bI5EkSa3y9odTweK8h6pqHbBuVtvbhx6fMc+2lwInjGh/RYspSpKkvcHivPcc1iJJkiRNCHvOJUmSpkBwWMs0sOdckiRJmhD2nEuSJE2Lsuu87yzOJUmSpoTDWvrPYS2SJEnShLDnXIu27P4dPOmye7vbwZJ0Fxtg6dJOw9f27Z3GB6ht3e5jyf7d/v9eW7d2Gp+d3XYhZWnH/Rs7uv+i3ur4PMiSbp+jpNvfE12fY53r+Pdo188/QG3d1u0Ouv5bM06Ft1KcAhbnkiRJUyI793YG2lMOa5EkSZImhD3nkiRJ08JhLb1nz7kkSZI0Iew5lyRJmhLeSrH/LM4lSZKmQeGXEE0Bh7VIkiRJE8Kec0mSpCnhsJb+s+e8h5KcnOTGJJuTnDli+WuTXJtkQ5IvJzm6ad83yV82y65O8rymfUWz7sx0Z5J3j/mwJEmSHvXsOe+ZJEuBc4GTgC3AFUnWVtX1Q6t9pKre26x/CnAOcDLwXwCq6ulJDgU+l+RZVXUfcOzQPq4EPjmWA5IkSe2x57z37Dnvn+OBzVV1c1VtBS4ETh1eoaruHZo9kIdP1aOBf2jWuQP4LrB6eNskTwUOBb7USfaSJKkTYTCspc1J42dx3j+HA7cOzW9p2h4hyeuTfB34Q+ANTfPVwClJliU5CvgpYOWsTU8DPlo1+nLvJGuSrE+yftv2B/bwUCRJkjTM4nxKVdW5VfUU4K3A7zbN5zMo5tcD7wb+Gdgxa9PTgAvmiXteVa2uqtX7LFvefuKSJGlxqtqfNHaOOe+f23hkb/cRTdtcLgTeA1BV24E3zSxI8s/A14bmnwEsq6or20xYkiRJu8ee8/65AliV5Kgk+zLo6V47vEKSVUOzLwFuatqXJzmweXwSsH3WhaSvYJ5ec0mSNNkcc95/9pz3TFVtT3I6cBGwFDi/qjYmOQtYX1VrgdOTvADYBtwNvLrZ/FDgoiQ7GfS2v2pW+F8FXjyO45AkSR2woO49i/Meqqp1wLpZbW8fenzGHNvdAjxtnrhPbilFSZIkLYLFuSRJ0pRwKEr/OeZckiRJmhD2nEuSJE2DAnbadd53FueSJEnTwtq89xzWIkmSJE0Ie84lSZKmhBeE9p/FuRYt23ey9K77Oou/88GHOosNwJL+f3CUfbo9hWvr1k7jk45fgyXpNr52bceOTsPXjp2dxs/Sbt+jO7du6zR+qttzoJYu7TT+WDhGWxPG4lySJGlalP9s9J3FuSRJ0pRwWEv/9f9zfUmSJGlK2HMuSZI0DQpvpTgFLM4lSZKmQIA45rz3HNYiSZIkTQh7ziVJkqZFt3cX1RjYcy5JkqRFSbIyySVJrk+yMckZTfsTknwhyU3Nz8c37UnyZ0k2J7kmyXF79wgmj8V5DyU5OcmNzRv7zHnWe3mSSrK6mT+4OYHuT/Lnc2yzNsl1XeUuSZK6k6pWp92wHfhvVXU0cALw+iRHA2cCF1fVKuDiZh7gRcCqZloDvKft56DvLM57JslS4FwGb+6jgVc0J8Hs9VYAZwCXDzU/CPwe8JY5Yv8ycH/bOUuSpDGoDqZd7bLq9qq6qnl8H7AJOBw4Ffhgs9oHgZc2j08F/qoGLgMOSnLYHhz11LE475/jgc1VdXNVbQUuZPBGn+2dwNkMCnIAqup7VfXl4bYZSR4DvBn4H51kLUmSplqSI4FnMugYfFJV3d4s+hbwpObx4cCtQ5ttadrUsDjvn12+qZvxWyur6rMLiPtO4I+BB+ZbKcmaJOuTrN+6c95VJUnSWBVUyxMcMvN3v5nWjNpz08n3CeCNVXXvI7Kq8g7sC+DdWqZMkiXAOcBrFrDNscBTqupNzX+9c6qq84DzAB633w95okmSNN3urKrV862QZB8GhfmHq+qTTfO3kxxWVbc3w1buaNpvA1YObX5E06aGPef9s6s39QrgGODSJLcwuDhj7cxFoXN4NrC6Wf/LwFOTXNpizpIkaQxS7U673F8S4P3Apqo6Z2jRWuDVzeNXA58eav+N5q4tJwD3DA1/Efac99EVwKokRzEoyk8DXjmzsKruAQ6ZmW+K7LdU1fq5AlbVe2iulm56zj9TVc9rP3VJktSp8X9D6M8CrwKuTbKhaftt4A+AjyX5TeCbwK82y9YBLwY2MxhK+5/Gm+7kszjvmaranuR04CJgKXB+VW1MchawvqrWzrd90zv+WGDfJC8FXlhV13edtyRJmj7NjSYyx+ITR6xfwOs7TarnLM57qKrWMfjPc7jt7XOs+7xZ80fuIvYtDIbFSJKkPimI3xDae445lyRJkiaEPeeSJEnTYvxjztUyi3NJkqRpYW3eew5rkSRJkiaEPeeSJElTIg5r6T17ziVJkqQJYc+5Fq22bWPH//tWZ/EHXzrWndqxo9P47Oy+96LzY6hu78mVpZ2Gh65f4jG8xp3bub3T8LWt2/hd6/wc61q67YOrrVs7jQ90fgyd2hu92Pac957FuSRJ0jQowPuc916P/x2VJEmSpos955IkSVMglBeETgF7ziVJkqQJYc+5JEnStLDnvPcsziVJkqaFxXnvOaxFkiRJmhD2nEuSJE0Db6U4Few576EkJye5McnmJGfOs97Lk1SS1c388Uk2NNPVSV7WtK9MckmS65NsTHLGuI5FkiS1J1WtTho/e857JslS4FzgJGALcEWStVV1/az1VgBnAJcPNV8HrK6q7UkOA65O8rfAduC/VdVVzXZXJvnC7JiSJEnqlj3n/XM8sLmqbq6qrcCFwKkj1nsncDbw4ExDVT1QVTPfpb0/gw/AqKrbq+qq5vF9wCbg8O4OQZIkdaKq3UljZ3HeP4cDtw7Nb2FWIZ3kOGBlVX129sZJfjrJRuBa4LVDxfrM8iOBZ/LIHvfh5WuSrE+yfls9OGoVSZIkLZLDWqZMkiXAOcBrRi2vqsuBn0jy48AHk3yualBlJ3kM8AngjVV17xzbnwecB/DYJQf7L7UkSRPD3u5pYM95/9wGrByaP6Jpm7ECOAa4NMktwAnA2pmLQmdU1Sbg/mZdkuzDoDD/cFV9srPsJUlSNwqHtUwBi/P+uQJYleSoJPsCpwFrZxZW1T1VdUhVHVlVRwKXAadU1fpmm2UASX4U+DHgliQB3g9sqqpzxn1AkiRJGnBYS880d1o5HbgIWAqcX1Ubk5wFrK+qtfNs/hzgzCTbGNwJ9XVVdWeS5wCvAq5NsqFZ97eral2HhyJJktrmfc57z+K8h5qied2strfPse7zhh7/NfDXI9b5MpB2s5QkSdJCWZxLkiRNCb84qP8sziVJkqaFxXnveUGoJEmSNCHsOZckSZoGBey057zv7DmXJEmSJoQ955IkSVPBLw6aBhbnkiRJ08LivPcszrVoWbKEJcuXdxZ/5wMPdBYboLZt7zQ+NQXfBJFuR77Vjh2dxu86/87t7Pj5AViytPt9aG4d/56ort9CfT/HgCzp8Gs+puDPgMbP4lySJGla2HPee/3/l1eSJEmaEvacS5IkTQNvpTgVLM4lSZKmQk3H9U6Pcg5rkSRJkiaEPeeSJEnTwgtCe8+ec0mSJGlCWJxPkSQnJ7kxyeYkZ86z3suTVJLVzfyRSf4tyYZmeu/4spYkSa2YuSC0zUlj57CWKZFkKXAucBKwBbgiydqqun7WeiuAM4DLZ4X4elUdO5ZkJUlSNxzW0nv2nE+P44HNVXVzVW0FLgROHbHeO4GzgQfHmZwkSZJ2zeJ8ehwO3Do0v6Vp+74kxwErq+qzI7Y/KslXk/xjkp/rME9JktSVqnYnjZ3DWh4lkiwBzgFeM2Lx7cCPVNVdSX4K+FSSn6iqe0fEWQOsAdh/yWM6zFiSJOnRx57z6XEbsHJo/oimbcYK4Bjg0iS3ACcAa5OsrqqHquougKq6Evg68NRRO6mq86pqdVWt3jf7d3AYkiRpcVruNbfnfK+w53x6XAGsSnIUg6L8NOCVMwur6h7gkJn5JJcCb6mq9UmeCHynqnYkeTKwCrh5nMlLkqQ9VMBOvyG07yzOp0RVbU9yOnARsBQ4v6o2JjkLWF9Va+fZ/LnAWUm2ATuB11bVd7rPWpIkScMszqdIVa0D1s1qe/sc6z5v6PEngE90mpwkSeqeQ1F6z+JckiRpWlic954XhEqSJEkTwp5zSZKkqVCw057zvrPnXJIkSZoQ9pxLkiRNg4Iqb6XYdxbnkiRJ08JhLb3nsBZJkiRpQthzrkWrnTvYed99ezuNxdu5o9v4Sbfxx6Hr56hr1fP8p0HfP2Lv+rZ0Hf+eyJJu49eO/p9jnf6a2Bud2N5KsffsOZckSZImhD3nkiRJ06AKdvb80ypZnEuSJE0Nh7X0nsNaJEmSpAlhz7kkSdKUKIe19J4955IkSdKEsOdckiRpKpRjzqeAPec9lOTkJDcm2ZzkzHnWe3mSSrJ6qO0nk/zfJBuTXJtk/yQrkmwYmu5M8u7xHI0kSWpFMfiG0DYnjZ095z2TZClwLnASsAW4Isnaqrp+1norgDOAy4falgEfAl5VVVcnORjYVlUPAscOrXcl8MnOD0aSJEmPYM95/xwPbK6qm6tqK3AhcOqI9d4JnA08ONT2QuCaqroaoKruqnrkd6MleSpwKPClLpKXJEkdqp3tTho7i/P+ORy4dWh+S9P2fUmOA1ZW1WdnbftUoJJclOSqJL81Iv5pwEerHLQmSZI0bg5rmTJJlgDnAK8ZsXgZ8BzgWcADwMVJrqyqi4fWOQ141Tzx1wBrAPZneUtZS5KkPVVAOU689yzO++c2YOXQ/BFN24wVwDHApUkAfghYm+QUBr3sX6yqOwGSrAOOAy5u5p8BLKuqK+faeVWdB5wH8NglT/A3gCRJk6LKoShTwGEt/XMFsCrJUUn2ZdDTvXZmYVXdU1WHVNWRVXUkcBlwSlWtBy4Cnp5keXNx6M8DwxeSvgK4YFwHIkmSpEey57xnqmp7ktMZFNpLgfOramOSs4D1VbV2nm3vTnIOgwK/gHWzxqX/KvDiDtOXJEkdclhL/1mc91BVrQPWzWp7+xzrPm/W/IcY3E5x1LpPbilFSZIkLYLFuSRJ0rRwzHnvWZxLkiRNgfu4+6K/r48f0nLYO1uOp12wOJckSZoCVXXy3s5Be867tUiSJEkTwuJckiRJmhAW55IkSdKEsDiXJEmSJoQXhGoPBNLd/3e1bWtnsQFIuo1fY/giiK6PQdNv5469nYE6VNu37+0UJC2QPeeSJEnShLA4lyRJkiaExbkkSZI0ISzOJUmSpAlhcS5JkiRNCItzSZIkaUJYnEuSJEkTwuK8h5KcnOTGJJuTnDnPei9PUklWN/P7JPlgkmuTbErytqF1b2naNyRZP47jkCRJ0iP5JUQ9k2QpcC5wErAFuCLJ2qq6ftZ6K4AzgMuHmv8DsF9VPT3JcuD6JBdU1S3N8n9fVXd2fhCSJEkayZ7z/jke2FxVN1fVVuBC4NQR670TOBt4cKitgAOTLAMOALYC93acryRJknaTxXn/HA7cOjS/pWn7viTHASur6rOztv048D3gduBfgHdV1XeaZQV8PsmVSdZ0krkkSZLm5bCWKZNkCXAO8JoRi48HdgA/DDwe+FKSv6+qm4HnVNVtSQ4FvpDkhqr64oj4a4A1APuzvKOjkCRJenSy57x/bgNWDs0f0bTNWAEcA1ya5BbgBGBtc1HoK4G/q6ptVXUH8E/AaoCquq35eQfwNwwK+R9QVedV1eqqWr1P9m/1wCRJkh7tLM775wpgVZKjkuwLnAasnVlYVfdU1SFVdWRVHQlcBpxSVesZDGV5PkCSAxkU7jckObC5gHSm/YXAdeM8KEmSJFmc905VbQdOBy4CNgEfq6qNSc5KcsouNj8XeEySjQyK/L+sqmuAJwFfTnI18BXgs1X1d90dhSRJkkZJVe3tHNRTj11ycJ2wz8mdxa9tWzuLDUDSbfxxnFvTcAzSo5nn8FS7vC7m3vpOxy+ypo0955IkSdKEsDiXJEmSJoTFuSRJkjQhLM4lSZKkCWFxLkmSJE0Ii3NJkiRpQlicS5IkSRPC+5xr0ZL8K/DNBWxyCHBnR+kY3/h92IfxjW/8bk3aMfxoVT2xq2Q0nSzONTZJ1lfVauMbfxLjj2Mfxje+8T2HpV1xWIskSZI0ISzOJUmSpAlhca5xOs/4xp/g+OPYh/GNb/x+72Mcx6BHOcecS5IkSRPCnnNJkiRpQlicqxNJbklybZINSdY3bU9I8oUkNzU/H7/AmOcnuSPJdUNtI2Nm4M+SbE5yTZLjFhn/HUlua45jQ5IXDy17WxP/xiS/sIvYK5NckuT6JBuTnNFm/vPEbyX/Zv39k3wlydXNPn6/aT8qyeVNrI8m2bdp36+Z39wsP3KR8T+Q5BtDx3DsYp6jof0sTfLVJJ9pM/954reWfxZwXrUYv8330EFJPp7khiSbkjy75fxHxW8z/6cNxdmQ5N4kb2zrGOaJ3+YxvCmD8+u6JBdkcN61dg7MEb/Nc+CMJvbGJG9s2tp8D42K39rzL+2WqnJyan0CbgEOmdX2h8CZzeMzgbMXGPO5wHHAdbuKCbwY+BwQ4ATg8kXGfwfwlhHrHg1cDewHHAV8HVg6T+zDgOOaxyuArzUxWsl/nvit5N9sE+AxzeN9gMub3D4GnNa0vxf4r83j1wHvbR6fBnx0kfE/APzKiPUX/Bo3270Z+AjwmWa+lfznid9a/izgvGoxfpvvoQ8C/7l5vC9wUMv5j4rfWv6ztl8KfAv40TaPYY74rRwDcDjwDeCAoff+a9o6B+aJ/wFaOAeAY4DrgOXAMuDvgX/X1vM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izAG//+9hIUH653N3n9Q3A+8GXgFNKwJNK/AhND9z/gT4W5qRJQD+H/CWND8/vHGu/O2y59PcdHUFsIHmBghmKqu3glW1sV332W2Zfw38alV9dwHb2VvWC2mGl7uurcOnB9ze3nIupOnv+nWag/94mp+ren24TR/0QzyUbR2wbr374pU0rY7H0PTDunNIdZn1mHahqt4BvIHmzu4ft9MpNH3/vtbmmW/fvJ5mm2+gGWLqsz3LdqIZpvQnND99PYDmTvTZ0nvrNtAxGfb2ttbSXDimpz+YpbxrgI/Q3JxD+/o57ZccbZ3vatFNM5zgg2hadW7g7p9Cd6c5t4bhLW2dT6D5fN7epkHT4jLbPnwvzQ3HF9Nc4L8FvGoI9fkYTR/P62jOlUuAC9tz5as0Afl3aUYhug7YUFXXw11fcu+l6R60oU1bR9Pv8qs018THAM+YbgGk+SyeWVt3h5lru7cyxGvnVn3ph2SuY/t/aP6guRr4e+A1VdXbYrc9ywfZf59PcgvNH55voxld54KFfEcMYoDjM9f1aJvM8x04yHXqrus6TfeMga9r23MdnKveM3g8zShUZ9L88nczzfn15kHeaxvN+ZlM0zL/e+3sCpobIqdvHP0Nmi5W3+svdBH3Gcx9Ts67jfPUZdBz5+M0v8peSjOizVsHXHe+eKOrGHPe7/+ZZJZfJbWIkuxH84X9oKrqv+FiLCX5Bs2wVR8cdV00ekn+GLi6qv58nnyHAG+oql9tv4guq6q/77BeO9L8wXBQVW3q6n1GrT0fX1lV32nn7xXb3RX3371Dmu4of1tV/zhPvg8B75o+vzRaSS6nGZ3qX0ddl645cPyItT93vwE4bZwD9CRPo2lt/AlNS81BzPyTm+6Fqur35s8FVXVukh8n+Q+aX8reNd8621mvjTStzhOtqp7UN3+v2O6uuP/uNR7PPE9FTrKWpmX20UlOqaoPLUbFJDBIH6kku9D8LPUDmuEXx9mjaR6esgvNz0svqmYISWlBquq3R10HSfduSe5H0+XgkrnyzXDDorRo7O4iSZIkjZntHSddkiRJ0pAZpEuSJEljxiBdkiRJGjMG6ZIkSdKYMUiXJEmSxoxBuiRJkjRmDNIlSZKkMWOQLkmSJI0Zg3RJkiRpzBikS5IkSWPGIF2SJEkaMwbpkiRJ0pgxSJckSZLGjEG6JEmSNGYM0iVJkqQxY5AuSZIkjRmDdEmSJGnMGKRLkiRJY8YgXZIkSRozBumSJEnSmDFIlyRJksaMQbokSZI0ZgzSJUmSpDFjkC5JkiSNGYN0SZIkacwYpEuSJEljZvmoK6Clba/7L6uH7buis/LvrKnOym7K7/YUuKO62zfT7tzS7TbctHFlp+Uv22FLp+VvnlrWafm1JZ2Wn03dlg+Qbk8z0u0hhuq2+B02d1t+tnS7AdnccflTXR9gYKrbD2lt7q78O+pWNtYd3Z/ImjgG6douD9t3Bf91xr6dlX/Zpls6Kxvgkk3367b8jQ/qtHyAS2/fu9Pyv3jlozst/74r7+y0/Gtv2qXT8u+8ZadOy1/x4+7/0FtxU7fxw4puT2N22NRtELrzdd2Wv+LWbgPQHa/f2Gn5y2+8o9PyAXLdjZ2WP/WT6zor+6xN/9JZ2ZpsdneRJEmSxoxBuiRJkjRmDNIlSZKkMWOQLkmSJI0Zg3RJkiRpzBikS5IkSWPGIH3CJDkiycVJ1ic5YYblT03yrSSbk7yob9lUknPbac3i1VqSJEm9HCd9giRZBpwMPAvYAJydZE1VXdiT7Qrg14A3zlDE7VV1SOcVlSRJ0pwM0ifLYcD6qroUIMlpwFHAXUF6VV3eLluER8RJkiRpW9jdZbLsA1zZM7+hTRvUyiTrkpyV5AXDrZokSZIGZUu6ej20qq5K8nDgy0nOr6rv92dKchxwHMB++/gRkiRJGjZb0ifLVcC+PfOr2rSBVNVV7f+XAmcCh86S79SqWl1Vq/fec9m211aSJEkzMkifLGcDByTZP8mOwNHAQKO0JLlfkp3a13sBT6anL7skSZIWj0H6BKmqzcDxwBnARcAnquqCJCclORIgyROTbABeDJyS5IJ29ccA65J8G/gK8Cd9o8JIkiRpkdiheMJU1VpgbV/aiT2vz6bpBtO/3teAx3deQUmSJM3LlnRJkiRpzBikS5IkSWPGIF2SJEkaMwbpkiRJ0pgxSJckSZLGjEG6JEmSNGYcglH3apdsfNCoqzD29t711k7Lv+yqvTotv27t9jK366Xdlr9x9+q0fIBs6bb83a7c3Gn5tz6g2ycfVzotnkrHb9B1+ZI6YUu6JEmSNGYM0iVJkqQxY5AuSZIkjRmDdEmSJGnMGKRLkiRJY8YgXZIkSRozDsEoSZI0IX7p6bvUtddNDbXMb5535xlVdcRQC9W8DNIlSZImxLXXTfFfZ+w31DKXPfiSbh9ooRnZ3WWJSnJEkouTrE9ywgzLn5rkW0k2J3lR37L9knwhyUVJLkzysDb9me065yb5apJHLs7WSJKkYShgy5D/aTQM0pegJMuAk4FnAwcCL01yYF+2K4BfAz4+QxEfAd5ZVY8BDgOubtP/Bnh5VR3SrveW4ddekiRJ87G7y9J0GLC+qi4FSHIacBRw4XSGqrq8XbbVn8BtML+8qr7Y5rulZ3EBu7Wvdwd+2FH9JUlSJ4qpsvV7EhikL037AFf2zG8AnjTguo8CbkjyaWB/4F+BE6pqCngVsDbJ7cBNwM8Mr8qSJKlrTXeXGnU1NAR2d7n3WQ48BXgj8ETg4TTdYgB+C3hOVa0CPgi8e6YCkhyXZF2SdddcO9w7yCVJkmSQvlRdBezbM7+qTRvEBuDcqrq0qjYDnwWekGRv4OCq+kab7x+An5upgKo6tapWV9Xqvfdctm1bIEmSOuGNo5PBIH1pOhs4IMn+SXYEjgbWLGDdPdqgHOAZNH3Zrwd2T/KoNv1ZwEVDrLMkSZIGZJ/0JaiqNic5HjgDWAZ8oKouSHISsK6q1iR5IvAZ4H7A85P8YVU9tqqmkrwR+FKSAN8E3teW+b+Af2xvNr0e+PWRbKAkSdomRTFV9kmfBAbpS1RVrQXW9qWd2PP6bJpuMDOt+0XgoBnSP0MT2EuSpCXKG0cng91dJEmSpDFjS7okSdKEKGDKlvSJYEu6JEmSNGZsSZckSZog9kmfDAbpkiRJE6LA0V0mhN1dJEmSpDFjS7rG2tfueGin5d8ytbLT8ndddken5QN87uJ7jKa5pNRUt20FO/+w28vc5vt0Wjw73phu3wDYYarb8m94RLfH4L5XLu0nIqbrVk9bVe91lvYZoWkG6ZIkSROiKEd3mRB2d5EkSZLGjC3pkiRJk6Jgyob0iWBLuiRJkjRmbEmXJEmaEIU3jk4Kg3RJkqSJEaboflQodc/uLpIkSdKYsSVdkiRpQhSwxRtHJ4It6ZIkSdKYMUhfopIckeTiJOuTnDDD8qcm+VaSzUle1LfsHUkuSHJRkvckSZt+Zlvmue30gMXaHkmSNBxTbb/0YU0aDbu7LEFJlgEnA88CNgBnJ1lTVRf2ZLsC+DXgjX3r/hzwZGD6WfJfBZ4GnNnOv7yq1nVWeUmS1JkCA+sJYZC+NB0GrK+qSwGSnAYcBdwVpFfV5e2y/pGYClgJ7AgEWAH8uPsqS5IkaVB2d1ma9gGu7Jnf0KbNq6q+DnwF+FE7nVFVF/Vk+WDb1eX/m+4G0y/JcUnWJVl3zbVT27YFkiSpE1sqQ500Ggbp9zJJHgk8BlhFE9g/I8lT2sUvr6rHA09pp/85UxlVdWpVra6q1XvvuWwxqi1JknSvYpC+NF0F7Nszv6pNG8T/AM6qqluq6hbgdOBnAarqqvb/m4GP03SrkSRJS8R0n3RvHF36DNKXprOBA5Lsn2RH4GhgzYDrXgE8LcnyJCtobhq9qJ3fC6BNfx7wnQ7qLkmSOlKEKXYY6qTRcM8vQVW1GTgeOAO4CPhEVV2Q5KQkRwIkeWKSDcCLgVOSXNCu/ing+8D5wLeBb1fV54GdgDOSnAecS9My/77F3C5JkiQ1HN1liaqqtcDavrQTe16fTdMNpn+9KeB/z5B+K/DTw6+pJElaTN7sORlsSZckSZLGjC3pkiRJE8KHGU0Og3RJkqSJEabKjhKTwKMoSZIkjRlb0iVJkiZEAVtsg50IBukaa4/d8Yedlv/1mx7ZafnLd5jqtHyA+u+VnZa/4pZu+zZu2XNLp+Vv2rU6Lf/+F3ZaPLc9qPu+pStu7HYf7fLf3Z4HXf+yv+zObvfPDpu6PQeypdv6s6Xb+kv3VgbpkiRJE8QbRyeDQbokSdKEqPLG0UnhUZQkSZLGjC3pkiRJE2SL3V0mgkG6JEnShGgeZmRHiUngUZQkSZLGjC3pkiRJE8MbRyeFR1GSJEkaM7akS5IkTQifODo5PIpLVJIjklycZH2SE2ZY/oYkFyY5L8mXkjy0Z9mxSS5pp2N70v8lybeTXJDkvUmWLdb2SJKk4ZiqDHXSaBikL0Ft8Hwy8GzgQOClSQ7sy3YOsLqqDgI+BbyjXff+wO8DTwIOA34/yf3adX6lqg4GHgfsDby4622RJEnSPRmkL02HAeur6tKq2gicBhzVm6GqvlJVt7WzZwGr2te/BHyxqq6rquuBLwJHtOvc1OZZDuxI86uZJElaIoowxQ5DnTQa7vmlaR/gyp75DW3abF4JnD7IuknOAK4GbqZpgb+HJMclWZdk3TXXTi289pIkSZqTQfqES3IMsBp45yD5q+qXgAcDOwHPmCXPqVW1uqpW772n3dYlSRonW2qHoU4aDff80nQVsG/P/Ko2bStJDgfeDBxZVXcOum5V3QF8jr4uNJIkabxNP3HU7i5Ln3t+aTobOCDJ/kl2BI4G1vRmSHIocApNgH51z6IzgF9Mcr/2htFfBM5IsmuSB7frLgeeC3x3EbZFkiRJfRwnfQmqqs1JjqcJuJcBH6iqC5KcBKyrqjU03Vt2BT6ZBOCKqjqyqq5L8kc0gT7ASW3aA4E1SXai+ePtK8B7F3nTJEnSdigcNnFSGKQvUVW1Fljbl3Ziz+vD51j3A8AH+tJ+DDxxyNWUJEnSNjBIlyRJmiA+cXQyGKRLkiRNiCqYckSWieBRlCRJksaMLemSJEkTI2zBG0cngS3pkiRJ0pixJV2SJGlCFPZJnxQG6Rprl2/es9PyH7TTjZ2W//6zntJp+QD3/263P2ve9uBuy1/5o2Wdlr/bZVs6LX9qx273z17f3thp+QBbduz2C32Hjd0eg6Ve/xU33tFp+bWs44BtS3VbPjR3Q2pgo3hKaJLfAl5F83fC+cArgAcDpwF7At8E/mdVbWyfyfIR4KeBa4GXVNXli17pMeefWpIkSdpmSfYBXgesrqrH0Txo8Wjg7cCfVdUjgeuBV7arvBK4vk3/szaf+hikS5IkTYgibKnhTgNaDuycZDlwH+BHwDOAT7XLPwy8oH19VDtPu/yZaR+PrrsZpEuSJGkueyVZ1zMd17uwqq4C3gVcQROc30jTveWGqtrcZtsA7NO+3ge4sl13c5u/2/6tS5B90iVJkiZIB33Sf1JVq2dbmOR+NK3j+wM3AJ8Ejhh2Je5tDNIlSZImRAFbFn90l8OBy6rqGoAknwaeDOyRZHnbWr4KuKrNfxWwL7Ch7R6zO80NpOphdxdJkiRtjyuAn0lyn7Zv+TOBC4GvAC9q8xwLfK59vaadp13+5SqH8OlnS7okSdLECFOL/MTRqvpGkk8B3wI2A+cApwL/DJyW5K1t2vvbVd4PfDTJeuA6mpFg1McgXZIkaUKMqLsLVfX7wO/3JV8KHDZD3juAFy9GvZYyu7ssUUmOSHJxkvVJTphh+RuSXJjkvCRfSvLQNv2QJF9PckG77CU96xzflldJ9lrM7ZEkSdLdDNKXoCTLgJOBZwMHAi9NcmBftnNoHipwEM0YpO9o028DfrWqHktz5/WfJ9mjXfafNDd//KDjTZAkSR2Zaru8DGvSaBikL02HAeur6tKq2kjzyN2jejNU1Veq6rZ29iyau6qpqu9V1SXt6x8CVwN7t/Pn+FheSZKk0bNP+tJ010MAWhuAJ82R/5XA6f2JSQ4DdgS+v5A3bx9icBzAfvv4EZIkaVxUZSR90jV8RlgTLskxwGoWs53KAAAgAElEQVTgaX3pDwY+ChxbVVsWUmZVnUpz1zarD17pkEmSJI2RKYP0iWCQvjRNPwRgWu8DAu6S5HDgzcDTqurOnvTdaIZFenNVndVxXSVJkrRABulL09nAAUn2pwnOjwZe1pshyaHAKcARVXV1T/qOwGeAj1TVpxavypIkqWsFbPFmz4ng7yFLUPt43eOBM4CLgE9U1QVJTkpyZJvtncCuwCeTnJtkTZv+K8BTgV9r089NcghAktcl2UDTMn9ekr9dzO2SJElSw5b0Jaqq1gJr+9JO7Hl9+Czr/R3wd7Msew/wniFWU5IkLarYJ31CGKRLkiRNiOaJo3Z3mQT+qSVJkiSNGVvSJUmSJsiUbbATwaMoSZIkjRlb0iVJkiZEEfukTwiDdEmSpAmyxY4SE8EgXWPtO7ev6rT8v/vuEzstf/fzV3RaPsDK66c6Lb+WdXux3+nG6rT85bd3W/6yTZ0WT6a6rT/Ayh/f3mn5t+1zn07L32XDbZ2WT3V7DGrFsk7Lz6ZurxFd7x/p3sogXZIkaUJUwZTdXSaCv4dIkiRJY8aWdEmSpAnijaOTwSBdkiRpQjSju9hRYhJ4FCVJkqQxY0u6JEnSBJnC7i6TwJZ0SZIkaczYki5JkjQhCm8cnRS2pC9RSY5IcnGS9UlOmGH5G5JcmOS8JF9K8tA2/aFJvpXk3CQXJHl1zzovTXJ+u86/JNlrMbdJkiRtr+bG0WFOGg33/BKUZBlwMvBs4EDgpUkO7Mt2DrC6qg4CPgW8o03/EfCzVXUI8CTghCQPSbIc+Avg6e065wHHd781kiRJ6meQvjQdBqyvqkuraiNwGnBUb4aq+kpVTT8r+yxgVZu+sarubNN34u7PQNpplyQBdgN+2O1mSJKkYdtChjppNOyTvjTtA1zZM7+BplV8Nq8ETp+eSbIv8M/AI4HfrqoftumvAc4HbgUuAV47U2FJjgOOA9hvHz9CkiSNiyqYsk/6RLAlfcIlOQZYDbxzOq2qrmy7tDwSODbJA5OsAF4DHAo8hKa7y+/OVGZVnVpVq6tq9d57Lut8GyRJku5tbAZdmq4C9u2ZX9WmbSXJ4cCbgaf1dHG5S1X9MMl3gKcAP2jTvt+u+wngHjekSpKk8ebNnpPBo7g0nQ0ckGT/JDsCRwNrejMkORQ4BTiyqq7uSV+VZOf29f2AnwcupgnyD0yyd5v1WcBFnW+JJEmS7sGW9CWoqjYnOR44A1gGfKCqLkhyErCuqtbQdG/ZFfhkcx8oV1TVkcBjgD9NUjQ3ir6rqs4HSPKHwL8n2UTTsv5ri7xpkiRpOxRxnPQJYZC+RFXVWmBtX9qJPa8Pn2W9LwIHzbLsvcB7h1hNSZK0yByRZTLY3UWSJEkaM7akS5IkTYgCu7tMCFvSJUmSpDFjS7okSdIEcQjGyWCQLkmSNCnK0V0mhX9qSZIkSWPGlnSNtZ9s2rXT8lesu2+n5d/8iC2dlg+w/I5u/9be/bLNnZafLdVp+Stu2thp+Vt2WtZp+bVD9y1im/bYqdPyd73s5k7Lzy23d1p+3afb/ZNuP6Lkjk3dlr+p22uEFqZwCMZJYUu6JEmSNGZsSZckSZog9kmfDAbpkiRJE8Jx0ieH3V0kSZKkMWNLuiRJ0gSxJX0y2JIuSZIkjRlb0iVJkiZE4cOMJoVBuiRJ0gRxnPTJYHeXJSrJEUkuTrI+yQkzLH9DkguTnJfkS0ke2rNsKsm57bSmJz1J3pbke0kuSvK6xdoeSZIk3c2W9CUoyTLgZOBZwAbg7CRrqurCnmznAKur6rYkrwHeAbykXXZ7VR0yQ9G/BuwL/FRVbUnygM42QpIkDV954+iksCV9aToMWF9Vl1bVRuA04KjeDFX1laq6rZ09C1g1QLmvAU6qqi1tGVcPsc6SJEkakEH60rQPcGXP/IY2bTavBE7vmV+ZZF2Ss5K8oCf9EcBL2mWnJzlgpsKSHNfmWXfNtVPbug2SJGnIph9mNMxJo2F3lwmX5BhgNfC0nuSHVtVVSR4OfDnJ+VX1fWAn4I6qWp3khcAHgKf0l1lVpwKnAqw+eGV1vhGSJGlgBtaTwZb0pekqmr7j01a1aVtJcjjwZuDIqrpzOr2qrmr/vxQ4Ezi0XbQB+HT7+jPAQcOuuCRJkuZnkL40nQ0ckGT/JDsCRwNrejMkORQ4hSZAv7on/X5Jdmpf7wU8GZi+4fSzwNPb108DvtfpVkiSpKGaHifd7i5Ln91dlqCq2pzkeOAMYBnwgaq6IMlJwLqqWgO8E9gV+GQSgCuq6kjgMcApSbbQ/JH2Jz2jwvwJ8LEkvwXcArxqUTdMkiRttzKwnggG6UtUVa0F1valndjz+vBZ1vsa8PhZlt0APHeI1ZQkSdI2MEiXJEmaID5xdDLYJ12SJEkaM7akS5IkTYjyiaMTwyBdkiRpgnjj6GSwu4skSZI0ZmxJlyRJmhiObT4pDNK1XW7eAmfe3t0PMt/6yb7zZ9oOO19dnZa/64ZOiwdg5XWbOy1/p2vv6LT8HW7b2Gn5m3db2Wn5K350U6fls2wRfvCc2tJt+VnaAUOmur1OsKnbc5gtHR/fjZu6LV+6lzJIlyRJmiD2SZ8MBumSJEkTonB0l0nhjaOSJEnSmLElXZIkaVJUM1a6lj5b0iVJkqQxY0u6JEnSBNmCfdIngUG6JEnShCgc3WVS2N1FkiRJGjMG6UtUkiOSXJxkfZITZlj+hiQXJjkvyZeSPLRNf3qSc3umO5K8oF32/iTfbtf5VJJdF3u7JEnS9mieODrMSaNhkL4EJVkGnAw8GzgQeGmSA/uynQOsrqqDgE8B7wCoqq9U1SFVdQjwDOA24AvtOr9VVQe361wBHN/91kiSJKmffdIXUZL9Bsx6Q1XN9azxw4D1VXVpW+5pwFHAhdMZquorPfnPAo6ZoZwXAadX1W3tOje15QXYmaZrmyRJWkIcgnEyGKQvrg/TBL5z/XZUwIeAj8yRZx/gyp75DcCT5sj/SuD0GdKPBt7dm5Dkg8BzaAL+/ztTYUmOA44DeMBD/AhJkjROvHF0MhhhLaKqevpiv2eSY4DVwNP60h8MPB44oze9ql7Rdqf5S+AlwAf7y6yqU4FTAR79+JX+vS5JkjRk9klfmq4C9u2ZX9WmbSXJ4cCbgSOr6s6+xb8CfKaqNvWvV1VTwGnALw+txpIkqXNVTUv6MCeNhkH6GEnypgGzng0ckGT/JDvSdFtZ01fWocApNAH61TOU8VLg73vyJ8kjp18DRwLfXfhWSJIkaXvZ3WWEknyidxY4BHj7fOtV1eYkx9N0VVkGfKCqLkhyErCuqtYA7wR2BT7ZxNxcUVVHtu/7MJqW+H/re/8PJ9mtff1t4DXbtYGSJGnROWziZDBIH62bqupV0zNJ/mbQFatqLbC2L+3EnteHz7Hu5TQ3n/ambQGePOj7S5Kk8eToLpPB7i6j9ba++TePpBaSJEkaK7akj0CS84HzgPOSnAecDxxbVf1BuyRJ0oJ4s+dksCV9NJ4GvA+4neamz+/QjE0uSZK05CTZI8mnknw3yUVJfjbJ/ZN8Mckl7f/3a/MmyXuSrE9yXpInjLr+48ggfQSq6rqqOrOq3lNVxwJPBNaPul6SJGlpK4Y7/OICWuX/AviXqvop4GDgIuAE4EtVdQDwpXYe4NnAAe10HDDwPXn3JgbpI5DkUb3zVXUJcNCIqiNJkiZIDXmaT5LdgacC7weoqo1VdQNwFM3T1mn/f0H7+ijgI9U4C9ijfciietgnfTROSfIImgcQnQesBL6T5D5VddtoqyZJkrSVvZKs65k/tX36+LT9gWuADyY5GPgm8HrggVX1ozbPfwMPbF/vA1zZs/6GNu1H6C4G6SNQVU8HSLIfzU9Ch7T/n5tkS/tT0ZJw3x3gF3be0ln5T9r78s7KBjj7mr06LX/zzt3/WLX8lns8NHaodrix278ba+cdOy1/2e3d7p9s7Lb8Wr6s0/IBMtXdOQxQO+/Uafm59fZOy+fObo8xyzq+TnR8fEn3NylW19swSaqTG0d/UlWr51i+HHgC8BtV9Y0kf8HdXVuaalVVEgeHXACD9BGqqiuAK4DPT6cl2XV0NZIkSUve4ofCG4ANVfWNdv5TNEH6j5M8uKp+1HZnmX4C+lU0D1WctqpNUw/7pI+Zqrpl1HWQJEkaVFX9N3Blkke3Sc8ELgTWAMe2accCn2tfrwF+tR3l5WeAG3u6xahlS7okSdIEGdE46b8BfCzJjsClwCtoGoM/keSVwA+AX2nzrqUZeno9cFubV30M0kcoyfOr6vPz55QkSRpfVXUuMFO/9WfOkLeA13ZeqSXO7i6j5RNGJUnSUFUNd9Jo2JI+Wj63V5IkDU0xsu4uGjJb0kfLv08lSZJ0D7akS5IkTYoCbEmfCLakL1FJjkhycZL1SU6YYfkbklyY5LwkX0ry0J5lb0/ynXZ6SU/68W15laTbp/xIkiRpVgbpo/XjbVkpyTLgZODZwIHAS5Mc2JftHGB1VR1E81CBd7TrPpfmqWCHAE8C3phkt3ad/wQOpxkmSZIkLUHeODoZDNJHqKqetY2rHgasr6pLq2ojcBpwVF/ZX6mq6ee5n0XzNC9ogvp/r6rNVXUrcB5wRLvOOVV1+TbWSZIkjYMa8qSRMEhfmvYBruyZ39CmzeaVwOnt628DRyS5T9ul5els/WheSZIkjZg3jk64JMfQPFzgaQBV9YUkTwS+BlwDfB2YWmCZxwHHAey3jx8hSZLGRxyCcULYkj5CSZ6V5H1JDmnnjxtw1avYuvV7VZvWX/7hwJuBI6vqzun0qnpbVR3SdrcJ8L2F1LuqTq2q1VW1eu89ly1kVUmSJA3AZtDR+nXgNcBbktyf5mbOQZwNHJBkf5rg/GjgZb0ZkhwKnAIcUVVX96QvA/aoqmuTHAQcBHxhu7dEkiSNB/uRTwRb0kfr5qq6oareCPwi8MRBVqqqzcDxwBnARcAnquqCJCclObLN9k5gV+CTSc5NsqZNXwH8R5ILgVOBY9rySPK6JBtoWubPS/K3Q9pOSZK0GKp54ugwJ42GLemj9c/TL6rqhCS/MeiKVbUWWNuXdmLP68NnWe8OmhFeZlr2HuA9g9ZBkiRJ3TBIH6Gq+lzf/F+Oqi6SJGlC2N1lIhikj0CS82nGJ5+ezgeOraq3jbRikiRJGgv2SR+NpwHvA26nuenzO8BzRlojSZI0ITLkSaNgS/oIVNV1wJntRJIDgLeMsEqSJGlS2N1lItiSPgJJHtU7X1WX0AyFKEmSJNmSPiKnJHkEzRjn5wErge8kuU9V3TbaqkmSpCXNlvSJYJA+AlX1dIAk+wEH0zzE6GDg3CRbquqnRlm/cfLjO+/bafnXHrii0/JXfeH6TssHqB27PY1z+53zZ9qe8m+7o9PyWdbtD4Z1R7f7h42bui0fYJf7dFv+8o6fTDw11W35O3T8o3N1HFGl2z7FtWkRPqNbOj7G0hgySF9EbVDe79vtBM3dGfdNsltV3bR4NZMkSROhAB9ANBEM0hfXh2lOn/6zp78Z5UPARxajQpIkabJ0/eOMFodB+iKa7uYiSZIkzcUgXZIkaZLYkj4RDNIlSZImiX3SJ4LjpI9Ikmf0/i9JkiRNM0gfnXf1/S9JkrTdUsOdNBoG6aPnb1KSJEnaikH6EpXkiCQXJ1mf5IQZlr86yflJzk3y1SQH9iz73Xa9i5P8Uk/665N8J8kFSX5zsbZFkiQNSXUwaSQM0pegJMuAk4FnAwcCL+0Nwlsfr6rHV9UhwDuAd7frHggcDTwWOAL46yTLkjwO+F/AYTRPP31ekkcuygZJkqQhSXPj6DAnjYRB+tJ0GLC+qi6tqo3AacBRvRn6nli6C3f/LXwUcFpV3VlVlwHr2/IeA3yjqm6rqs3AvwEv7Hg7JEmSNAOD9NG5pf3/5m1Ydx/gyp75DW3aVpK8Nsn3aVrSXzfPut8BnpJkzyT3AZ4D7LsNdZMkSaNkd5eJYJA+IlX11N7/O3qPk6vqEcCbgLfMk/ci4O3AF4B/Ac4FpmbKm+S4JOuSrLvm2hmzSJIkaTsYpC9NV7F1K/eqNm02pwEvmG/dqnp/Vf10+4fD9cD3Ziqsqk6tqtVVtXrvPZdt4yZIkqRO2JI+EQzSl6azgQOS7J9kR5obQdf0ZkhyQM/sc4FL2tdrgKOT7JRkf+AA4L/adR7Q/r8fTX/0j3e6FZIkafgM0ifC8lFXQJDk54GXVtVrB8lfVZuTHA+cASwDPlBVFyQ5CVhXVWuA45McDmyiaRU/tl33giSfAC4ENgOvrarpPiv/mGTPdp3XVtUNQ9xMSZIkDcggfUSSHAq8DPgV4L+BnwIGCtIBqmotsLYv7cSe16+fY923AW+bIf0pg76/JEkaQ4XDJk4Ig/RFlORRwEtpgvObgU8Cv1BVlyW5bKSVkyRJ0tgwSF9c36XpT/6iqjq/b5m9viRJ0naLEcVE8MbRxfVC4DLgC0k+muT5SVaMulKSJGmCeOPoRDBIX0RV9dmqOhp4JHA6cBywIckHgd1GWjlJkiSNDYP0EaiqW6vq41X1fJobRr8OnDfiakmSJGlMGKSPWFVd3z4c6BmjroskSZLGgzeOaqyd9/eP67T8B6+7tdPyd7ix2/IBakW3p3Hdfnun5TO1pdvyl3e7f7Jjt7eV1NTU/Jm29z3u3Nhp+aluO7XWpk2dlk86bs+qjs+BjuufZd239y3GeTBJvHF0MhikS5IkTRLHSZ8IdneRJEmSxowt6SOQZCXwf4Cfpxnc6KvA31TVHSOtmCRJWtocNnFiGKSPxkdonjj6l+38y4CPAi8eWY0kSZI0NgzSR+NxVXVgz/xXklw4stpIkqTJYUv6RLBP+mh8K8nPTM8keRKwboT1kSRJEyI13EmjYUv6aPw08LUkV7Tz+wEXJzkfqKo6aHRVkyRJ0qgZpI/GEaOugCRJmlC2fk8Eu7uMQFX9ALgJeCDw0Ompqn7QLptXkiOSXJxkfZITZlj+6iTnJzk3yVeTHNimPyvJN9tl30zyjJ51dkxyapLvJflukl8eygZLkqTFU0OeNBK2pI9AklcBrwdWAecCPwN8HXjGXOv1rL8MOBl4FrABODvJmqrqvfn041X13jb/kcC7aVrwfwI8v6p+mORxwBnAPu06bwaurqpHJdkBuP/2bakkSZK2hS3po/F64InAD6rq6cChwA0LWP8wYH1VXVpVG4HTgKN6M1TVTT2zu9D+LVxV51TVD9v0C4Cdk+zUzv868P/afFuq6icL2yxJkjRKw75p1BtHR8cgfTTumH5wUZKdquq7wKMXsP4+wJU98xu4uzX8Lklem+T7wDuA181Qzi8D36qqO5Ps0ab9UZJvJflkkgcuoE6SJEkaEoP00djQBsWfBb6Y5HPAQH3RF6KqTq6qRwBvAt7SuyzJY4G3A/+7TVpO0/3ma1X1BJruN++aqdwkxyVZl2TdNddODbvakiRpe1SGO2kk7JM+AlX1P9qXf5DkK8DuwL8soIirgH175le1abM5Dfib6Zkkq4DPAL9aVd9vk68FbgM+3c5/EnjlLPU/FTgVYPXBK/0hTJKkceI380QwSB+BJG+YIfl/JvlmVZ07QBFnAwck2Z8mOD8aeFnfexxQVZe0s88FLmnT9wD+GTihqv5zOn9VVZLPA78AfBl4JuBTUCVJkkbAIH00VrfT59v55wHnAa9O8smqesdcK1fV5iTH04zMsgz4QFVdkOQkYF1VrQGOT3I4sAm4Hji2Xf144JHAiUlObNN+saqupukW89Ekfw5cA7xiSNsrSZIWiTd7TgaD9NFYBTyhqm4BSPL7NK3bTwW+SXOj55yqai2wti/txJ7Xr59lvbcCb51l2Q/aOkiSJGmEDNJH4wHAnT3zm4AHVtXtSe6cZR1JkqT52ZI+EQzSR+NjwDfaUV0Ang98PMku2A9ckiRtK8c2nxgG6SNQVX+U5HTgyW3Sq6tqXfv65SOqliRJksaEQfqItEH5unkzSpIkLYQt6RPBhxlJkiRJY8aWdEmSpEliS/pEsCV9ESX5aPv/jMMjSpIkba/UcCeNhi3pi+unkzwE+PUkHwHSu7CqrhtNtbbd+TftxcO/+Oudlf+A67Z0VjbAih9c02n5TE11Wz7ALbd2/x5d6nof7ZD582yHLTfc1mn5WbEIl+kVO3ZafN1xR6fld27zxm7LT8ftZVObOi2+Oj7HAFi2rPv3kMaMQfriei/wJeDhNA8t6r2yVZsuSZKkezm7uyyiqnpPVT0G+EBVPbyq9u+ZDNAlSZIE2JI+ElX1miQHA09pk/69qs4bZZ0kSdKEsB/5RLAlfQSSvI7mqaMPaKePJfmN0dZKkiQteUO+adQbR0fHlvTReBXwpKq6FSDJ24GvA3850lpJkiRpLBikj0aA3iEtpugb6UWSJGmb2Po9EQzSR+ODwDeSfKadfwHw/hHWR5IkSWPEIH0EqurdSc4Efr5NekVVnTPCKkmSpElhS/pE8MbREamqb7VDMr5nWwL0JEckuTjJ+iQnzLD81UnOT3Jukq8mObBv+X5Jbknyxr70ZUnOSfJPC98qSZI0SsEbRyeFQfoSlGQZcDLwbOBA4KX9QTjw8ap6fFUdArwDeHff8ncDp89Q/OuBi4ZcZUmSJC2AQfrSdBiwvqouraqNwGnAUb0Zquqmntld6PnxK8kLgMuAC3rXSbIKeC7wtx3VW5Ikda2GPGkkDNJHII1jkpzYzu+X5LAFFLEPcGXP/IY2rf99Xpvk+zQt6a9r03YF3gT84Qzl/jnwO8CWeep/XJJ1SdZN3XzrAqotSZI65TjpE8MgfTT+GvhZ4KXt/M003VeGqqpOrqpH0ATlb2mT/wD4s6q6pTdvkucBV1fVNwco99SqWl1Vq5fdd5dhV1uSJOlez9FdRuNJVfWEJOcAVNX1SXZcwPpXAfv2zK9q02ZzGvA30+8NvCjJO4A9gC1J7qBpiT8yyXOAlcBuSf6uqo5ZQL0kSdKo2fo9EWxJH41N7c2fBZBkb+bpYtLnbOCAJPu3wf3RwJreDEkO6Jl9LnAJQFU9paoeVlUPo+ne8sdV9VdV9btVtapNPxr4sgG6JEkaVP8IcW2c8o12JLp/mG6QTLJTO7++Xf6wUdZ7XBmkj8Z7gM8AD0jyNuCrwB8PunJVbQaOB86gGYnlE1V1QZKTkhzZZjs+yQVJzgXeABw71C2QJEnjaXQ3jvaPEPd2mi62jwSuB17Zpr8SuL5N/7M2n/rY3WUEqupjSb4JPJNmSNMXVNWChj2sqrXA2r60E3tev36AMv5glvQzgTMXUh9JkjQeRnGzZ88IcW8D3pAkwDOAl7VZPkxzX9zf0IxI9wdt+qeAv0qSqrKjTg+D9BGpqu8C3x11PSRJkoZgeoS4+7bzewI3tL/+w9Yj0d01Sl1VbU5yY5v/J4tX3fFnkD4C00Mv9quqkxa7LpIkacIMvz16ryTreuZPrapTp2d6R4hL8gtDf/d7KYP00egdXHwl8Dx8yqckSRpPP6mq1XMsfzJ9I8QBfwHskWR525reOxLd9Ch1G5IsB3YHru2s9kuUQfoIVNWf9s4neRfNTaCSJEnbbgRPCa2q3wV+F6BtSX9jVb08ySeBF9EMBX0s8Ll2lTXt/Nfb5V+2P/o9ObrLeLgPzV+YkiRJ22WMnjj6JpqbSNfT9Dl/f5v+fmDPNv0NwAnb9S7/f3v3HmVJWd57/PujBxCGiSCIUZg4aEYTghHJSDAhhogYvAQ05oImxpyVnFlGWWIuK8FcOB7MWSskhpisED1EicYLaNToRMegIRA1CciAw2UYkBExDAdFEEEgwFye88euhk3T3TPdXdW9d/n9rFWrd71V9dRTe+/qfvrdb9XuKXvSl0CSa3jk/9wJ4ImA49ElSdJYG75DXFXdBBw9zToPAD+/qImNIYv0pfGyocfbgW8MXf08Vg5b8U3O+4m/7Sz+H7x3bWexAWq/fTuNn3vv7zQ+ANu2db+PLk1MdBs/6Tb8XnP5suARte2hbuN3/BrXQ92eA5no9kPn2t7tr//O89+2CH++dszl+/7mobqMvwSjOBw40gsW6Uugqr621DlIkiRpdFmkL6Ik32H6/28DVFV9zyKnJEmSemYpvsxI7bNIX0RVtWLXa0mSJC2ARXovWKQvkSQHAKsZ3E8UgKr63NJlJEmSpFFhkb4Ekvw6cBqD2y5uBI5hcK/QFyxlXpIkacwtwX3S1Q3vk740TgOeC3ytqn4KeA7w7aVNSZIkSaPCnvSl8UBVPZCEJHtX1fVJnrnUSUmSpPGWZtL4s0hfGluT7A98HPhskrsAb8soSZIWzuEuveBwl0WU5JwkP15Vr6iqb1fVW4A/YvD1uC+fY6wTk9yQZEuSx3ydbpLXJbkmycYkX0hy+NCyH07yn0k2Nes8Lsm+ST6V5Pqm/U8WerySJEmaH3vSF9eXgbcleTLwYeD8qvq3uQZJMgGcA5wAbAUuT7Kuqq4bWu2DVfXOZv2TgLOBE5MsA94PvKaqrkpyILAN2Bt4W1VdnGQv4KIkL66qT8//cCVJ0mLzPun9YE/6Iqqqv6yq5wE/CdwJnNf0XP+vJM+YQ6ijgS1VdVNVPQRcAJw8ZV/3DM0u55EPv14EXF1VVzXr3VlVO6rq/qq6uGl7CLiSwd1nJEmStMgs0pdAVX2tqs6qqucAr2Iw1GXzHEIcAtwyNL+1aXuUJG9I8hXgT4E3Ns3PACrJhUmuTPK702y3P/AzwEXT7TzJ2iQbkmz49p075pC2JEnqXLU8aUlYpC+BJMuS/EySDwCfBm4Afrbt/VTVOVX1dOD3gD9smpcBxwK/1Px8RZLjh3MDzgf+qqpumiHuuVW1pqrW7H/gRNtpS5KkhbBI7wXHpC+iJCcw6Dl/CfBFBsNU1lbVfXMMdSuwcmj+0KZtJhcA72gebwU+V1V3NDmtB47ikV7zc4Ebq+rtc8xJkiRJLbEnfXG9GV/Af14AACAASURBVPgP4Aer6qSq+uA8CnSAy4HVSQ5rLvI8BVg3vEKS1UOzLwVubB5fCDyruZvLMgbj469rtvlj4PHAm+aRkyRJWmo1uHC0zUlLw570RVRVL2gpzvYkpzIouCeA86pqU5IzgQ1VtQ44NckLGdy55S7gtc22dyU5m0GhX8D6qvpUkkOBPwCuB65MAvDXVfWuNnKWJEnS7rNIH1NVtR5YP6XtjKHHp82y7fsZ3IZxuG0rfkmZJEnjz97vXrBIlyRJ6hGHqPSDY9IlSZKkEWNPuiRJUp/Yk94LFumSJEk94nCXfnC4iyRJkjRi7EmXJEnqC78ltDcs0rUgX/32wbz6k6/vLP7TdzzUWWyA3P9Ap/F33vmtTuMDsOee3e+jQ809+buzY0en4avG/69h169BPdTteda1zl/hnTu7jT/m55j03coiXZIkqU/Gv+9AWKRLkiT1RvDC0b7wwlFJkiRpxNiTLkmS1Cf2pPeCPemSJEnSiLEnXZIkqUfSg7tOySJdkiSpP7xPem843EWSJEkaMRbpYyrJiUluSLIlyenTLH9dkmuSbEzyhSSHN+2/1LRNTjuTHNks+8UkVyfZlOSsxT4mSZK0cKl2Jy0Ni/QxlGQCOAd4MXA48KrJInzIB6vqWVV1JPCnwNkAVfWBqjqyaX8N8NWq2pjkQODPgOOr6oeA701y/GIdkyRJkh5hkT6ejga2VNVNVfUQcAFw8vAKVXXP0Oxyph+h9qpmW4CnATdW1Teb+X8BXtlq1pIkqXvV8qQl4YWj4+kQ4Jah+a3Aj05dKckbgN8C9gJeME2cX+SR4n4L8Mwkq5p4L2+2e4wka4G1ABMH7D+f/CVJUkccotIP9qT3WFWdU1VPB34P+MPhZUl+FLi/qq5t1r0L+A3gQ8DngZuBHTPEPbeq1lTVmon99uvwCCRJkr472ZM+nm4FVg7NH9q0zeQC4B1T2k4Bzh9uqKp/Av4JHu4tn7ZIlyRJI8ye9F6wJ308XQ6sTnJYkr0YFNzrhldIsnpo9qXAjUPL9gB+gUfGo0+2H9z8PAB4PfCuTrKXJEnSrOxJH0NVtT3JqcCFwARwXlVtSnImsKGq1gGnJnkhsA24C3jtUIjnA7dU1U1TQv9lkmc3j8+sqi93eySSJKlV3jaxNyzSx1RVrQfWT2k7Y+jxabNsewlwzDTtr2oxRUmStBQs0nvB4S6SJEnSiLEnXZIkqSeCw136wp50SZIkacTYky5JktQnZVd6H1ikS5Ik9YjDXfrB4S6SJEnSiLEnXQuTovbs7l/2Pb/0lc5iA9Q+j+s0PhMT3cYH6r//u9sddH0MHcfPsm5/zdUDD3Yaf1FMdNxfs0fH8Xfu7DZ+x2rb9m530OHv6N5Il+/RdBh7GoW3YOwJi3RJkqQeyXj/36qGw10kSZKkEWNPuiRJUp843KUX7EmXJEmSRow96ZIkST3iLRj7wSJdkiSpLwq/zKgnHO4iSZIkjRh70iVJknrE4S79YE/6mEpyYpIbkmxJcvo0y1+X5JokG5N8IcnhTfteSf6uWXZVkuOa9hXNupPTHUnevsiHJUmSJOxJH0tJJoBzgBOArcDlSdZV1XVDq32wqt7ZrH8ScDZwIvA/AarqWUkOBj6d5LlV9R3gyKF9XAF8bFEOSJIktcee9F6wJ308HQ1sqaqbquoh4ALg5OEVquqeodnlPHLKHg78a7PO7cC3gTXD2yZ5BnAw8PlOspckSZ0Ig+EubU5aGhbp4+kQ4Jah+a1N26MkeUOSrwB/Cryxab4KOCnJsiSHAT8CrJyy6SnAh6qmvzw8ydokG5Js2HHvfQs8FEmSJE1lkd5jVXVOVT0d+D3gD5vm8xgU9RuAtwP/AeyYsukpwPmzxD23qtZU1ZqJ/Za3n7gkSZqfqvYnLQnHpI+nW3l07/ehTdtMLgDeAVBV24HfnFyQ5D+ALw/NPxtYVlVXtJmwJEmSdp896ePpcmB1ksOS7MWg53vd8ApJVg/NvhS4sWnfN8ny5vEJwPYpF5y+ill60SVJ0mhzTHo/2JM+hqpqe5JTgQuBCeC8qtqU5ExgQ1WtA05N8kJgG3AX8Npm84OBC5PsZND7/pop4X8BeMliHIckSeqAhXUvWKSPqapaD6yf0nbG0OPTZtjuZuCZs8R9WkspSpIkaZ4s0iVJknrEISr94Jh0SZIkacTYky5JktQXBey0K70PLNIlSZL6xBq9FxzuIkmSJI0Ye9IlSZJ6xAtH+8EiXQszUUx8z7alzmLe6v7/7nYHe3T/YVX22afT+PXAg93G37m90/jjLhPdv4eq66/93tbxa7xHuo3fcf61Y0en8Xthx85Ow3f6GnR9fqm3LNIlSZL6xH8MesEiXZIkqUcc7tIPXjgqSZIkjRh70iVJkvqi8BaMPWGRLkmS1BMB4pj0XnC4iyRJkjRi7EmXJEnqk27vWKlFYk+6JEmSNGIs0sdUkhOT3JBkS5LTZ1nvlUkqyZpm/sAkFye5N8lfz7DNuiTXdpW7JEnqTqpanbQ0HO4yhpJMAOcAJwBbgcuTrKuq66astwI4DbhsqPkB4I+AI5ppauyfBe7tKHVJktQl7+7SG/akj6ejgS1VdVNVPQRcAJw8zXpvBc5iUJgDUFX3VdUXhtsmJdkP+C3gjzvJWpIk9U6Slc2n9Ncl2ZTktKb9CUk+m+TG5ucBTXuS/FUzGuDqJEct7RGMJov08XQIcMvQ/Nam7WHNG35lVX1qDnHfCvw5cP9sKyVZm2RDkg07vnPfHMJLkqRuFVTL065tB367qg4HjgHekORw4HTgoqpaDVzUzAO8GFjdTGuBd7T9LPSBRXoPJdkDOBv47TlscyTw9Kr6x12tW1XnVtWaqlozsWL5AjKVJEnjrqpuq6orm8ffATYz6Dw8GXhvs9p7gZc3j08G/r4GLgX2T/LkRU575DkmfTzdCqwcmj+0aZu0gsF480uSAHwvsC7JSVW1YYaYzwPWJLmZwfvi4CSXVNVxLecuSZI6lCUck55kFfAcBtfDPamqbmsWfR14UvN4phEBt6GH2ZM+ni4HVic5LMlewCnAusmFVXV3VR1UVauqahVwKTBbgU5VvaOqntKsfyzwZQt0SZLGUPvDXQ6aHObaTGun221zbdtHgTdV1T2PTqm8pHWO7EkfQ1W1PcmpwIXABHBeVW1KciawoarWzbZ901v+PcBeSV4OvGjqnWEkSZIad1TVmtlWSLIngwL9A1X1sab5G0meXFW3NcNZbm/adzUiQFikj62qWg+sn9J2xgzrHjdlftUuYt/MNLdnlCRJI64gi/yNoxmMrX03sLmqzh5atA54LfAnzc9PDLWfmuQC4EeBu4eGxahhkS5JkqSF+HHgNcA1STY2bb/PoDj/cJJfA74G/EKzbD3wEmALgzvK/Y/FTXc8WKRLkiT1ySJ/S2jz/SuZYfHx06xfwBs6TaoHLNIlSZL6xMsze8G7u0iSJEkjxp50SZKkHskiD3dRN+xJlyRJkkaMPelamJ1hxwMT3YV/+spdr7QA+fLNncavBx/sND4A6fZ/7Ux0G792dHuvsNqxo9P4Xev6+VkMXb8GqZmuVxsP2WO881+Mc6zr56jT+EtxCtuT3gsW6ZIkSX1RLM0/Bmqdw10kSZKkEWNPuiRJUk+E8sLRnrAnXZIkSRox9qRLkiT1iT3pvWCRLkmS1CcW6b3gcBdJkiRpxNiTLkmS1BfegrE37EkfU0lOTHJDki1JTp9lvVcmqSRrmvmjk2xspquSvKJpX5nk4iTXJdmU5LTFOhZJktSeVLU6aWnYkz6GkkwA5wAnAFuBy5Osq6rrpqy3AjgNuGyo+VpgTVVtT/Jk4Kok/wRsB367qq5strsiyWenxpQkSVL37EkfT0cDW6rqpqp6CLgAOHma9d4KnAU8MNlQVfdX1fZm9nEMPhijqm6rqiubx98BNgOHdHcIkiSpE1XtTloSFunj6RDglqH5rUwpqJMcBaysqk9N3TjJjybZBFwDvG6oaJ9cvgp4Do/ugR9evjbJhiQbdtx730KOQ5IkSdOwSO+hJHsAZwO/Pd3yqrqsqn4IeC7w5iSPG9p2P+CjwJuq6p4Ztj+3qtZU1ZqJ/Za3fwCSJGmeWu5Ftyd9yVikj6dbgZVD84c2bZNWAEcAlyS5GTgGWDd58eikqtoM3NusS5I9GRToH6iqj3WWvSRJ6kZhkd4TFunj6XJgdZLDkuwFnAKsm1xYVXdX1UFVtaqqVgGXAidV1YZmm2UASZ4K/ABwc5IA7wY2V9XZi31AkiRJeoR3dxlDzZ1ZTgUuBCaA86pqU5IzgQ1VtW6WzY8FTk+yjcGdVF9fVXckORZ4DXBNko3Nur9fVes7PBRJktQ275PeCxbpY6opntdPaTtjhnWPG3r8PuB906zzBSDtZilJkqT5sEiXJEnqEb+AqB8s0iVJkvrEIr0XvHBUkiRJGjH2pEuSJPVFATvtSe8De9IlSZKkEWNPuiRJUm/4BUR9YZEuSZLUJxbpvWCRrgXZ44GwYtNencXP5ms7iw1QO3rwjQ/V7THU9jF/jnZ0G766HvvZ8esLkImJzvcxzmpHx2+idD3ytNv8s0f3X7Ex1ueZ9bLmySJdkiSpT+xJ7wUvHJUkSZJGjD3pkiRJfeEtGHvDIl2SJKk3alGuZVH3HO4iSZIkjRh70iVJkvrEC0d7wZ50SZIkacRYpPdMkhOT3JBkS5LTZ1nvlUkqyZpmflWS/06ysZneuXhZS5KkVkxeONrmpCXhcJceSTIBnAOcAGwFLk+yrqqum7LeCuA04LIpIb5SVUcuSrKSJKkbDnfpBXvS++VoYEtV3VRVDwEXACdPs95bgbOABxYzOUmSJO0ei/R+OQS4ZWh+a9P2sCRHASur6lPTbH9Yki8l+bckP9FhnpIkqStV7U5aEg53+S6SZA/gbOBXp1l8G/B9VXVnkh8BPp7kh6rqnmnirAXWAuy54oAOM5YkSfruZE96v9wKrByaP7Rpm7QCOAK4JMnNwDHAuiRrqurBqroToKquAL4CPGO6nVTVuVW1pqrWTOy7vIPDkCRJ89NyL7o96UvGnvR+uRxYneQwBsX5KcCrJxdW1d3AQZPzSS4BfqeqNiR5IvCtqtqR5GnAauCmxUxekiQtUAE7/cbRPrBI75Gq2p7kVOBCYAI4r6o2JTkT2FBV62bZ/PnAmUm2ATuB11XVt7rPWpIkSVNZpPdMVa0H1k9pO2OGdY8bevxR4KOdJidJkrrnEJVesEiXJEnqE4v0XvDCUUmSJGnE2JMuSZLUGwU77UnvA3vSJUmSpBFjT7okSVJfFFR5C8Y+sEiXJEnqE4e79ILDXSRJkqQRY0+6Fma/HfD8u7qL/zfdhQaoHTu63cHOjuMD7DHRbfyuPzbNmPcV9OD56fw86Fht77jXsOtzrGsdv0drvN8+/eQtGHthzP86SpIkSf1jT7okSVJfVMFOLxztA4t0SZKkPnG4Sy843EWSJEkaMfakS5Ik9Ug53KUX7EmXJEmSRow96ZIkSb1RjknvCXvSx1SSE5PckGRLktNnWe+VSSrJmqG2H07yn0k2JbkmyeOSrEiycWi6I8nbF+doJElSK4rBN462OWlJ2JM+hpJMAOcAJwBbgcuTrKuq66astwI4DbhsqG0Z8H7gNVV1VZIDgW1V9QBw5NB6VwAf6/xgJEmS9Bj2pI+no4EtVXVTVT0EXACcPM16bwXOAh4YansRcHVVXQVQVXdWPfr74pI8AzgY+HwXyUuSpA7VznYnLQmL9PF0CHDL0PzWpu1hSY4CVlbVp6Zs+wygklyY5MokvztN/FOAD1U5qE2SJGkpONylh5LsAZwN/Oo0i5cBxwLPBe4HLkpyRVVdNLTOKcBrZom/FlgLsOcTH99S1pIkaaEKKMeR94JF+ni6FVg5NH9o0zZpBXAEcEkSgO8F1iU5iUGv++eq6g6AJOuBo4CLmvlnA8uq6oqZdl5V5wLnAuzz/U/xN4EkSaOiyiEqPeFwl/F0ObA6yWFJ9mLQ871ucmFV3V1VB1XVqqpaBVwKnFRVG4ALgWcl2be5iPQngeELTl8FnL9YByJJkqTHsid9DFXV9iSnMii4J4DzqmpTkjOBDVW1bpZt70pyNoNCv4D1U8at/wLwkg7TlyRJHXK4Sz9YpI+pqloPrJ/SdsYM6x43Zf79DG7DON26T2spRUmSJM2TRbokSVKfOCa9FyzSJUmSeuI73HXhv9RHDmo57B0tx9NusEiXJEnqiao6calzUDu8u4skSZI0YizSJUmSpBFjkS5JkiSNGIt0SZIkacR44agWZOLOPdj/PSs63MFEd7EB6sFu4yfdxgfYuaP7fXSq41uF1Zh/qUctwuu7GO/TLnWdf9e3sxv39+hi6Pw19jXQ6LEnXZIkSRoxFumSJEnSiLFIlyRJkkaMRbokSZI0YizSJUmSpBFjkS5JkiSNGIt0SZIkacRYpI+pJCcmuSHJliSnz7LeK5NUkjXN/J5J3pvkmiSbk7x5aN2bm/aNSTYsxnFIkiTpsfwyozGUZAI4BzgB2ApcnmRdVV03Zb0VwGnAZUPNPw/sXVXPSrIvcF2S86vq5mb5T1XVHZ0fhCRJkmZkT/p4OhrYUlU3VdVDwAXAydOs91bgLOCBobYClidZBuwDPATc03G+kiRJmgOL9PF0CHDL0PzWpu1hSY4CVlbVp6Zs+xHgPuA24L+At1XVt5plBXwmyRVJ1naSuSRJknbJ4S49lGQP4GzgV6dZfDSwA3gKcADw+ST/UlU3AcdW1a1JDgY+m+T6qvrcNPHXAmsB9t5n/46OQpIk6buXPenj6VZg5dD8oU3bpBXAEcAlSW4GjgHWNRePvhr456raVlW3A/8OrAGoqlubn7cD/8igoH+Mqjq3qtZU1Zpley9v9cAkSZJkkT6uLgdWJzksyV7AKcC6yYVVdXdVHVRVq6pqFXApcFJVbWAwxOUFAEmWMyjgr0+yvLnQdLL9RcC1i3lQkiRJGrBIH0NVtR04FbgQ2Ax8uKo2JTkzyUm72PwcYL8kmxgU+39XVVcDTwK+kOQq4IvAp6rqn7s7CkmSJM3EMeljqqrWA+untJ0xw7rHDT2+l8FtGKeucxPw7HazlCRJ0nzYky5JkiSNGIt0SZIkacRYpEuSJEkjxiJdkiRJGjEW6ZIkSdKIsUiXJEmSRoxFuiRJkjRiUlVLnYPGWJJvAl+bwyYHAXd0lI7xjT8O+zC+8Y3frVE7hqdW1RO7Skb9ZZGuRZVkQ1WtMb7xRzH+YuzD+MY3vuewtDsc7iJJkiSNGIt0SZIkacRYpGuxnWt8449w/MXYh/GNb/zx3sdiHIPkmHRJkiRp1NiTLkmSJI0Yi3R1JsnNSa5JsjHJhqbtCUk+m+TG5ucBc4x5XpLbk1w71DZtzAz8VZItSa5OctQ8478lya3NcWxM8pKhZW9u4t+Q5Kd3EXtlkouTXJdkU5LT2sx/lvit5N+s/7gkX0xyVbOP/920H5bksibWh5Ls1bTv3cxvaZavmmf89yT56tAxHDmf52hoPxNJvpTkk23mP0v81vLPHM6rFuO3+R7aP8lHklyfZHOS57Wc/3Tx28z/mUNxNia5J8mb2jqGWeK3eQy/mcH5dW2S8zM471o7B2aI3+Y5cFoTe1OSNzVtbb6Hpovf2vMv7baqcnLqZAJuBg6a0vanwOnN49OBs+YY8/nAUcC1u4oJvAT4NBDgGOCyecZ/C/A706x7OHAVsDdwGPAVYGKW2E8GjmoerwC+3MRoJf9Z4reSf7NNgP2ax3sClzW5fRg4pWl/J/AbzePXA+9sHp8CfGie8d8D/Nw068/5NW62+y3gg8Anm/lW8p8lfmv5M4fzqsX4bb6H3gv8evN4L2D/lvOfLn5r+U/ZfgL4OvDUNo9hhvitHANwCPBVYJ+h9/6vtnUOzBL/PbRwDgBHANcC+wLLgH8Bvr+t53+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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 13.1 s, sys: 1.69 s, total: 14.8 s\n", "Wall time: 16 s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(false_alarm, \"False alarm\",\n", " SubGaussianGLR,\n", " values_Delta=values_Delta,\n", " values_tau=values_tau,\n", " firstMean=firstMean,\n", " horizon=horizon,\n", " repetitions=10,\n", " savefig=\"False_alarm__SubGaussianGLR__Bernoulli_T1000_N10__20deltas__19taus\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this tuning ($\\delta=0.01$), the `Sub-Gaussian GLR` almost always gives a false alarm! It detects too soon!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Gaussian data:" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) mu^1 = 0.5 and horizon = 1000...\n", "with 20 values for Delta, and 19 values for tau, and 1 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Tau s ||', max=1, 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 14.7 s, sys: 1.87 s, total: 16.6 s\n", "Wall time: 4min 25s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(false_alarm, \"False alarm\",\n", " SubGaussianGLR,\n", " values_Delta=values_Delta,\n", " values_tau=values_tau,\n", " firstMean=firstMean,\n", " horizon=horizon,\n", " repetitions=1,\n", " gaussian=True,\n", " savefig=\"False_alarm__SubGaussianGLR__Gaussian_T1000_N1__20deltas__19taus\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For another, simpler problem." ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values of tau: [ 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180\n", " 190]\n" ] } ], "source": [ "horizon = T = 200\n", "min_tau = 10\n", "max_tau = T - min_tau\n", "step = 10\n", "values_tau = np.arange(min_tau, max_tau + 1, step)\n", "nb_values_tau = len(values_tau)\n", "print(f\"Values of tau: {values_tau}\")" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values of delta: [0.02 0.12888889 0.23777778 0.34666667 0.45555556 0.56444444\n", " 0.67333333 0.78222222 0.89111111 1. 1.10888889 1.21777778\n", " 1.32666667 1.43555556 1.54444444 1.65333333 1.76222222 1.87111111\n", " 1.98 ]\n" ] } ], "source": [ "firstMean = mu_1 = -1.0\n", "max_mu_2 = 1.0\n", "nb_values_Delta = nb_values_tau\n", "max_delta = max_mu_2 - mu_1\n", "epsilon = 0.01\n", "values_Delta = np.linspace(epsilon * max_delta, (1 - epsilon) * max_delta, nb_values_Delta)\n", "print(f\"Values of delta: {values_Delta}\")" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This will give a grid of 19 x 19 = 361 values of Delta and tau to explore.\n" ] } ], "source": [ "print(f\"This will give a grid of {nb_values_Delta} x {nb_values_tau} = {nb_values_Delta * nb_values_tau} values of Delta and tau to explore.\")" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "code_folding": [], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm SubGaussian-GLR($\\delta=$0.01, $\\sigma=$0.25, joint) mu^1 = -1.0 and horizon = 200...\n", "with 19 values for Delta, and 19 values for tau, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Delta s ||', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 13.3 s, sys: 1.81 s, total: 15.1 s\n", "Wall time: 47.3 s\n" ] } ], "source": [ "%%time\n", "_ = view2D_onemeasure(missed_detection, \"Missed detection probability\",\n", " SubGaussianGLR,\n", " values_Delta=values_Delta,\n", " values_tau=values_tau,\n", " firstMean=firstMean,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " savefig=\"Missed_detection__SubGaussianGLR__Gaussian_T1000_N10__19deltas__19taus\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "# Exploring the parameters of change point detection algorithms: how to tune them?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A simple problem function\n", "\n", "We consider again a problem with $T=1000$ samples, first coming from a distribution of mean $\\mu^1 = 0.25$ then from a second distribution of mean $\\mu^2 = 0.75$ (largest gap, $\\Delta = 0.5$).\n", "We consider also a single breakpoint located at $\\tau = \\frac{1}{2} T = 500$, ie the algorithm will observe $500$ samples from $\\nu^1$ then $500$ from $\\nu^2$.\n", "\n", "We can consider Bernoulli or Gaussian distributions." ] }, { "cell_type": "code", "execution_count": 241, "metadata": {}, "outputs": [], "source": [ "horizon = 200\n", "firstMean = mu_1 = 0.25\n", "secondMean = mu_2 = 0.75\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A generic function" ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "code_folding": [ 0 ] }, "outputs": [], "source": [ "def explore_parameters(measure,\n", " CDAlgorithm,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=True,\n", " gaussian=False,\n", " n_jobs=1,\n", " list_of_args_kwargs=tuple(),\n", " mean=True,\n", " ):\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " print(f\"\\nExploring {measure.__name__} for algorithm {CDAlgorithm}, mu^1 = {firstMean}, mu^2 = {secondMean}, and horizon = {horizon}, tau = {tau}...\")\n", "\n", " nb_of_args_kwargs = len(list_of_args_kwargs)\n", " print(f\"with {nb_of_args_kwargs} values for args, kwargs, and {repetitions} repetitions.\")\n", " results = np.zeros(nb_of_args_kwargs) if mean else np.zeros((repetitions, nb_of_args_kwargs))\n", " \n", " for i, argskwargs in tqdm(enumerate(list_of_args_kwargs), desc=\"ArgsKwargs\", leave=False):\n", " args, kwargs = argskwargs\n", " # now the random Monte Carlo repetitions\n", " for j, rep in tqdm(enumerate(range(repetitions)), desc=\"Repetitions\", leave=False):\n", " data = get_toy_data(firstMean=firstMean, secondMean=secondMean, tau=tau, horizon=horizon, gaussian=gaussian)\n", " result = measure(data, tau, CDAlgorithm, *args, **kwargs)\n", " if mean:\n", " results[i] += result\n", " else:\n", " results[j, i] = result\n", " if mean:\n", " results[i] /= repetitions\n", " if verbose: print(f\"For args = {args}, kwargs = {kwargs} ({i}th), {'mean' if mean else 'vector of'} result = {results[i]}\")\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I want to (try to) use [`joblib.Parallel`](https://joblib.readthedocs.io/en/latest/parallel.html) to run the \"repetitions\" for loop in parallel, for instance on 4 cores on my machine." ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "def explore_parameters_parallel(measure,\n", " CDAlgorithm,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=True,\n", " gaussian=False,\n", " n_jobs=CPU_COUNT,\n", " list_of_args_kwargs=tuple(),\n", " mean=True,\n", " ):\n", " if isinstance(tau, float): tau = int(tau * horizon)\n", " print(f\"\\nExploring {measure.__name__} for algorithm {CDAlgorithm}, mu^1 = {firstMean}, mu^2 = {secondMean}, and horizon = {horizon}, tau = {tau}...\")\n", "\n", " nb_of_args_kwargs = len(list_of_args_kwargs)\n", " print(f\"with {nb_of_args_kwargs} values for args, kwargs, and {repetitions} repetitions.\")\n", " results = np.zeros(nb_of_args_kwargs) if mean else np.zeros((repetitions, nb_of_args_kwargs))\n", "\n", " def delayed_measure(i, j, argskwargs):\n", " args, kwargs = argskwargs\n", " data = get_toy_data(firstMean=firstMean, secondMean=secondMean, tau=tau, horizon=horizon, gaussian=gaussian)\n", " return i, j, measure(data, tau, CDAlgorithm, *args, **kwargs)\n", "\n", " # now the random Monte Carlo repetitions\n", " for i, j, result in Parallel(n_jobs=n_jobs, verbose=int(verbose))(\n", " delayed(delayed_measure)(i, j, argskwargs)\n", " for i, argskwargs in tqdm(enumerate(list_of_args_kwargs), desc=\"ArgsKwargs\", leave=False)\n", " for j, rep in tqdm(enumerate(range(repetitions)), desc=\"Repetitions||\", leave=False)\n", " ):\n", " if mean:\n", " results[i] += result\n", " else:\n", " results[j, i] = result\n", " if mean:\n", " results /= repetitions\n", "\n", " if verbose:\n", " for i, argskwargs in enumerate(list_of_args_kwargs):\n", " args, kwargs = argskwargs\n", " print(f\"For args = {args}, kwargs = {kwargs} ({i}th), {'mean' if mean else 'vector of'} result = {results[i]}\")\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the result as a 1D plot" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "code_folding": [ 14 ] }, "outputs": [], "source": [ "def view1D_explore_parameters(measure, name,\n", " CDAlgorithm,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=False,\n", " gaussian=False,\n", " n_jobs=CPU_COUNT,\n", " list_of_args_kwargs=tuple(),\n", " argskwargs2str=None,\n", " savefig=None,\n", " ):\n", " explore = explore_parameters_parallel if n_jobs > 1 else explore_parameters\n", " results = explore(measure,\n", " CDAlgorithm,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=repetitions,\n", " verbose=verbose,\n", " gaussian=gaussian,\n", " n_jobs=n_jobs,\n", " list_of_args_kwargs=list_of_args_kwargs,\n", " mean=False,\n", " )\n", " fig = plt.figure()\n", "\n", " plt.boxplot(results)\n", " plt.title(fr\"{name} for {CDAlgorithm.__name__}, for $T={horizon}$, {'Gaussian' if gaussian else 'Bernoulli'} data, and $\\mu_1={firstMean:.3g}$, $\\mu_2={secondMean:.3g}$, $\\tau={tau:.3g}$ and ${repetitions}$ repetitions\")\n", " plt.ylabel(f\"{name}\")\n", "\n", " x_ticklabels = []\n", " for argskwargs in list_of_args_kwargs:\n", " args, kwargs = argskwargs\n", " x_ticklabels.append(f\"{args}, {kwargs}\" if argskwargs2str is None else argskwargs2str(args, kwargs))\n", " ax = plt.gca()\n", " ax.set_xticklabels(x_ticklabels, rotation=80, verticalalignment=\"top\")\n", " \n", " show_and_save(savefig=savefig)\n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments for `Monitored`" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_Monitored = tuple([\n", " ((), {'window_size': w, 'threshold_b': None}) # empty args, kwargs = {window_size=80, threshold_b=None}\n", " for w in [5, 10, 20, 40, 80, 120, 160, 200, 250, 300, 350, 400, 500, 1000, 1500]\n", "])" ] }, { "cell_type": "code", "execution_count": 246, "metadata": {}, "outputs": [], "source": [ "argskwargs2str_for_Monitored = lambda args, kwargs: fr\"$w={kwargs['window_size']:.4g}$\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On a first Bernoulli problem, a very easy one (with a large gap of $\\Delta=0.5$)." ] }, { "cell_type": "code", "execution_count": 247, "metadata": {}, "outputs": [], "source": [ "horizon = 100\n", "firstMean = mu_1 = 0.25\n", "secondMean = mu_2 = 0.75\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 373, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 100, tau = 50...\n", "with 15 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 5, 'threshold_b': None} (0th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 10, 'threshold_b': None} (1th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 20, 'threshold_b': None} (2th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 40, 'threshold_b': None} (3th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 80, 'threshold_b': None} (4th), mean result = 37.9\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 120, 'threshold_b': None} (5th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 160, 'threshold_b': None} (6th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 200, 'threshold_b': None} (7th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 250, 'threshold_b': None} (8th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 300, 'threshold_b': None} (9th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 350, 'threshold_b': None} (10th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 400, 'threshold_b': None} (11th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 500, 'threshold_b': None} (12th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 1000, 'threshold_b': None} (13th), mean result = 50.0\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions', max=1, style=ProgressStyle(de…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 1500, 'threshold_b': None} (14th), mean result = 50.0\n", "CPU times: user 792 ms, sys: 24.9 ms, total: 817 ms\n", "Wall time: 1.01 s\n" ] }, { "data": { "text/plain": [ "array([50. , 50. , 50. , 50. , 37.9, 50. , 50. , 50. , 50. , 50. , 50. ,\n", " 50. , 50. , 50. , 50. ])" ] }, "execution_count": 373, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "explore_parameters(detection_delay,\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=True,\n", " gaussian=False,\n", " n_jobs=1,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " )" ] }, { "cell_type": "code", "execution_count": 374, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 100, tau = 50...\n", "with 15 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ 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style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For args = (), kwargs = {'window_size': 5, 'threshold_b': None} (0th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 10, 'threshold_b': None} (1th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 20, 'threshold_b': None} (2th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 40, 'threshold_b': None} (3th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 80, 'threshold_b': None} (4th), mean result = 38.9\n", "For args = (), kwargs = {'window_size': 120, 'threshold_b': None} (5th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 160, 'threshold_b': None} (6th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 200, 'threshold_b': None} (7th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 250, 'threshold_b': None} (8th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 300, 'threshold_b': None} (9th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 350, 'threshold_b': None} (10th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 400, 'threshold_b': None} (11th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 500, 'threshold_b': None} (12th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 1000, 'threshold_b': None} (13th), mean result = 50.0\n", "For args = (), kwargs = {'window_size': 1500, 'threshold_b': None} (14th), mean result = 50.0\n", "CPU times: user 619 ms, sys: 139 ms, total: 758 ms\n", "Wall time: 1.87 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=4)]: Done 150 out of 150 | elapsed: 1.8s finished\n" ] }, { "data": { "text/plain": [ "array([50. , 50. , 50. , 50. , 38.9, 50. , 50. , 50. , 50. , 50. , 50. ,\n", " 50. , 50. , 50. , 50. ])" ] }, "execution_count": 374, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "explore_parameters_parallel(detection_delay,\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " verbose=True,\n", " gaussian=False,\n", " n_jobs=4,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " )" ] }, { "cell_type": "code", "execution_count": 375, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 100, tau = 50...\n", "with 15 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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"model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__Monitored__Bernoulli_T1000_N100__15.png'...\n", " Saved! 'Detection_delay__Monitored__Bernoulli_T1000_N100__15.png' created of size '25734b', at 'Mon Dec 17 13:13:36 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__Monitored__Bernoulli_T1000_N100__15.pdf'...\n", " Saved! 'Detection_delay__Monitored__Bernoulli_T1000_N100__15.pdf' created of size '17219b', at 'Mon Dec 17 13:13:36 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.36 s, sys: 536 ms, total: 2.89 s\n", "Wall time: 4.07 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=False,\n", " n_jobs=4,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " argskwargs2str=argskwargs2str_for_Monitored,\n", " savefig=f\"Detection_delay__Monitored__Bernoulli_T1000_N100__{len(list_of_args_kwargs_for_Monitored)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the same problem, with $10000$ data instead of $1000$." ] }, { "cell_type": "code", "execution_count": 376, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 15 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, 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"display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__Monitored__Bernoulli_T10000_N100__15.png'...\n", " Saved! 'Detection_delay__Monitored__Bernoulli_T10000_N100__15.png' created of size '27448b', at 'Mon Dec 17 13:13:47 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__Monitored__Bernoulli_T10000_N100__15.pdf'...\n", " Saved! 'Detection_delay__Monitored__Bernoulli_T10000_N100__15.pdf' created of size '19215b', at 'Mon Dec 17 13:13:47 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.31 s, sys: 737 ms, total: 3.04 s\n", "Wall time: 10.8 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=10*horizon,\n", " repetitions=100,\n", " gaussian=False,\n", " n_jobs=4,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " argskwargs2str=argskwargs2str_for_Monitored,\n", " savefig=f\"Detection_delay__Monitored__Bernoulli_T10000_N100__{len(list_of_args_kwargs_for_Monitored)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On two Gaussian problems, one with a gap of $\\Delta=0.5$ (easy) and a harder with a gap of $\\Delta=0.1$.\n", "It is very intriguing that small difference in the gap can yield such large differences in the detection delay (or missed detection probability, as having a detection delay of $D=T-\\tau$ means a missed detection!)." ] }, { "cell_type": "code", "execution_count": 377, "metadata": {}, "outputs": [], "source": [ "horizon = 10000\n", "firstMean = mu_1 = -0.25\n", "secondMean = mu_2 = 0.25\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 378, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.25, mu^2 = 0.25, and horizon = 10000, tau = 5000...\n", "with 15 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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'Detection_delay__Monitored__Gaussian_T1000_N100__15.png' created of size '26971b', at 'Mon Dec 17 13:14:57 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__Monitored__Gaussian_T1000_N100__15.pdf'...\n", " Saved! 'Detection_delay__Monitored__Gaussian_T1000_N100__15.pdf' created of size '18588b', at 'Mon Dec 17 13:14:57 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.22 s, sys: 877 ms, total: 4.1 s\n", "Wall time: 1min 10s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=True,\n", " n_jobs=4,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " argskwargs2str=argskwargs2str_for_Monitored,\n", " savefig=f\"Detection_delay__Monitored__Gaussian_T1000_N100__{len(list_of_args_kwargs_for_Monitored)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a smaller gap, the problem gets harder, and can become impossible to solve (with such a small time horizon)." ] }, { "cell_type": "code", "execution_count": 379, "metadata": {}, "outputs": [], "source": [ "horizon = 10000\n", "firstMean = mu_1 = -0.1\n", "secondMean = mu_2 = 0.1\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 380, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.1, mu^2 = 0.1, and horizon = 10000, tau = 5000...\n", "with 15 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, 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'Detection_delay__Monitored__Bernoulli_T1000_N100__15_2.png' created of size '25412b', at 'Mon Dec 17 13:16:37 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__Monitored__Bernoulli_T1000_N100__15_2.pdf'...\n", " Saved! 'Detection_delay__Monitored__Bernoulli_T1000_N100__15_2.pdf' created of size '16860b', at 'Mon Dec 17 13:16:37 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.67 s, sys: 735 ms, total: 4.4 s\n", "Wall time: 1min 40s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=True,\n", " n_jobs=4,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " argskwargs2str=argskwargs2str_for_Monitored,\n", " savefig=f\"Detection_delay__Monitored__Bernoulli_T1000_N100__{len(list_of_args_kwargs_for_Monitored)}_2\",\n", " )" ] }, { "cell_type": "code", "execution_count": 381, "metadata": {}, "outputs": [], "source": [ "horizon = 10000\n", "firstMean = mu_1 = -0.05\n", "secondMean = mu_2 = 0.05\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 382, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_Monitored = tuple([\n", " ((), {'window_size': w, 'threshold_b': None}) # empty args, kwargs = {window_size=80, threshold_b=None}\n", " for w in [5, 10, 20, 40, 80, 120, 160, 200, 250, 300, 350, 400, 500, 1000, 1500, 2000, 2500, 3000, 4000]\n", "])" ] }, { "cell_type": "code", "execution_count": 383, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.05, mu^2 = 0.05, and horizon = 10000, tau = 5000...\n", "with 19 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.73 s, sys: 1.01 s, total: 5.74 s\n", "Wall time: 2min 26s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " Monitored,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=True,\n", " n_jobs=4,\n", " list_of_args_kwargs=list_of_args_kwargs_for_Monitored,\n", " argskwargs2str=argskwargs2str_for_Monitored,\n", " savefig=f\"Detection_delay__Monitored__Bernoulli_T1000_N100__{len(list_of_args_kwargs_for_Monitored)}_3\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments for `Bernoulli GLR`" ] }, { "cell_type": "code", "execution_count": 316, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_BernoulliGLR = tuple([\n", " ((), {'mult_threshold_h': h}) # empty args, kwargs = {threshold_h=None}\n", " for h in [0.0001, 0.01, 0.1, 0.5, 0.9, 1, 2, 5, 10, 20, 50, 100, 1000, 10000]\n", "])" ] }, { "cell_type": "code", "execution_count": 317, "metadata": {}, "outputs": [], "source": [ "def argskwargs2str_for_BernoulliGLR(args, kwargs):\n", " h = kwargs['mult_threshold_h']\n", " return fr\"$h_0={h:.4g}$\" if h is not None else \"$h=$'auto'\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple Bernoulli problem for BernoulliGLR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, for a Bernoulli problem:" ] }, { "cell_type": "code", "execution_count": 319, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = 0.25\n", "secondMean = mu_2 = 0.75\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 387, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14.png'...\n", " Saved! 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14.png' created of size '28514b', at 'Mon Dec 17 13:22:02 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14.pdf'...\n", " Saved! 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14.pdf' created of size '18798b', at 'Mon Dec 17 13:22:02 2018' ...\n" ] }, { "data": { "image/png": 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9YP6lAQAAALBIawZFSX4jyfcm+Ylx/9ok/3VuFQEAAACwJdb81bMk39Pd311V70+S7j5QVbeYc10AAAAALNgsZxRdX1XbMhnAOlV1SpJvzLUqAAAAABZulqDoJUl+P8kdquqFSfYl+cW5VgUAAADAwq156Vl3v7qqLk7ysCSV5HHdfencKwMAAABgoVYMiqrq5Km7n0uyZ3ped181z8IAAAAAWKzVzii6OJNxiSrJ6UkOjOkTk/xtkrvPvToAAAAAFmbFMYq6++7d/W1J3pnkMd19++6+XZJHJ/mjRRUIAAAAwGLMMpj1A7v77QfvdPcfJPm++ZUEAAAAwFZYczDrJJ+uqucm+d1x//FJPj2/kgAAAADYCrOcUXR2klOS/H6SN43ps+dZFAAAAACLt+YZRePXzZ62gFoAAAAA2EKznFEEAAAAwM2AoAgAAACAJIIiAAAAAIY1xyiqqlOS/FSSM6aX7+7/e35lAQAAALBoawZFSd6c5H8keWeSG+ZbDgAAAABbZZag6Fbd/cy5VwIAAADAlppljKK3VdWj5l4JAAAAAFtqlqDoaZmERddV1bXj9sV5FwYAAADAYq156Vl333YRhQAAAACwtWYZoyhV9UNJvn/cfU93v21+JQEAAACwFda89KyqXpTJ5Wd/OW5Pq6pfmndhAAAAACzWLGcUPSrJmd39jSSpqguTvD/Js+dZGAAAAACLNctg1kly4tT0CfMoBAAAAICtNcsZRb+U5P1VtTdJZTJW0bPmWhUAAAAACzfLr57tqar3JLn/aHpmd392rlUBAAAAsHArXnpWVfca/353kjsnuWLc7jLaAAAAALgJWe2MoqcnOTfJLy8zr5M8dC4VAQAAALAlVgyKuvvcMfnI7r5uel5VHTfXqgAAAABYuFl+9ezPZmwDAAAA4Ci24hlFVXWnJKcmuWVV3S+TXzxLkuOT3GoBtQEAAACwQKuNUfSDSZ6U5LRMxik6GBR9Mclz5lsWAAAAAIu22hhFFya5sKp+uLvfuMCaAAAAANgCs4xR9I+q6sSDd6rqpKp6wRxrAgAAAGALzBIUPbK7rz54p7sPJHnU/EoCAAAAYCvMEhRtq6pjD96pqlsmOXaV5QEAAACl7fdfAAAgAElEQVQ4Cq02mPVBr07yrqr6nXH/yUkunF9JAAAAAGyFNYOi7n5xVX0wyT8ZTf+xu//7fMsCAOCgqlp7oS1y0kknbXUJAMAmmuWMoiS5NMnXu/udVXWrqrptd187z8IAAEi6e1PXV1Wbvk4A4KZjzTGKquqnkrwhyW+OplOTXDTPogAAAABYvFkGs35Kkgcl+WKSdPfHktxhnkUBAAAAsHizBEVf7e6vHbxTVduTOF8ZAAAA4CZmlqDoj6vqOUluWVUPT/L6JG+db1kAAAAALNosQdGzklyZ5ENJfjrJ27t791yrAgAAAGDhZvnVs/O6+9eS/NbBhqp62mgDAAAA4CZiljOKzlmm7UmbXAcAAAAAW2zFM4qq6uwkP5Hk7lX1lqlZt01y1bwLAwAAAGCxVrv07M+SfCbJ7ZP88lT7tUkumWdRAAAAACzeikFRd1+e5PIk31tVd0tyj+5+Z1XdMsktMwmMAAAAALiJWHOMoqr6qSRvSPKbo+m0JBfNsygAAAAAFm+WwayfkuRBSb6YJN39sSR3mGdRAAAAACzeLEHRV7v7awfvVNX2JD2/kgAAAADYCrMERX9cVc9JcsuqeniS1yd563zLAgAAAGDRZgmKnpXkyiQfSvLTSd6e5LnzLAoAAACAxVvxV88O6u5vVNVFSS7q7isXUBMAAAAAW2DFM4pq4vyq+nySjyb5aFVdWVX/YXHlAQAAALAoq1169m8y+bWz+3f3yd19cpLvSfKgqvo3C6kOAAAAgIVZLSh6QpKzu/tvDjZ098eT/GSSJ867MAAAAAAWa7Wg6Jju/vzSxjFO0THzKwkAAACArbBaUPS1Dc4DAAAA4Ci02q+e3beqvrhMeyU5bk71AACwAVW16ct290bLAQCOUisGRd29bZGFAACwcUIdAGAzrHbpGQAAAAA3I4IiAAAAAJIIigAAAAAYBEUAAAAAJBEUAQAAADAIigAAAABIIigCAAAAYBAUAQAAAJBEUAQAAADAMLegqKruWlV7q+ovq+ojVfW00X5yVb2jqj42/j1ptFdVvaSqLquqS6rqu+dVGwAAAAA3Ns8zir6e5N92972TPDDJU6rq3kmeleRd3X2PJO8a95PkkUnuMW7nJnnZHGsDAAAAYIm5BUXd/Znu/l9j+toklyY5Ncljk1w4FrswyePG9GOTvLIn/iLJiVV153nVBwAAAMChFjJGUVWdkeR+Sd6b5I7d/Zkx67NJ7jimT03yyamHXTHaAAAAAFiAuQdFVXWbJG9M8nPd/cXped3dSXqd6zu3qvZX1f4rr7xyEysFAAAAuHmba1BUVcdkEhK9urvfNJr/7uAlZePfz432TyW569TDTxtth+jul3f3Wd191imnnDK/4gEAAABuZub5q2eV5IIkl3b3f5ma9ZYk54zpc5K8ear9iePXzx6Y5JqpS9QAAAAAmLPtc1z3g5I8IcmHquoDo+05SV6U5PeqaleSy5P82Jj39iSPSnJZkq8kefIcawMAAABgibkFRd29L0mtMPthyyzfSZ4yr3oAAAAAWN1CfvUMAAAAgCOfoAgAAACAJIIiAAAAAAZBEQAAAABJBEUAAAAADIIiAAAAAJIIigAAAAAYBEUAAAAAJBEUAQAAADAIigAAAABIIigCAAAAYBAUAQAAAJBEUAQAAADAICgCAAAAIImgCAAAAIBBUAQAAABAEkERAAAAAIOgCAAAAIAkgiIAAAAABkERAAAAAEkERQAAAAAMgiIAAAAAkgiKAAAAABgERQAAAAAkERQBAAAAMAiKAAAAAEgiKAIAAABgEBQBAAAAkERQBAAAAMAgKAIAAAAgiaAIAAAAgEFQBAAAAEASQREAAAAAg6AIAAAAgCSCIgAAAAAGQREAAAAASQRFAAAAAAyCIgAAAACSCIoAAAAAGARFAAAAACQRFAEAAAAwCIoAAAAASCIoAgAAAGAQFAEAAACQRFAEAAAAwCAoAgAAACCJoAgAAACAQVAEAAAAQBJBEQAAAACDoAgAAACAJIIiAAAAAAZBEQAAAABJBEUAAAAADIIiAAAAAJIIigAAAAAYBEUAAAAAJBEUAQBHkT179mTnzp3Ztm1bdu7cmT179mx1SQAANynbt7oAAIBZ7NmzJ7t3784FF1yQBz/4wdm3b1927dqVJDn77LO3uDoAgJuG6u6trmHDzjrrrN6/f/9WlwEALMDOnTvz0pe+NA95yEO+2bZ3796cd955+fCHP7yFlQEAHPmq6uLuPmvN5QRFAMDRYNu2bbnuuutyzDHHfLPt+uuvz3HHHZcbbrhhCysDADjyzRoUGaMIADgq7NixI/v27Tukbd++fdmxY8cWVQQAcNMjKAIAjgq7d+/Orl27snfv3lx//fXZu3dvdu3ald27d291aQAANxkGswYAjgoHB6w+77zzcumll2bHjh154QtfaCBrAIBNZIwiAAAAgJs4YxQBAAAAsC6CIgAAAACSCIoAAAAAGARFAAAAACQRFAEAAAAwCIoAAAAASCIoAgAAAGAQFAEAAACQRFAEAAAAwCAoAgAAACCJoAgAAACAQVAEAAAAQBJBEQAAAACDoAgAAACAJIIiAAAAAAZBEQAAAABJBEUAAAAADIIiAAAAAJIIigAAAAAYBEUAAAAAJBEUAQBHkT179mTnzp3Ztm1bdu7cmT179mx1SQAANynbt7oAAIBZ7NmzJ7t3784FF1yQBz/4wdm3b1927dqVJDn77LO3uDoAgJuG6u6trmHDzjrrrN6/f/9WlwEALMDOnTvz0pe+NA95yEO+2bZ3796cd955+fCHP7yFlQEAHPmq6uLuPmvN5QRFAMDRYNu2bbnuuutyzDHHfLPt+uuvz3HHHZcbbrhhCysDADjyzRoUGaMIADgq7NixI/v27Tukbd++fdmxY8cWVQQAcNMjKAIAjgq7d+/Orl27snfv3lx//fXZu3dvdu3ald27d291aQAANxkGswYAjgoHB6w+77zzcumll2bHjh154QtfaCBrAIBNZIwiAAAAgJs4YxQBAAAAsC6CIgAAAACSCIoAAAAAGARFAAAAACQRFAEAAAAwCIoAAAAASDLHoKiq/ltVfa6qPjzVdnJVvaOqPjb+PWm0V1W9pKouq6pLquq751UXAAAAAMub5xlFr0jyiCVtz0ryru6+R5J3jftJ8sgk9xi3c5O8bI51AQAAALCMuQVF3f0nSa5a0vzYJBeO6QuTPG6q/ZU98RdJTqyqO8+rNgAAAABubNFjFN2xuz8zpj+b5I5j+tQkn5xa7orRBgAAAMCCbNlg1t3dSXq9j6uqc6tqf1Xtv/LKK+dQGQAAAMDN06KDor87eEnZ+Pdzo/1TSe46tdxpo+1Guvvl3X1Wd591yimnzLVYAAAAgJuTRQdFb0lyzpg+J8mbp9qfOH797IFJrpm6RA0AAACABdg+rxVX1Z4kP5Dk9lV1RZLnJXlRkt+rql1JLk/yY2Pxtyd5VJLLknwlyZPnVRcAAAAAy5tbUNTdZ68w62HLLNtJnjKvWgAAAABY25YNZg0AAADAkUVQBAAAAEASQREAAAAAg6AIAAAAgCSCIgAAAAAGQREAAAAASQRFAAAAAAyCIgAAAACSCIoAAAAAGARFAAAAACQRFAEAAAAwCIoAAAAASCIoAgAAAGAQFAEAAACQRFAEAAAAwCAoAgAAACCJoAgAAACAYftWFwAA3PSdfPLJOXDgwFaXsaKTTjopV1111VaXAQCw5QRFAMDcHThwIN291WWsqKq2ugQAgCOCS88AAAAASCIoAgAAAGAQFAEAAACQRFAEAAAAwGAwawBg7vp5xyfnn7DVZayon3f8VpcAAHBEEBQBAHNXz//iEf+rZ33+/2nv3qMlq6sDj39303Q3NDR0q6CCqCggGAODCL5QQOJoHAVFx2d8ZNRRho7RZGWMTgK+euELgyhBNKAgUdeoITKCqCQ+QRABUQR88ZA3SKPQ0ICy54/f79LF7bqPOlXdp869389arNW3bnW73evUqXP22b/9azsKSZKk9rn0TJIkSZIkSYCFIkmSJEmSJFUWiiRJkiRJkgRYKJIkSZIkSVJloUiSJEmSJEmAhSJJkiRJkiRVFookSZIkSZIEWCiSJEmSJElSZaFIkiRJkiRJgIUiSZIkSZIkVRaKJEmSJEmSBFgokiRJkiRJUmWhSJIkSZIkSYCFIkmSJEmSJFUWiiRJkiRJkgRYKJIkSZIkSVK1sO0AJEnS/BARbYcwpeXLl7cdgiRJ0liwUCRJkja4zBzpvxcRI/83JUmS5NIzSZIkSZIkVRaKJEmSJEmSBFgokiRJkiRJUmWhSJIkSZIkSYCFIkmSJEmSJFUWiiRJkiRJkgTAwrYDkCRpromIkf+bbgUvSZKkjcFCkSRJIzbbok5EWACSJEnSWHHpmSRJkiRJkgA7iiRJ0hgZZNnebN9r15YkSdLsWSiSJEljw6KOJElSu1x6JkmSJEmSJMBCkSRJkiRJkioLRZIkSZIkSQIsFEmSJEmSJKlymLUkqa9Bdp+ara4PKl6xYgWrV68e6b85yjwvX76cW2+9dWT/niRJkuYfC0WSpL5mW9SJiM4XgGZr9erVY/3/dUMU9yRJkjS/uPRMkiRJkiRJgIUiSZIkSZIkVS49kyRplvLwZXDEVm2HMaU8fFnbIUiSJKnjLBRJkjRL8a7fj/2Mojyi7SgkSZLUZS49kyRJkiRJEmBHkSTNO27xLkmSJGkqFookaZ5xi3dJkiRJU3HpmSRJkiRJkgA7iiRJGsg4dzwtX7687RAkSZLUcRaKJEmapVEv2YuIsV4GKEmSpPnHQpEkzTN5+DI4Yqu2w5hSHr6s7RAkSZKkectCkSTNM/Gu3491F0tEkEe0HYUkSZI0PznMWpIkSZIkSYCFIkmSJEmSJFUuPZOkeciduyRJkiT1Y6FIkuYZd+6SJEmSNBWXnkmSJEmSJAmwUCRJkiRJkqTKQpEkSZIkSZIAZxRJmuM2xNBm5/FoJoMcd7N9r8edJEmSNgYLRZI6a8WKFaxevXqj/+/O5sZ++fLl3HrrrRshGo0jizqSJEnqKgtFkjpr9erVY3tDPs7bz0uSJEnSVCwUSeqsPHwZHLFV22H0lYcvazsESZIkSRqYhSJJnRXv+v1YdxTlEW1HIUmSJEmDcdczSZIkSZIkARaKJEmSJEmSVFkokiRJkiRJEmChSJIkSZIkSZXDrCV12rhuQ798+fK2QxjaILmd7XvHdfi4JEmSpMJCkaTOGmXRISIsYkxiPiRJkqT5x6VnkiRJkiRJAiwUSZIkSZIkqXLpmaQ5zTk7kiRJkjR7FookzWkWdSRJkiRp9lx6JkmSJEmSJMBCkSRJkiRJkioLRdIcsHLlSpYsWUJEsGTJElauXNl2SJIkSZKkDrJQJHXcypUrOe6441i1ahVr1qxh1apVHHfccRaLJEmSJEkDi3Ea9BoRzwGOBjYBPpWZR073/r322ivPP//8jRKbNK6WLFnCqlWreNvb3nb/a0cddRTveMc7WLt2bYuRSZIkSZLGRUT8KDP3mvF941IoiohNgJ8DfwZcA/wQeHlm/myqv2OhSCpbuq9Zs4bNN9/8/tfuvPNOli5d6o5fkiRJkiRg9oWihRsjmFnaG/hlZv4aICI+DxwETFkokuaTiJjyd0uXLh3471hEkiRJkiRNNk6Fou2A3/T8fA2wz+Q3RcQbgTcC7LDDDqON4IitRvvvjdoRv2s7gqmZu+Zmmbs8fFkr/7tjnTtJkiRJ0kiNU6FoVjLzeOB4KEvPRvqPe0PcnLlrbgS5W7lyJZ/85Ce5++67Wbx4MW94wxs45phjRhCcJEmSJGk+GadC0bXAI3p+3r6+JmkGxxxzjIUhSZIkSdLQFrQdQI8fAjtFxKMjYhHwMuArLcckSZIkSZI0b4xNR1Fm/iEiDgPOBDYBTsjMS1oOS5IkSZIkad4Ym0IRQGaeDpzedhySJEmSJEnz0TgtPZMkSZIkSVKLLBRJkiRJkiQJsFAkSZIkSZKkykKRJEmSJEmSAAtFkiRJkiRJqiwUSZIkSZIkCbBQJEmSJEmSpMpCkSRJkiRJkgALRZIkSZIkSaosFEmSJEmSJAmwUCRJkiRJkqTKQpEkSZIkSZIAC0WSJEmSJEmqLBRJkiRJkiQJsFAkSZIkSZKkykKRJEmSJEmSAAtFkiRJkiRJqiwUSZIkSZIkCbBQJEmSJEmSpMpCkSRJkiRJkgALRZIkSZIkSaoiM9uOobGIuBm4qu04pvFg4Ja2g+gg89acuWvO3DVn7pozd82Zu+bMXXPmrjlz15y5a87cNWfumhn3vD0yMx8y05s6XSgadxFxfmbu1XYcXWPemjN3zZm75sxdc+auOXPXnLlrztw1Z+6aM3fNmbvmzF0zcyVvLj2TJEmSJEkSYKFIkiRJkiRJlYWiDev4tgPoKPPWnLlrztw1Z+6aM3fNmbvmzF1z5q45c9ecuWvO3DVn7pqZE3lzRpEkSZIkSZIAO4okSZIkSZJUWSiSJEmSJEkSYKFI0jwXEZv0eS3aiEWSNhTPa81Nzl1UbcXTJeauOXOntnjsCZxRtEFERKSJHVhELADIzPvajqVrarEjzZ02ln7nOc99M6sXWguApHxmzdeAImKLzLyj7Ti6yM9ocxGxAliQmbe0HUvXmLvmzN1wImIRsDAz72w7lq6p92Vepwxh4povM//YdixNWCjawOqHLAAy848RsUlXD5aNrSd39wHLMvN3LYfUGRMnd2ARsEVm/rblkMZSROwM7JeZx9eflwB/CmwLfCszb28zvi6IiAcBqyeKlN6IDi4iFmTmfRHxGOCazLy77ZjGUUQ8GXgDcAtwDnAacF9mZkQ8hHIc/qHNGMdVRBwLvDszb4iIhcAewBOAn2Xmue1GN94iYnfgJcCmwDLgD8AVwBcy89o2Yxt35q45czeciNgGeA6wPeWa7o/A1cDpmfnzNmMbdxGxDHgK8Bjg0cDdwJXAf2Tmr1sMrRMiYjmwee/ntN5fPCwzr2gvssFZKBqx2tmxN3BFZt7Q5/crgX/2YvaB6s3mi4HLKReuN036/fsy852tBDfmIuJpwH8FLq7//WLiRj0iHge8KDNXtRji2IqIQ4GnZuarImI34K3AjsCvgZuAVZm5ps0Yx1FEbAbsDzyechG2CLgO+EZm/qDN2MZdRHyaUui4GPgxcHlmrq2/+1fgsMy8tb0Ix1dEfAf4d2AT4OXAxzLzX+rvPga8IzN/32KIY6k+0bwUeEJm3hsR7wReBJxFKRh9IDO/2WaM4ywizgJ+APwM+D2wnHLztCfwwcz8XovhjTVz15y5G05EfBR4COW79pfA1sBDgV2AkzPzzBbDG2sR8Q/AkynfGxdTjr0HU/L5WY+96UXE3wN/CfwUuAv4CbBr/fObuvQw1ULRiNUnnt8DLgQWAjcCvwDOo5zoj87MR7UW4JiKiFcCnwD+E9iB0kV0IyWPmwEHZOafTjx5by/S8RMRJ1G6YC6jVP8XU461C4CnAT/IzP9lN9v6IuIjwEWZ+ZmIOBJYCxxNuZh4D/C5zPySXTIPFBEvBQ4Bvk/5fD6n/vmRwLcz85Mthje2ImJXynfBkcDuwCOALYDbgZsp57kt24twfEXELsBJmblP/Xkb4Ezg/2TmVyPi55m5c6tBjqmI2An4RGYeUJexfA3YB1gCPBd4VWa+qM0Yx1VEbAecm5nb97y2gHLD9Fzg+Zl5SFvxjTNz15y5G15EXA/skJn31p8XUTqL9qc8aPgfmXldiyGOrYi4EtgnM2+sx93mwMOAP6c8mH5TZl7dYohjLSL+J3Aw8BlKN9YOwFsoD6DvBt6XmV/vwr3FwrYDmIMeD/w/4KWUqvVOlPbu/YBnU6qzeNO+nkcCJ2TmX9Xuou0o1deHA4dRnnxCme1hoeiBNgU+lJmfjYilwDaUY+8hwKuB97cZ3JjbB9g5Im6kFNX+MTNXA6sj4k5KpwyUJZBjfTLfyJ4HnJqZ/wr3t9TeCnwJ+LuIOD8zL2wzwDG1LXAG5WnwPRGxmPJ5fRDwWkqx1++H/namdGIREYsy86Z6MfauWoD7cf2dDxPWtxvwzIh4H+U79dp6cXpXRPyC8rTY3PV3F/C1iHg3cCrwq7oM/saI+CZwBJi7KUzk7j2U3P3S3M2ax90Q6uqOzwNvj4j/S8nfPcBvgJMi4oOUBzTq73TgpRHxhcy8EbiD0vRwdES8jbIMUlPIzE/UQuVbgXdm5r9FxKuAd1GOwd/U9439fYWFotH7CaVauCgzJ5YCfQkgIt4LLG0xtnH2b8BDImKzOk/nt5TcERGPp94E4M16P+8Cslam11DWsF8BEBGHUfOIBbZ+3gkcCBwKbEk57ibsAbwPHLDex8XAfhFxGaXzbw/g+My8oD612xW40IvY9ZxNWee/KXBPZt4dEddm5m8i4kJKh4f6+yFw+8Qg64jYNDPPi4hPAZ8FTqnvc1eW9X2D8iT4iZQOtt5lA8+lLLUFc7eezLy1Lmv8a8pF/9qyko/tgHuAj9a3mrtJau6OpXy/vgW4JyISczejmrt/pjwofRulqBuUpd4Tnc9g7vqqM2E/TnlQ+l+AW+rDv80o3TFnZubtXejoaMnHgGOA/WvB4zbKsbYY+KmdWNOLiIWZ+ZWIuBR4dx0D8gjKcdepa2KXno1Q7wmn3w1SRLwZuCwz/9MbqJnFugGvrwTOycxfe1IfTES8PDM/13YcXVMH+b0zM/9327GMoyiDg/+RUvjem3KTfky9gb8YeElmXt5mjF0TZZD1wsy83PNcf9FnZ8z62tuBH9claHZjDSAi/hy4PTO/63XJ+iZd1+1JKXIsptw0XZ0OAp9RRGxOmf23A+UmPShD+89pNbCOiIg/AR5Febj/B+B3wPf9rM5OlPmTu1I6JzendGt9Lt05c0YRcQBldcJmlO7nzShzdm+a9i+q9x52R+CVwO6Z+eKufc9aKBqxerG/B/BYyuC0myhdRt9KB1jPKCIeDNxmrmbPm8rm6k3mxO56f+i5ITCnM4iyc9ITgKsmhi/XG4LXZ+ZHp/3L0ohZIJpe9Owi6rltOLWb7d6IeChlpz13KewjyszONwHXA9/NzNMnPqd1mfzdXuv1VzuxVmXmNfXnPSmdMT/IzEtaDa5D6iiLP6S7Js9aRGxLGS/wMMqy+E0oneOnZOZVbcbWBXUUw26U0TO7UpY4bg5ckplfbjO2JiwUjVCUIZEnUJYVnEnZivFBwArgKuBf0h1Z1hNlB6XnUnZy2Kz+91vKE5OvtRlbF0wuavRciD0N2LaLJ6aNZZrcPYWSu1NbDE9zWEQsybrbWf3Z4uQsTc6dZs/cDSbW7VL4E8qS20tz3S6FnwcOTXcp7CvW7VK4EHgFpev0U/V3xwJv95p4fXWJWe8uhf8AvJCyhHQv4P2Z+fU2Yxx3EbE78BLKnMllwL3Ar4AvThTf1F+s2y3uJ5Td4pZRuon+hLLM+wyvVaYWZTflF1Hu+88HtqIch0sox98FLYY3MGcUjdYzga0zcz+4vwCyNWXA9WGUzoUPtxbd+DqYchFxHuVDtYQy9PXlEfEk4CO2iE4tMzMitqd8nm/ouQkIylp2Bx5OYZrcbUKZoWDu+pimwPZ0SoHtSy2GN/bqMffsiDgZeFJmnu2F1+z0y13bMXWFuRtMlCHph1B2KXwepTtmi4i4nVI82t8iUX9RdilcnJkfrj+fCJwZEddn5leBAy0STemxwHW1SLQCeD5lvtjELoVvBiwUTe8o4AeU+aa/pyw72xH4eER8MN3efTov4YG7xS2m3JPtB6wELgKcUTS1w4D/npk/rd1FSymzxV4IvCciDsvMK1qNcAAWikZrNWVHgl0z89LMvIuyFvb6iNgSeB3wYVvk1/MC4KuZedzECxGxFfBt4HDKbnFf9on7+iLiGcBfAGuog3Aj4mpKi+j9X4QWOtZn7pqbpsAG5Zxnga2Pnpw8i/Jk+CeUi4qzzdf0zF1z5q6xmXYpfBi47HEK7lLY3Ey7FG4N5m4qEbEdsEtmPqvntQWULpnnUobSWyjqI/rvFnc3cDXuFjdb5wB7RcTP67XxWsoqmR9HxM8oM+46w0LRaH0XeAbwmXowXE/ZUnATyon/zBZjG2enAgdFxJXABZl5U11PfFE9aW1V3+cW5T1qe/IHgJMoJ/G1lGWOOwMnRITtyVMwd81ZYBvKxA41u1G+L/agbDnb+zv1Z+6aM3fNuEthc+5S2Jy7FA7nLuBrEfFuyv3Fr+o9xY0R8U3gCLDQ1k+6W9wofAg4GXhxRFxOme+0llIgui0zL2szuEE5o2gDiDLQ+gmUAseWlHWdXwdOm2jl0zq1Ne9vKSelOyg3oIsoT+7WAn9TL8w8MfWIst3iaZm506TXl1IuJl6ZmS9sJbgxZ+6aqQW2c+hfYNsfZydMK9btgnEaZaeuwyjr/b/iRev0zF1z5m70wl0KZxTuUjhy4S6FsxIRewB/TXlQP9HxvB1lpMB3MvMoj73phbvFDSUingk8jtKBui2l2LYqM69sM65BWSgaoXpi2pdy47QCuBO4KDO/0GpgHVHbRXej5G4hpfr6tcx0LWwfEbGc0hVzL/Al4DLg+npDsBdwcmbu6sXE+sxdMxbYRiMizqXMtDsDeF3XLhzaZO6aM3caF96kTy/cpbCx3sJtlN3itqPcTwRwdWae22Z8XRDuFtdY731DXa68uMvz2CwUjUhEPAo4jtJi9h3KjmcrgB0oXTJHOfSwv9qlsGDiosGnc7NXB0a+gVLxT0on1s7ATcAXMvMUL8j6M3eDs8A2vDp/7ezMfHxEXJSZe7QdU1eYu+bM3XDCXQobm5w7zZ65G4269PHeiHgosLrO3dEk4W5xG0xXr4udUTQ6B1DWsL8G7h8ItjWwE/BXwF9S1i2qj0k34wFkROwLPCjdonxKmXk58Ld1OOTDKZ/pO4FbMvPS+h4LHX2Yu8P99I4AAA8KSURBVMFl5uqI+BClwPYKaoEtIiYKbO+tb3V2wtQWA0dH2c3mKujuBUQLzF1z5q6hcJfCxvrlru2YusLcNRMRn6YMUv8JcDFwaU+x7Z+AQwELRf25W9wG0tXvWjuKRqSuRXwz8HHgwt41nBHxRmC/zHyFHQr9RZ8dlKJstb0sM0/3grY/n2o2Z+6GM12BTZK6rme202uAfYATgbfWazmvSaZh7pozd83V65LzgCOB3YFHUAaC304pHu2fmVu2F+H4quM/zs3M7Xte690t7vmZeUhb8XXFpKWPTwB+2uV7DTuKRue7wJMoT9SvjohbKBXrZZQhVp9vMbaxFe6gNJSek9HmmXln2/F0iblrZuJLsBaFLAwNqC61BdYdg5odc9ecuWvE3eKaM3fNmbvmtqXMYPtgZt5TZ8RsQxko/FrgYeCMrCm4W9wI9NxbPJQyvPr5Xc7ZgrYDmCsy877M/BDwAsrWn5cANwC3Ah8Bvljf54mpqheuHwAuBL5JydF/UI7LEyLi2S2G1xm1G+tlEbFpRDy17Xi6xNwNrrfA1nYsXZQ9oJwHe2/iNTVz15y5a2SioLYbZQnLnsCPJv1O/Zm75sxdc2cDfwdsClBnEV2bmRdR7jV+NM3fndeyzNH9GGW27luBD0XEJyPi9Pr6R+tb/d6YQkQ8tA5QB9iFMq+4080OdhSNUJ1LtAD4RpcPio1oF8oMomN7X4yyg9JllKV8brU9hZ4K9bOAvSjrsQ8Dzu5y9XpjMHfDcXbCcOqcmE0y82a7OwZj7pozd4Pp+R7YhjLQ9XHA+yf9Tn2Yu+bMXXOZeQ9w5aTXJnL2Pcr8HQDzOEntFr8IeG1Ms1ucDQ/T2gN4fUTcSCnw/jEiDqGsMDonM3/banQNWCgagYjYjLJ+c09gKbA4Im6m7DByZqvBjbcbgW9FxLE8cAelNRFxJeXL0TbHqdme3Jy5a8AC2/BqIfxNwOPq+v/fAZ/IzIvbjWz8mbvmzF0zUXaL2yIz10bE8sy8su2YusLcNWfuRi8zf9XzZwvlk/TmJDMvAC6Int3iImJxulvcTH4A/IYyTuVTwK8pHVp7UoaDf6dr81EtFI3GwZQdgM4DzqfM2tkWeEVE7A38U2be3mJ8YyndQWlYve3JJ1Fu2M+Y9Dv1Z+6ascDWUM/Fwe7Atpn56vr6vsD+lCUG6sPcNWfuhuZucc2Zu+bM3ZAiYkmu2+3MDUxmEO4WN7TMvA24DSAiFlI2uPoZZeOXG+t7OnUMWigajRcAX83M4yZeqE8Dvg0cDvwZ8GVPUutLtyhvzPbk5sxdYxbYGuo5918BXBdlV8cbgCdSdmXxRmAK5q45czeczLwJOL7+eFB9zVzNgrlrztwNp9/yeO+/plbvwQ6h7Bb3PEr36RYR0btb3K0ththFD8nMH9c/X9VqJEOwUDQapwIH1eVSF2TmTVkmxV9U5xZtVd8XeDP1AO6gNBzbk5szd4OzwDa4iXNcRGwNHEC5ST+Xsu1xUJ42TXR1+P3Qw9w1Z+5Go3fgtzeagzF3zZm7Zlwe35i7xY3eM6D7nWwWikbj34GdKEuo7oiINcAiyodsLWVHL/BibD0TH55wi/KmbE9uztw1YIFtMD0XCCdTujoArqUsVT6ntzW+yxcTG4K5a87cjcbk3EzcwJuzmZm75sxdYy6Pb+ZsyhDwTYF7MvPuiLg2M38TERdSRqpoAFkHV3f9Mxsdj3+sRMR2lJPTCkoRbjHwtcy8rtXAxtxEiyjlgtYdlKQxFhHbUOayfRE4MTMPssDWX724X5KZd0XEJcDewGOAZ1KeNu0O7GGRfH3mrjlzN1q9u8W1HUvXmLvmzN3gJq5FIuI04O3U5fGZ+RWvU5qJiMcACzPz8q53x2wMEbFFZt7R83Onc2ahaATqRdmCiXa8rh8UG0vPCf01lHb4E4G3ZuYrPKHPju3JzZk7bWgR8WjKmv+zKF2nH87MG3p+v31mXtNWfOPM3DVn7kYnym5xb6Ess3W3uAGYu+bM3XAi4lxKYfwM4HV2PmtD6lnqvR/wHOCxwN9kZmdnE01Y0HYAc8WkNZsBZVeRiDi4pZC6wBbRIWUPKCer3gKIpmbumokebcfSATcDnwN2pJzfPhsRL4qIR9ULi2vM45TMXXPmbkg9+bl/t7jMfBXwecpucZqCuWvO3A2vd3k84PL4AUXEkkk/+10xg56HzR8DTqd08K6NiK0j4sCI2LS96IbjjKIRqFXE7Sn5vKFn7X8C94CzT6bgDkoj0NuebGfMYMzd4JydMHu1/fjUiDgL2AU4EHg58Fzgsoj4cmZeMd2/MV+Zu+bM3fB6zmfuFjcgc9ecuRsJ5082FO4W11g93n6bmd+JCDLzxoh4OHBEZj697fiaslA0pIh4BvAXwBrqsK+IuBo4JTO/N/E+T1DrS3dQGlptT34T8LiIsD15AOauOQtsg8nM24Hz639ExD7ASynz7K5wufLUzF1z5m5wPUsI3C1uQOauOXM3Opl5E3B8/fGg+pr3FNMId4sbhcXAdyLiEEpXL8B9lAHhnR1LY6FoCPVJ+gco3TBXU3Y4WwHsDJwQEe/PzK+3GOLYC3dQaqTnhHN/e3J9fV9Ke7LFjimYu+FYYGtu4oIrM8+NiPOoy7+7ePGwsZm75szd7PXkxN3iBmTumjN3o9O7VMpczZqjQIaUmddHxLeAE4ALI+JZwOuBn9a3LAD+OMVfH1sWioazC/CgzDy298V6I3UZ8GbAQtH0bBFtwPbk5sxdMxbYhtd7TNVcdu6ioS3mrjlzNzv1BnNJZt5Fme/0MtbtFnco8ImIcLe4Psxdc+ZutFwe34ijQBqqx9cCyqZWZ0XEAcCrgEOAC4Cv1Ld2Mo/uejaEiFhO6Si6F/gSpTh0fZadvPYCTs7MXb3p1KhM0Z68iFLkuL89OeuwUr8Y1zF3oxERDwNeDXy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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.5 s, sys: 728 ms, total: 3.23 s\n", "Wall time: 2min 58s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}\",\n", " )" ] }, { "cell_type": "code", "execution_count": 388, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring false_alarm for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 20 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { 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'False_alarm__BernoulliGLR__Bernoulli_T1000_N20__params14.png' created of size '27744b', at 'Mon Dec 17 13:25:04 2018' ...\n", "Saving figure with format pdf, to file 'False_alarm__BernoulliGLR__Bernoulli_T1000_N20__params14.pdf'...\n", " Saved! 'False_alarm__BernoulliGLR__Bernoulli_T1000_N20__params14.pdf' created of size '18967b', at 'Mon Dec 17 13:25:05 2018' ...\n" ] }, { "data": { "image/png": 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/bffLQ7BPJgdpv9yyzvtk1pHtlzLrcPuVC+NXHYsudgyZMP2O2/tAjuur2rHFD2f2rX9vzKxz9/OSnJPZm9J1WbqP1uyKlcfO/z4uyb/LbL/6bH3vypUHZtaPzO9kFhh8IbMOdO/dF+8seKdtvLLdNetP7PQkv7TiaqWD3u4Fv5jkld299balVW2f8lr+QGZXSn25qn5oD+3Zzo5t3NK+Kc/dRTb1elixv61s44RaphwndzoXWjXvducwi5aOP4D/idue866YB4ZXO1xxDnDYqaqPJ/nl7v4/h7qWdaqqY5P8fZLrdPfWTii5FKlZHyLvTXLCQXojNxTbb7MuLdu7ZrcH/XF3v2LFdM9P8l+6+2D1T7N2y7bxqnbPr9Q7I8nvd/f/XXuxB9GlZd9i90Y5twJcOQRwWKnZffenJHmxYOjSr2ffrnIzb4b2xvbbrEvR9v7hfO921W1V1Wszu7XqtKr6pU0UdTCs2Mar2v2AJD+a5Ler6o1Vdb+1FLkGl6J9C4Ad7NRfAgCXMlV1lcwuXf9EZl9jD3CZUlXfl9ktIB9eNl3v8KUJh6sp7e7u07OL24kBYDfcVgYAAAAwMLeVAQAAAAxMOAQAAAAwsMOuz6Gjjz66jz/++ENdBgAAAMCl2jvf+c4vdPcxq6Y77MKh448/PmeeeeahLgMAAADgUq2qPjFlOreVAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMbG3hUFU9r6o+X1V/t8P4qqpnVNXZVfW+qrrtumoBAAAAYHvrvHLo+UlOXDL+bkluMv95eJI/XGMtABc5+eSTc9RRR6WqctRRR+Xkk08+1CUBAAAcMmsLh7r7r5J8ackk90rywp55a5JrVtV111UPQDILhk499dQ85SlPyfnnn5+nPOUpOfXUUwVEAADAsA5ln0PXT/LJhcfnzIcBrM1pp52Wpz71qTnllFNy5StfOaecckqe+tSn5rTTTjvUpQEAABwSlz/UBUxRVQ/P7NazHHvssQd34U+4xsFd3sH2hK8e6gp2ZtvtnW13yFx44YV5xCMecbFhj3jEI/KoRz3qEFUEAABwaB3KcOhTSW648PgG82GX0N3PTfLcJNm3b18f1Couw2+C18622zvb7pA58sgjc+qpp+aUU065aNipp56aI4888hBWBQAAcOgcytvKzkjykPm3lt0+yVe7+zOHsB5gAA972MPy6Ec/Ok9/+tNzwQUX5OlPf3oe/ehH52EPe9ihLg0AAOCQWNuVQ1X1Z0nulOToqjonyX9McoUk6e5Tk7w2yc8kOTvJBUkeuq5aAPZ75jOfmSR57GMfm0c96lE58sgj84hHPOKi4QAAAKOp7oN7l9a67du3r88888xDXQYAAADApVpVvbO7962a7lDeVgYAAADAISYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABjYWsOhqjqxqj5YVWdX1WO2GX9sVb2hqt5dVe+rqp9ZZz0AAAAAXNzawqGqOiLJs5PcLcnNkzygqm6+ZbLHJXlpd98myf2TPGdd9QAAAABwSeu8cuh2Sc7u7o929zeTvDjJvbZM00muPv/7Gkk+vcZ6AAAAANhineHQ9ZN8cuHxOfNhi56Q5EFVdU6S1yY5ebsFVdXDq+rMqjrz3HPPXUetAAAAAEM61B1SPyDJ87v7Bkl+JsnpVXWJmrr7ud29r7v3HXPMMRsvEgAAAOCyap3h0KeS3HDh8Q3mwxb92yQvTZLufkuSo5IcvcaaAAAAAFiwznDoHUluUlU3qqorZtbh9BlbpvmHJHdJkqq6WWbhkPvGAAAAADZkbeFQd387yUlJXp/kA5l9K9lZVfWkqrrnfLJHJXlYVb03yZ8l+aXu7nXVBAAAAMDFXX6dC+/u12bW0fTisMcv/P3+JD++zhoAAAAA2Nmh7pAaAAAAgENIOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxMOAQAAAAwMOEQAAAAwMCEQwAAAAADEw4BAAAADEw4BAAAADAw4RAAAADAwIRDAAAAAAMTDgEAAAAMTDgEAAAAMDDhEAAAAMDAhEMAAAAAAxMOAQAAAAxsreFQVZ1YVR+sqrOr6jE7TPPzVfX+qjqrql60znoAAAAAuLjLr5qgqn64u/92twuuqiOSPDvJXZOck+QdVXVGd79/YZqbJPnNJD/e3V+uqmvvdj0AAAAA7N2UK4eeU1Vvr6pHVtU1drHs2yU5u7s/2t3fTPLiJPfaMs3Dkjy7u7+cJN39+V0sHwAAAIADtDIc6u5/nuSBSW6Y5J1V9aKquuuEZV8/yScXHp8zH7bopkluWlVvqqq3VtWJE+sGAAAA4CBYeVtZknT3h6vqcUnOTPKMJLepqkry2O5+5QGu/yZJ7pTkBkn+an4b21cWJ6qqhyd5eJIce+yxB7A6AAAAABatvHKoqk6oqj9I8oEkd05yj+6+2fzvP1gy66cyu9povxvMhy06J8kZ3f2t7v5Ykg9lFhZdTHc/t7v3dfe+Y445ZlXJAAAAAEw0pc+hZyZ5V5Jbdfe/7+53JUl3fzrJ45bM944kN6mqG1XVFZPcP8kZW6Z5VWZXDaWqjs7sNrOP7qoFAAAAAOzZlHDoz7v79O7++v4BVfWrSdLdp+80U3d/O8lJSV6f2VVHL+3us6rqSVV1z/lkr0/yxap6f5I3JPn17v7iHtsCAAAAwC5Vdy+foOpd3X3bLcPe3d23WWtlO9i3b1+feeaZh2LVAAAAAIeNqnpnd+9bNd2OHVJX1QOS/EKSG1XV4u1gV0vypQMvEQAAAIBDbdm3lb05yWeSHJ3k9xeGn5fkfessCgAAAIDN2DEc6u5PJPlEkjtsrhwAAAAANmnZbWV/0913rKrzkix2TFRJuruvvvbqAAAAAFirZVcO3XH++2qbKwcAAACATVp25dC1ls3Y3TqlBgAAADjMLeuQ+p2Z3U5W24zrJDdeS0UAAAAAbMyy28putMlCAAAAANi8ZbeV/VB3/31V3Xa78d39rvWVBQAAAMAmLLut7JQkD0/y+9uM6yR3XktFAAAAAGzMstvKHj7//ZObKwcAAACATVp25VCSpKqOSvLIJHfM7Iqhv05yand/Y821AQAAALBmK8OhJC9Mcl6SZ84f/0KS05P83LqKAgAAAGAzpoRDt+zumy88fkNVvX9dBQEAAACwOZebMM27qur2+x9U1Y8mOXN9JQEAAACwKcu+yv5vM+tj6ApJ3lxV/zB/fFySv99MeQAAAACs07Lbyu6+sSoAAAAAOCSWfZX9JxYfV9W1kxy19ooAAAAA2JiVfQ5V1T2r6sNJPpbkL5N8PMnr1lwXAAAAABswpUPqJye5fZIPdfeNktwlyVvXWhUAAAAAGzElHPpWd38xyeWq6nLd/YYk+9ZcFwAAAAAbsKxD6v2+UlVXTfLXSf60qj6f5Pz1lgUAAADAJky5cuheSb6e5NeS/K8kH0lyj3UWBQAAAMBmrLxyqLvPr6rrJLldki8lef38NjMAAAAADnNTvq3sl5O8Pcl9kvzrJG+tqn+z7sIAAAAAWL8pfQ79epLb7L9aqKr+SZI3J3neOgsDAAAAYP2m9Dn0xSTnLTw+bz4MAAAAgMPcjlcOVdUp8z/PTvK2qvqLJJ1ZB9Xv20BtAAAAAKzZstvKrjb//ZH5z35/sb5yAAAAANikHcOh7n7i4uOquup8+NfWXRQAAAAAmzHl28puWVXvTnJWkrOq6p1VdYv1lwYAAADAuk3pkPq5SU7p7uO6+7gkj0py2nrLAgAAAGATpoRDV+nuN+x/0N1vTHKVtVUEAAAAwMYs65B6v49W1W8nOX3++EFJPrq+kgAAAADYlClXDv2bJMckeWWSVyQ5ej4MAAAAgMPc0iuHquqIJL/V3b+yoXoAAAAA2KClVw5193eS3HFDtQAAAACwYVP6HHp3VZ2R5GVJzt8/sLtfubaqAAAAANiIKeHQUUm+mOTOC8M6sz6IAAAAADiMrQyHuvuhmygEAAAAgM1b+W1lVXXjqnp1VZ1bVZ+vqr+oqhttojgAAAAA1mvKV9m/KMlLk1w3yfUy63voxessCgAAAIDNmBIOXbm7T+/ub89//kdm/RABAAAAcJib0iH166rqMZldLdRJ7pfktVV1rSTp7i+tsT4AAAAA1mhKOPTz89//bsvw+2cWFt34oFYEAAAAwMZM+bYynU8DAAAAXEZN6XMIAAAAgMso4RAAAADAwIRDAAAAAAOb0iF1quqEJMcvTt/dr1xTTQAAAABsyMpwqKqel+SEJGcl+e58cCd1UcJIAAAgAElEQVQRDgEAAAAc5qZcOXT77r752isBAAAAYOOm9Dn0lqoSDgEAAABcBk25cuiFmQVEn01yYZJK0t19wlorAwAAAGDtpoRDf5LkwUn+Nt/rcwgAAACAy4Ap4dC53X3G2isBAAAAYOOmhEPvrqoXJXl1ZreVJfFV9gAAAACXBVPCoStlFgr9y4VhvsoeAAAA4DJgaThUVUckeV93/8GG6gEAAABgg5Z+lX13fyfJAzZUCwAAAAAbNuW2sjdV1bOSvCTJ+fsHdve71lYVAAAAABsxJRy69fz3kxaGdZI7H/xyAAAAANikleFQd//kJgoBAAAAYPOmXDmUqvrZJLdIctT+Yd39pJ3nAAAAAOBwsLRD6iSpqlOT3C/JyUkqyc8lOW7NdQEAAACwASvDoSQ/1t0PSfLl7n5ikjskuel6ywIAAABgE6aEQ1+f/76gqq6X5FtJrru+kgAAAADYlCl9Dr2mqq6Z5GlJ3pXZN5X98VqrAgAAAGAjpnxb2ZPnf76iql6T5Kju/up6ywIAAABgE3YMh6rqPkvGpbtfuZ6SAAAAANiUZVcO3WPJuE4iHAIAAAA4zO0YDnX3QzdZCAAAAACbN6VD6lTVzya5RZKj9g/r7ietqygAAAAANmPlV9lX1alJ7pfk5CSV5OeSHLfmugAAAADYgJXhUJIf6+6HJPlydz8xyR2S3HS9ZQEAAACwCVPCoa/Pf19QVddL8q0k111fSQAAAABsypQ+h15TVddM8rQk78rsm8pOW2tVAAAAAGzEynCou588//MVVfWaJEd191fXWxYAAAAAmzDp28r26+4Lk1y4ploAAAAA2LApfQ4BAAAAcBklHAIAAAAY2MpwqGYeVFWPnz8+tqput/7SAAAAAFi3KVcOPSfJHZI8YP74vCTPXltFAAAAAGzMlA6pf7S7b1tV706S7v5yVV1xzXUBAAAAsAFTrhz6VlUdkaSTpKqOSfLdtVYFAAAAwEZMCYeekeTPk1y7qn43yd8kecpaqwIAAABgI1beVtbdf1pV70xylySV5N7d/YG1VwYAAADA2k35trIfSPKx7n52kr9LctequubaKwMAAABg7abcVvaKJN+pqn+a5I+S3DDJi9ZaFQAAAAAbMSUc+m53fzvJfZI8q7t/Pcl111sWAAAAAJsw9dvKHpDkIUleMx92hfWVBAAAAMCmTAmHHprkDkl+t7s/VlU3SnL6essCAAAAYBOmfFvZ+5P8ysLjjyV56jqLAgAAAGAzdgyHqupvk/RO47v7hLVUBAAAAMDGLLty6O4bqwIAAACAQ2LHcKi7P7HJQgAAAADYvJUdUlfV7avqHVX1tar6ZlV9p6r+cRPFAQAAALBeU76t7FlJHpDkw0mulOSXkzx7nUUBAAAAsBlTwqF099lJjuju73T3f09y4pT5qurEqvpgVZ1dVY9ZMt19q6qrat+0sgEAAAA4GFZ+lX2SC6rqikneU1W/l+QzmXY72hGZXWF01yTnJHlHVZ3R3e/fMt3VkvxqkrfttngAAAAADsyUK4cePJ/upCTnJ7lhkvtOmO92Sc7u7o929zeTvDjJvbaZ7slJnprkG5MqBgAAAOCg2TEcqqpjk9m3lnX3N7r7H7v7id19yvw2s1Wun+STC4/PmQ9bXMdtk9ywu//nHmoHAAAA4AAtu3LoVfv/qKpXHOwVV9Xlkjw9yaMmTPvwqjqzqs4899xzD3YpAAAAAMNaFg7Vwt833sOyP5XZLWj73WA+bL+rJbllkjdW1ceT3D7JGdt1St3dz+3ufd2975hjjtlDKQAAAABsZ1k41Dv8PdU7ktykqm4079D6/knOuGiB3V/t7qO7+/juPj7JW5Pcs7vP3MO6AAAAANiDZd9Wdquq+sfMriC60vzvzB93d1992YK7+9tVdVKS1yc5IsnzuvusqnpSkjO7+4xl8wMAAACwfjuGQ919xIEuvLtfm+S1W4Y9fodp73Sg6wMAAABgd6Z8lT0AAAAAl1HCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBrTUcqqoTq+qDVXV2VT1mm/GnVNX7q+p9VfV/q+q4ddYDAAAAwMWtLRyqqiOSPDvJ3ZLcPMkDqurmWyZ7d5J93X1Ckpcn+b111QMAAADAJa3zyqHbJTm7uz/a3d9M8uIk91qcoLvf0N0XzB++NckN1lgPAAAAAFusMxy6fpJPLjw+Zz5sJ/82yevWWA8AAAAAW1z+UBeQJFX1oCT7kvyLHcY/PMnDk+TYY4/dYGUAAAAAl23rvHLoU0luuPD4BvNhF1NVP5Xkt5Lcs7sv3G5B3f3c7t7X3fuOOeaYtRQLAAAAMKJ1hkPvSHKTqrpRVV0xyf2TnLE4QVXdJskfZRYMfX6NtQAAAACwjbWFQ9397SQnJXl9kg8keWl3n1VVT6qqe84ne1qSqyZ5WVW9p6rO2GFxAAAAAKzBWvsc6u7XJnntlmGPX/j7p9a5fgAAAACWW+dtZQAAAABcygmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAAAABiYcAgAAABiYcAgAAABgYMIhAAAAgIEJhwAAAAAGJhwCAAAAGJhwCAAAAGBgwiEAAACAgQmHAAAAAAYmHAIAAAAYmHAIAAAAYGDCIQAAAICBCYcAAID/v707j7qsrA40/uyiqCqGYhQQQURGwRiqEcEJZIotsRUUbQRxStRGuhKCyUpr0zHOSxyDAxGjoCARVoMh0DKoGEUFijAWIKBiMc+jTMW4+4/3fNThq/sN99z71bm37vNbi7XqDlXrXZt7zz1nn/3uLUkaYSaHJEmSJEmSRpjJIUmSJEmSpBFmckiSJEmSJGmEmRySJEmSJEkaYSaHJEmSJEmSRpjJIUmSJEmSpBFmckiSJEmSJGmEmRySJEmSJEkaYSaHJEmSJEmSRpjJIUmSJEmSpBFmckiSJEmSJGmEmRySJEmSJEkaYSaHJEmSJEmSRpjJIUmSJEmSpBFmckiSJEmSJGmEmRySJEmSJEkaYSaHJEmSJEmSRpjJIUmSJEmSpBFmckiSJEmSJGmEmRySJEmSJEkaYTOaHIqIN0TEdRHx+4j4SIfX50bEydXriyJi85lcjyRJkiRJkp5rxpJDEbEK8A1gH2B74MCI2H7c2/4SuD8ztwK+Ahw5U+uRJEmSJEnS8maycmhn4PeZ+YfMfAI4Cdh33Hv2Bb5X/fkUYK+IiBlckyRJkiRJkmpmz+C/vQlwc+3xLcAuE70nM5+KiAeB9YF76m+KiA8CHwTYbLPNZmq9kobBx9duewWT+/iDba+gs0GPGxi7Xhi75oxdc8auOWPXnLFrZlDjBsauF8auOWO3nMjMmfmHI94GvCEz3189fhewS2YurL3nquo9t1SPr6/ec0+nfxNgp512yosvvnhG1ixJkiRJkrSyiIhLMnOnqd43k9vKbgVeWHu8afVcx/dExGxgbeDeGVyTJEmSJEmSamYyOfSfwNYR8eKImAO8Azh93HtOB95T/fltwM9ypkqZJEmSJEmStJwZ6zlU9RBaCJwDrAIcm5lXR8QngYsz83TgO8AJEfF74D5KAkmSJEmSJEkryEw2pCYzzwTOHPfcx2p/Xgq8fSbXIEmSJEmSpInN5LYySZIkSZIkDTiTQ5IkSZIkSSPM5JAkSZIkSdIIMzkkSZIkSZI0wkwOSZIkSZIkjTCTQ5IkSZIkSSPM5JAkSZIkSdIIMzkkSZIkSZI0wkwOSZIkSZIkjTCTQ5IkSZIkSSPM5JAkSZIkSdIIMzkkSZIkSZI0wkwOSZIkSZIkjTCTQ5IkSZIkSSPM5JAkSZIkSdIIMzkkSZIkSZI0wkwOSZIkSZIkjTCTQ5IkSZIkSSPM5JAkSZIkSdIIMzkkSZIkSZI0wkwOSZIkSZIkjbDIzLbX0JWIuBu4se11TOJ5wD1tL2IIGbfmjF1zxq45Y9ecsWvO2DVn7Jozds0Zu+aMXXPGrjlj18ygx+1FmbnBVG8auuTQoIuIizNzp7bXMWyMW3PGrjlj15yxa87YNWfsmjN2zRm75oxdc8auOWPXnLFrZmWJm9vKJEmSJEmSRpjJIUmSJEmSpBFmcqj/vtX2AoaUcWvO2DVn7Jozds0Zu+aMXXPGrjlj15yxa87YNWfsmjN2zawUcbPnkCRJkiRJ0gizckiSJEmSJGmEmRySJEmSJEkaYSaHJI2ciFilw3PRxlokaaZ4XGtufOyi0tZ6homxa87YqS1+9gT2HOqbiIg0mF2LiFkAmflM22sZNlWCI42dVpROxzmPfVOrTq5mAUn5zhqvLkXEmpn5cNvrGEZ+R5uLiPWAWZl5T9trGTbGrjlj15uImAPMzsxH217LsKmuyzxP6cHYOV9mPt32WpowOTQDqi9WAGTm0xGxyrB+QFa0WuyeAdbKzAdbXtLQGDugA3OANTPz3paXNJAiYhtg98z8VvV4HvCnwEbAzzPzoTbXNwwiYn3g/rHEpBef3YuIWZn5TERsCdySmY+3vaZBFBGvBD4A3ANcAJwBPJOZGREbUD6HT7W5xkEVEUcDn8zMOyJiNrAAeBnwm8xc1O7qBltE7AC8HVgVWAt4ClgCnJyZt7a5tkFn7Jozdr2JiA2BNwCbUs7pngZuAs7MzN+2ubZBFxFrAa8CtgReDDwO3AD8LDP/0OLShkJErAusXv+eVtcXG2fmkvZW1j2TQ31QVXDsDCzJzDs6vP5XwD97Avtc1QXm24DrKCerd417/TOZeUQrixtwEfEa4L8Ci6v/fjd2cR4RLwHempmfbXGJAysiDgVenZkHR8T2wOHAFsAfgLuAz2bmI22ucRBFxGrAHsBLKSdec4DbgJ9k5oVtrm3QRcR3KcmNxcAVwHWZubR67V+BhZl5X3srHFwRcR7w78AqwIHA1zPzO9VrXwf+d2b+scUlDqTqzuU1wMsy88mIOAJ4K3AuJUn0+cz8aZtrHGQRcS5wIfAb4I/AupQLph2BL2Tmr1pc3kAzds0Zu95ExFeBDSi/tb8H1gGeD2wLnJCZ57S4vIEWEf8AvJLyu7GY8tl7HiWe3/ezN7mI+CjwF8BVwGPAlcB21Z8PGaYbqCaH+qC6s/kr4DJgNnAn8DvgIsrB/ajM3Ly1BQ6oiHgncAzwH8BmlGqhOylxXA3YMzP/dOwOe3srHTwRcTyl2uVaSpZ/LuWzdinwGuDCzPyfVq0tLyK+Alyemd+LiM8BS4GjKCcQnwJ+kJmnWg3zXBFxALA/8GvK9/MN1Z9fBPwiM/+lxeUNrIjYjvJb8DlgB+CFwJrAQ8DdlOPc/PZWOLgiYlvg+MzcpXq8IXAO8H8y80cR8dvM3KbVRQ6oiNgaOCYz96y2qJwN7ALMA/YBDs7Mt7a5xkEVEZsAizJz09pzsygXSfsAb8rM/dta3yAzds0Zu95FxO3AZpn5ZPV4DqWCaA/KzYW/zMzbWlziwIqIG4BdMvPO6nO3OrAx8OeUm9GHZOZNLS5xoEXE/wD2A75HqbraDDiMctP5ceAzmfnjYbi2mN32AlYSLwX+H3AAJTu9NaV0e3fg9ZQsLF6oL+dFwLGZ+ddVFdEmlCzrC4CFlDucUHp1mBx6rlWBL2bm9yNiDWBDymdvA+DdwJFtLm7A7QJsExF3UhJpH8vM+4H7I+JRSkUMlO2NA30AX8HeCJyWmf8Kz5bL3gecCvx9RFycmZe1ucABtRFwFuWu7xMRMZfyfV0feC8lwevvQ2fbUCquiIg5mXlXdQL2iSrpdkX1mjcQlrc98LqI+AzlN/XW6oT0sYj4HeWusLHr7DHg7Ij4JHAacH21xf3OiPgp8HEwdhMYi92nKLH7vbGbNj93Pah2cZwEfCQi/i8lfk8ANwPHR8QXKDdl1NmZwAERcXJm3gk8TCl0OCoiPkzZ4qgJZOYxVXLycOCIzPy3iDgY+ATlM3hz9b6Bv64wOdQfV1KygnMyc2ybz6kAEfFpYI0W1zbI/g3YICJWq/rj3EuJHRHxUqoTf7xA7+QTQFYZ6Ecoe9KXAETEQqo4YlKtkyOAvYFDgfmUz92YBcBnwCbpHSwGdo+IaykVfguAb2XmpdXdue2AyzxxXc75lH37qwJPZObjEXFrZt4cEZdRKjnU2X8CD401o46IVTPzooj4NvB94MTqfU5TWd5PKHd8X06pVKtvCdiHso0WjN1yMvO+asvi31BO9JeWXXpsAjwBfLV6q7Ebp4rd0ZTf18OAJyIiMXZTqmL3z5Sbox+mJHKDso17rMIZjF1HVY/Xb1Bujv4X4J7qht9qlCqYczLzoWGo3GjJ14GvAXtUSY4HKJ+1ucBVVlxNLiJmZ+bpEXEN8MmqxccLKZ+7oTondltZj+oHmU4XRRHxIeDazPwPL5qmFsuatL4TuCAz/+CBvDsRcWBm/qDtdQybqhnfEZn5v9peyyCK0vz3Y5Rk986UC/OvVRfti4G3Z+Z1ba5x2ERpRj07M6/zONdZdJhoWT33EeCKanuZVVddiIg/Bx7KzF96XrK8ced1O1ISG3MpF0o3pc28pxQRq1N6+W1GuTAPSuP9C1pd2JCIiD8BNqfcxH8KeBD4td/V6YnST3I7SoXk6pSqrB+kEy+nFBF7UnYhrEapcl6N0jf3rkn/ourXsFsA7wR2yMy3DdvvrMmhPqhO8BcAW1Gan91FqSb6edqEekoR8TzgAWM1fV5INlddWI5NxXuqdhFgTKcQZeLRy4AbxxooVxcB78/Mr076l6U+Myk0uahN//TY1puqau3JiHg+ZUKe0wU7iNKD8xDgduCXmXnm2Pe02gL/uOd6nVUVV5/NzFuqxztSKmAuzMyrW13cEKnaVDyVTjuetojYiNI6YGPKlvdVKBXiJ2bmjW2ubRhUbRa2p7SV2Y6yfXF14OrM/GGba2vC5FCPojR6PJayZeAcytjE9YH1gBuB76STVJYTZfLRPpQJDKtV/91LuTNydptrGwbjExm1k6/XABsN48FoRZkkdq+ixO60FpenlVhEzMtqSln12ITkNI2PnabP2HUnlk0XvJKynfaaXDZd8CTg0HS6YEexbLrgbOAgSnXpt6vXjgY+4jnx8qrtY/Xpgv8AvIWyPXQn4MjM/HGbaxx0EbED8HZK38i1gCeB64FTxhJu6iyWTXm7kjLlbS1K1dCfULZwn+W5ysSiTEF+K+W6/2JgbcrncB7l83dpi8vrmj2Hevc6YJ3M3B2eTXqsQ2lSvZBSofCl1lY3uPajnDhcRPkizaM0bj0wIl4BfMXyz4llZkbEppTv8B21E/+g7E23aeEEJondKpSeCMaug0mSaq+lJNVObXF5A6/6zL0+Ik4AXpGZ53uyNT2dYtf2moaFsetOlEbn+1OmC76RUgWzZkQ8REkY7WFiqLMo0wXnZuaXqsfHAedExO2Z+SNgbxNDE9oKuK1KDK0HvInSL2xsuuCHAJNDk/sycCGlX+kfKVvKtgC+ERFfSEexT+btPHfK21zKNdnuwF8BlwP2HJrYQuC/Z+ZVVRXRGpReYW8BPhURCzNzSasr7ILJod7dT5kksF1mXpOZj1H2tt4eEfOB9wFfsvx9OW8GfpSZ3xx7IiLWBn4B/CNlytsPvbO+vIjYDXgX8AhVM9uIuIlS/vnsj5/JjeUZu+YmSapBOeaZVOugFpO9KHeAr6ScSJxvvCZn7Jozdo1NNV1wY3BL4wScLtjcVNMF1wFjN5GI2ATYNjP3qj03i1INsw+lsbzJoQ6i85S3x4GbcMrbdF0A7BQRv63OjZdSdsNcERG/ofSsGxomh3r3S2A34HvVB+B2yvi/VSgH+3NaXNsgOw3YNyJuAC7NzLuq/cGXVweqtav3OU68pio9/jxwPOXAvZSyhXEb4NiIsPR4AsauOZNqPRmbLLM95fdiAWU8bP01dWbsmjN2zThdsDmnCzbndMHePAacHRGfpFxfXF9dU9wZET8FPg4m1zpJp7z1wxeBE4C3RcR1lH5NSylJoQcy89o2F9ctew71SZSm1C+jJDXmU/Zp/hg4Y6xMT8tUZXd/RzkQPUy56JxDuUO3FPjb6mTMg1FNlNGIZ2Tm1uOeX4NyAvHOzHxLK4sbcMaumSqpdgGdk2p7YC+EScWy6RVnUCZsLaTs3z/dE9XJGbvmjF3/hdMFpxROF+y7cLrgtETEAuBvKDfnxyqbN6G0CzgvM7/sZ29y4ZS3nkTE64CXUCpNN6Ik2D6bmTe0ua5umRzqUXUw2pVysbQe8ChweWae3OrChkRVCro9JXazKVnWszPTva0dRMS6lOqXJ4FTgWuB26uLgJ2AEzJzO08glmfsmjGp1h8RsYjSo+4s4H3DdrLQJmPXnLHToPDCfHLhdMHG6snaKFPeNqFcTwRwU2YuanN9wyCc8tZY/bqh2oo8d5j7q5kc6kFEbA58k1I+dh5lUtl6wGaUapgv27iws6oaYdbYiYJ34aavavr4AUpmPykVV9sAdwEnZ+aJnoR1Zuy6Z1Ktd1U/tfMz86URcXlmLmh7TcPC2DVn7HoTThdsbHzsNH3Grj+qbY1PRsTzgfurPjoaJ5zyNmOG9bzYnkO92ZOyJ/098GxTr3WArYG/Bv6Csg9RHYy7AA8gI2JXYP10nPiEMvM64O+qBo8voHyPHwXuycxrqveY3OjA2HUvM++PiC9SkmoHUSXVImIsqfbp6q32QpjYXOCoKFNoboThPWlogbFrztg1FE4XbKxT7Npe07Awds1ExHcpzdCvBBYD19QSbP8EHAqYHOrMKW8zZFh/a60c6kG1t/BDwDeAy+p7MiPig8DumXmQlQidRYfJR1HGYq+VmWd6EtuZdy+bM3a9mSypJknDrtar6T3ALsBxwOHVuZznJJMwds0Zu+aq85KLgM8BOwAvpDT1foiSMNojM+e3t8LBVbX2WJSZm9aeq095e1Nm7t/W+obFuG2NLwOuGuZrDSuHevNL4BWUO+c3RcQ9lMz0WpRGVCe1uLaBFU4+6kntALR6Zj7a9nqGibFrZuyHr0oEmQzqUrWNFlj2GdT0GLvmjF0jTnlrztg1Z+ya24jSU+0LmflE1fNlQ0pT4PcCG4M9rybglLc+qF1bPJ/SgPpNwxyzWW0vYJhl5jOZ+UXgzZQxnVcDdwD3AV8BTqne58GoUp2sfh64DPgpJUY/o3wWj42I17e4vKFRVV29IyJWjYhXt72eYWLsuldPqrW9lmGUNVCOg/ULd03M2DVn7BoZS6JtT9mesiNwybjX1Jmxa87YNXc+8PfAqgBVb6FbM/NyyrXGJZP83ZGWpS/u1ym9cg8HvhgR/xIRZ1bPf7V6q78bE4iI51dN0AG2pfQfHuoCByuHelT1GZoF/GSYPwgr0LaUnkJH15+MMvnoWso2PcdiT6CWid4L2Imyv3ohcP4wZ6lXBGPXG3sh9Kbq+7JKZt5tFUd3jF1zxq47td+BDSlNWV8CHDnuNXVg7Jozds1l5hPADeOeG4vZryj9dACM4zhVVfjlwHtjkilvFuOqBHYAAAyGSURBVDlMagHw/oi4k5LUfToi9qfsJLogM+9tdXUNmBxqKCJWo+zH3BFYA5gbEXdTJoOc0+riBtudwM8j4mieO/nokYi4gfKDaAnjxCw9bs7YNWBSrXdV8vsQ4CXVfv4HgWMyc3G7Kxt8xq45Y9dMlClva2bm0ohYNzNvaHtNw8LYNWfs+i8zr6/92eT4OPWYZOalwKVRm/IWEXPTKW9TuRC4mdIq5dvAHyiVWDtSGnyfN2z9Tk0ONbcfZXLPRcDFlN45GwEHRcTOwD9l5kMtrm8gpZOPelUvPT6ecpF+1rjX1Jmxa8akWkO1E4IdgI0y893V87sCe1C2D6gDY9ecseuZU96aM3bNGbseRcS8XDalzCEkUwinvPUsMx8AHgCIiNmUIVW/oQxvubN6z1B9Bk0ONfdm4EeZ+c2xJ6qs/y+AfwT+DPihB6blpePEG7P0uDlj15hJtYZqx/4lwG1RpjHeAbycMk3Fk/8JGLvmjF1vMvMu4FvVw32r54zVNBi75oxdbzptfff6a2LVNdj+lClvb6RUma4ZEfUpb/e1uMRhtEFmXlH9+cZWV9IDk0PNnQbsW22FujQz78rS4f3yqg/R2tX7Ai+gnsPJR72x9Lg5Y9c9k2rdGzvGRcQ6wJ6UC/NFlBHFQbmrNFa94e9DjbFrztj1R71ptxeX3TF2zRm7Ztz63phT3vpvNxj+ijWTQ839O7A1ZXvUwxHxCDCH8sVaSpnEBZ6ALWfsCxOOE2/K0uPmjF0DJtW6UzspOIFSvQFwK2Ub8gX1svdhPoGYCcauOWPXH+NjM3bRbsymZuyaM3aNufW9mfMpjbxXBZ7IzMcj4tbMvDkiLqO0S1EXsmo+Pezf2Rjy9bcuIjahHJDWoyTb5gJnZ+ZtrS5swI2Vf1JOYp18JA2wiNiQ0mftFOC4zNzXpFpn1Qn9vMx8LCKuBnYGtgReR7mrtAOwwMT48oxdc8auv+pT3tpey7Axds0Zu+6NnYtExBnAR6i2vmfm6Z6nNBMRWwKzM/O6Ya+CWREiYs3MfLj2eKhjZnKooepEbNZYqd2wfxBWlNpB/D2UUvfjgMMz8yAP4tNj6XFzxk4zLSJeTNnDfy6luvRLmXlH7fVNM/OWttY3yIxdc8auf6JMeTuMsoXWKW9dMHbNGbveRMQiSjL8LOB9VjhrJtW2ce8OvAHYCvjbzBzaXkNjZrW9gGE2bg9mQJkGEhH7tbSkYWD5Z4+yBsoBqp700MSMXTNR0/ZahsDdwA+ALSjHt+9HxFsjYvPqZOIW4zghY9ecsetRLT7PTnnLzIOBkyhT3jQBY9ecsetdfes74Nb3LkXEvHGP/a2YQu0G89eBMymVuksjYp2I2DsiVm1vdb2x51BDVbZwU0oM76jt5U/gCbCXyQScfNQH9dJjK2C6Y+y6Zy+E6atKi0+LiHOBbYG9gQOBfYBrI+KHmblksn9jVBm75oxd72rHM6e8dcnYNWfs+sJ+kg2FU94aqz5v92bmeRFBZt4ZES8APp6Zr217fU2ZHGogInYD3gU8QtWwKyJuAk7MzF+Nvc+D0vLSyUc9q0qPDwFeEhGWHnfB2DVnUq07mfkQcHH1HxGxC3AApT/dErciT8zYNWfsulfbHuCUty4Zu+aMXf9k5l3At6qH+1bPeU0xiXDKWz/MBc6LiP0p1bsAz1CafA9tyxmTQ12q7ph/nlL1chNlMtl6wDbAsRFxZGb+uMUlDrxw8lEjtYPMs6XH1fO7UkqPTXBMwNj1xqRac2MnWZm5KCIuotrOPYwnDCuasWvO2E1fLSZOeeuSsWvO2PVPfRuUsZo223z0KDNvj4ifA8cCl0XEXsD7gauqt8wCnp7grw8sk0Pd2xZYPzOPrj9ZXTxdC3wIMDk0Ocs/G7D0uDlj14xJtd7VP1NVLIfuRKEtxq45Yzc91UXlvMx8jNKv6R0sm/J2KHBMRDjlrQNj15yx6y+3vjdim4+Gqs/XLMpgqnMjYk/gYGB/4FLg9OqtQxlHp5V1KSLWpVQOPQmcSkkI3Z5lAtdOwAmZuZ0XmuqXCUqP51ASG8+WHmfVcNQfw2WMXX9ExMbAu4FfU2L43yjVf5/2WCdpWIVT3hozds0Zu/6rb31vey3DJJzy1hcRsSblZvMamXl92+vphcmhBiJiW+ADwLqUrOAcyrayu4CTM/PEiFglnzvNTBXLP5uJiDOYovRYnRm77phUkzQKqhP6vYFXsuz4djTl7u+N1XHQY1wHxq45Y9df1e6Nwyh9TN36Pk1Vm4/zM/OlEXF5Zi5oe03DICK+C9xDqZ6/Arhu7HoiIk4CDs3M+9pbYW9MDvUgIrYDXkDZnvcocE9mXtPuqoaP5Z8Tq5ceR8TVwM4sKz3ejbLdx9LjDoxd70yqSRoFETGfZVPeXg48QKkMd8rbFIxdc8auN7UbWa8GDsjMw6rndwV2zMyj2l3hYIuIDYH9gFOA4zJzX6vBJ1dd+19EqfzbAXghpWLoYUrCaPfMnN/eCntncqgBs/m9s/xzeiw9bs7YNWNSTdKoi2VT3k7MzEs875s+Y9ecsWvGre9aUSJid0pfsIMz84mImEuZwL0+8F7gNZn5imHeQWRyqAcRsboXSN2z/HP6LD1uztg1Y1JN0qiqX0iONR0d1hP8Fc3YNWfsuuPW9/6wzUf3ImIOZdfQ3Zn5SPXcrKr38HuAV2XmISaHRlBEbAq8njKC8hWZeX7LSxp4ln82Z+lxc8auOybVJEnSoHPre3/Z5qM3EbElMDszrxvm82STQ10alx3cBTgOODwzD7J0cXos/+ydpcfNGbvpMakmSZIGiVvf+8s2HxrP5FCXxsrEIuJI4HJKE6pNM/Mfh7mEbCZZ/tk/lh43Z+x6Y1JNkiS1ya3v/WObj95ExLx6ldrKcl48u+0FDKGx/+nbA8cDC4Gzxr2mmtoX5QSmKP9cGb5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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.02 s, sys: 750 ms, total: 2.77 s\n", "Wall time: 3min 2s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"False_alarm__BernoulliGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}\",\n", " )" ] }, { "cell_type": "code", "execution_count": 389, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 20 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params14.png' created of size '28800b', at 'Mon Dec 17 13:30:58 2018' ...\n", "Saving figure with format pdf, to file 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params14.pdf'...\n", " Saved! 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params14.pdf' created of size '18821b', at 'Mon Dec 17 13:30:58 2018' ...\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.07 s, sys: 722 ms, total: 2.79 s\n", "Wall time: 5min 53s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(missed_detection, \"Missed detection probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Missed_detection__BernoulliGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Medium Bernoulli problem for BernoulliGLR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, for a harder Bernoulli problem. Here the gap is $\\frac{1}{\\sqrt{2}}$ smaller, $\\Delta=\\frac{1}{2\\sqrt{2}}$, so the complexity of the problem should be twice as hard (it scales as $\\propto \\frac{1}{\\Delta^2}$)." ] }, { "cell_type": "code", "execution_count": 329, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gap = 0.35355339059327373\n" ] } ], "source": [ "horizon = 1000\n", "firstMean = mu_1 = 0.25\n", "gap = 1/2 / np.sqrt(2)\n", "print(f\"Gap = {gap}\")\n", "secondMean = mu_2 = mu_1 + gap\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 330, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.6035533905932737, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ 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style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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}, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14_2.png'...\n", " Saved! 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14_2.png' created of size '28283b', at 'Tue Dec 18 14:09:53 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14_2.pdf'...\n", " Saved! 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params14_2.pdf' created of size '18667b', at 'Tue Dec 18 14:09:53 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.5 s, sys: 1.03 s, total: 3.53 s\n", "Wall time: 2min 1s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Detection_delay__BernoulliGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}_2\",\n", " )" ] }, { "cell_type": "code", "execution_count": 331, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring false_alarm for algorithm , mu^1 = 0.25, mu^2 = 0.6035533905932737, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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'False_alarm__BernoulliGLR__Bernoulli_T1000_N10__params14_2.png' created of size '27700b', at 'Tue Dec 18 14:10:51 2018' ...\n", "Saving figure with format pdf, to file 'False_alarm__BernoulliGLR__Bernoulli_T1000_N10__params14_2.pdf'...\n", " Saved! 'False_alarm__BernoulliGLR__Bernoulli_T1000_N10__params14_2.pdf' created of size '18420b', at 'Tue Dec 18 14:10:52 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.58 s, sys: 554 ms, total: 3.13 s\n", "Wall time: 58.3 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"False_alarm__BernoulliGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}_2\",\n", " )" ] }, { "cell_type": "code", "execution_count": 332, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm , mu^1 = 0.25, mu^2 = 0.6035533905932737, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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"display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N10__params14_2.png'...\n", " Saved! 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N10__params14_2.png' created of size '28833b', at 'Tue Dec 18 14:13:00 2018' ...\n", "Saving figure with format pdf, to file 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N10__params14_2.pdf'...\n", " Saved! 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N10__params14_2.pdf' created of size '18424b', at 'Tue Dec 18 14:13:01 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.61 s, sys: 606 ms, total: 3.22 s\n", "Wall time: 2min 8s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(missed_detection, \"Missed detection probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Missed_detection__BernoulliGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}_2\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Harder Bernoulli problem for BernoulliGLR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, for a really harder Bernoulli problem. Here the gap is again smaller, $\\Delta=\\frac{1}{10}$, so the complexity of the problem should (again) much harder (it scales as $\\propto \\frac{1}{\\Delta^2}$)." ] }, { "cell_type": "code", "execution_count": 359, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_BernoulliGLR = tuple([\n", " ((), {'mult_threshold_h': h}) # empty args, kwargs = {threshold_h=None}\n", " for h in [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2]\n", "])" ] }, { "cell_type": "code", "execution_count": 360, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gap = 0.1\n" ] } ], "source": [ "horizon = 1000\n", "firstMean = mu_1 = 0.25\n", "gap = 0.1\n", "print(f\"Gap = {gap}\")\n", "secondMean = mu_2 = mu_1 + gap\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 361, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.35, and horizon = 1000, tau = 500...\n", "with 12 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N100__params12_4.png'...\n", " Saved! 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N100__params12_4.png' created of size '28649b', at 'Tue Dec 18 15:29:35 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N100__params12_4.pdf'...\n", " Saved! 'Detection_delay__BernoulliGLR__Bernoulli_T1000_N100__params12_4.pdf' created of size '20352b', at 'Tue Dec 18 15:29:35 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.74 s, sys: 768 ms, total: 3.51 s\n", "Wall time: 18min 11s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Detection_delay__BernoulliGLR__Bernoulli_T1000_N100__params{len(list_of_args_kwargs_for_BernoulliGLR)}_4\",\n", " )" ] }, { "cell_type": "code", "execution_count": 362, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring false_alarm for algorithm , mu^1 = 0.25, mu^2 = 0.35, and horizon = 1000, tau = 500...\n", "with 12 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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"display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'False_alarm__BernoulliGLR__Bernoulli_T1000_N100__params12_5.png'...\n", " Saved! 'False_alarm__BernoulliGLR__Bernoulli_T1000_N100__params12_5.png' created of size '23546b', at 'Tue Dec 18 15:36:09 2018' ...\n", "Saving figure with format pdf, to file 'False_alarm__BernoulliGLR__Bernoulli_T1000_N100__params12_5.pdf'...\n", " Saved! 'False_alarm__BernoulliGLR__Bernoulli_T1000_N100__params12_5.pdf' created of size '19260b', at 'Tue Dec 18 15:36:09 2018' ...\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.23 s, sys: 795 ms, total: 3.03 s\n", "Wall time: 6min 34s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"False_alarm__BernoulliGLR__Bernoulli_T1000_N100__params{len(list_of_args_kwargs_for_BernoulliGLR)}_5\",\n", " )" ] }, { "cell_type": "code", "execution_count": 363, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm , mu^1 = 0.25, mu^2 = 0.35, and horizon = 1000, tau = 500...\n", "with 12 values for args, kwargs, and 100 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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"display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params12_5.png'...\n", " Saved! 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params12_5.png' created of size '24903b', at 'Tue Dec 18 15:56:37 2018' ...\n", "Saving figure with format pdf, to file 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params12_5.pdf'...\n", " Saved! 'Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params12_5.pdf' created of size '19895b', at 'Tue Dec 18 15:56:37 2018' ...\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.43 s, sys: 463 ms, total: 3.89 s\n", "Wall time: 20min 28s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(missed_detection, \"Missed detection probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=100,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Missed_detection__BernoulliGLR__Bernoulli_T1000_N100__params{len(list_of_args_kwargs_for_BernoulliGLR)}_5\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Easy Gaussian problem for BernoulliGLR\n", "And now on Gaussian problems:" ] }, { "cell_type": "code", "execution_count": 266, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.25\n", "secondMean = mu_2 = 0.25\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 267, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.25, mu^2 = 0.25, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": 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figure with format png, to file 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14.png'...\n", " Saved! 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14.png' created of size '29505b', at 'Mon Dec 17 19:15:23 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14.pdf'...\n", " Saved! 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14.pdf' created of size '19719b', at 'Mon Dec 17 19:15:23 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.37 s, sys: 777 ms, total: 3.15 s\n", "Wall time: 54.6 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}\",\n", " )" ] }, { "cell_type": "code", "execution_count": 270, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring false_alarm for algorithm , mu^1 = -0.25, mu^2 = 0.25, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 50 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'False_alarm__BernoulliGLR__Gaussian_T1000_N10__params14.pdf' created of size '19225b', at 'Mon Dec 17 19:17:55 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.06 s, sys: 745 ms, total: 2.8 s\n", "Wall time: 2min 54s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=50,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"False_alarm__BernoulliGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}\",\n", " )" ] }, { "cell_type": "code", "execution_count": 392, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.05\n", "secondMean = mu_2 = 0.05\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 393, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.05, mu^2 = 0.05, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ 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}, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14_2.png'...\n", " Saved! 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14_2.png' created of size '28975b', at 'Mon Dec 17 13:33:14 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14_2.pdf'...\n", " Saved! 'Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params14_2.pdf' created of size '19241b', at 'Mon Dec 17 13:33:14 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.02 s, sys: 767 ms, total: 2.79 s\n", "Wall time: 2min 15s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Detection_delay__BernoulliGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}_2\",\n", " )" ] }, { "cell_type": "code", "execution_count": 394, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring false_alarm for algorithm , mu^1 = -0.05, mu^2 = 0.05, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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'False_alarm__BernoulliGLR__Gaussian_T1000_N10__params14_2.png' created of size '27567b', at 'Mon Dec 17 13:34:09 2018' ...\n", "Saving figure with format pdf, to file 'False_alarm__BernoulliGLR__Gaussian_T1000_N10__params14_2.pdf'...\n", " Saved! 'False_alarm__BernoulliGLR__Gaussian_T1000_N10__params14_2.pdf' created of size '18886b', at 'Mon Dec 17 13:34:10 2018' ...\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.03 s, sys: 682 ms, total: 2.71 s\n", "Wall time: 55.5 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"False_alarm__BernoulliGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}_2\",\n", " )" ] }, { "cell_type": "code", "execution_count": 271, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring missed_detection for algorithm , mu^1 = -0.25, mu^2 = 0.25, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": 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"model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Missed_detection__BernoulliGLR__Gaussian_T1000_N10__params14_2.png'...\n", " Saved! 'Missed_detection__BernoulliGLR__Gaussian_T1000_N10__params14_2.png' created of size '28537b', at 'Mon Dec 17 19:22:44 2018' ...\n", "Saving figure with format pdf, to file 'Missed_detection__BernoulliGLR__Gaussian_T1000_N10__params14_2.pdf'...\n", " Saved! 'Missed_detection__BernoulliGLR__Gaussian_T1000_N10__params14_2.pdf' created of size '18662b', at 'Mon Dec 17 19:22:44 2018' ...\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.61 s, sys: 573 ms, total: 3.18 s\n", "Wall time: 58.3 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(missed_detection, \"Missed detection probability\",\n", " BernoulliGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_BernoulliGLR,\n", " argskwargs2str=argskwargs2str_for_BernoulliGLR,\n", " savefig=f\"Missed_detection__BernoulliGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_BernoulliGLR)}_2\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments for `Gaussian GLR`" ] }, { "cell_type": "code", "execution_count": 272, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_GaussianGLR = tuple([\n", " ((), {'mult_threshold_h': h}) # empty args, kwargs = {threshold_h=None}\n", " for h in [0.0001, 0.01, 0.1, 0.5, 0.9, 1, 2, 5, 10, 20, 50, 100, 1000, 10000]\n", "])" ] }, { "cell_type": "code", "execution_count": 273, "metadata": {}, "outputs": [], "source": [ "def argskwargs2str_for_GaussianGLR(args, kwargs):\n", " h = kwargs['mult_threshold_h']\n", " return fr\"$h={h:.4g}$\" if h is not None else \"$h=$'auto'\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, for a Bernoulli problem:" ] }, { "cell_type": "code", "execution_count": 274, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = 0.25\n", "secondMean = mu_2 = 0.75\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 275, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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"model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__GaussianGLR__Bernoulli_T1000_N10__params14_2.png'...\n", " Saved! 'Detection_delay__GaussianGLR__Bernoulli_T1000_N10__params14_2.png' created of size '27449b', at 'Mon Dec 17 19:24:19 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__GaussianGLR__Bernoulli_T1000_N10__params14_2.pdf'...\n", " Saved! 'Detection_delay__GaussianGLR__Bernoulli_T1000_N10__params14_2.pdf' created of size '18488b', at 'Mon Dec 17 19:24:20 2018' ...\n" ] }, { "data": { "image/png": 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kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { 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'False_alarm__GaussianGLR__Bernoulli_T1000_N10__params14_2.png' created of size '24897b', at 'Mon Dec 17 19:25:38 2018' ...\n", "Saving figure with format pdf, to file 'False_alarm__GaussianGLR__Bernoulli_T1000_N10__params14_2.pdf'...\n", " Saved! 'False_alarm__GaussianGLR__Bernoulli_T1000_N10__params14_2.pdf' created of size '18321b', at 'Mon Dec 17 19:25:39 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.47 s, sys: 609 ms, total: 3.08 s\n", "Wall time: 42.4 s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm\",\n", " GaussianGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_GaussianGLR,\n", " argskwargs2str=argskwargs2str_for_GaussianGLR,\n", " savefig=f\"False_alarm__GaussianGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_GaussianGLR)}_2\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, for a Gaussian problem:" ] }, { "cell_type": "code", "execution_count": 277, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.1\n", "secondMean = mu_2 = 0.1\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.1, mu^2 = 0.1, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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'Detection_delay__GaussianGLR__Gaussian_T1000_N10__params14_2.pdf' created of size '17669b', at 'Mon Dec 17 19:27:43 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.58 s, sys: 641 ms, total: 3.22 s\n", "Wall time: 1min 53s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " GaussianGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_GaussianGLR,\n", " argskwargs2str=argskwargs2str_for_GaussianGLR,\n", " savefig=f\"Detection_delay__GaussianGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_GaussianGLR)}_2\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And for a harder Gaussian problem:" ] }, { "cell_type": "code", "execution_count": 279, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.01\n", "secondMean = mu_2 = 0.01\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 280, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.01, mu^2 = 0.01, and horizon = 1000, tau = 500...\n", "with 14 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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}, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving figure with format png, to file 'Detection_delay__GaussianGLR__Gaussian_T1000_N10__params14_3.png'...\n", " Saved! 'Detection_delay__GaussianGLR__Gaussian_T1000_N10__params14_3.png' created of size '26079b', at 'Mon Dec 17 19:29:35 2018' ...\n", "Saving figure with format pdf, to file 'Detection_delay__GaussianGLR__Gaussian_T1000_N10__params14_3.pdf'...\n", " Saved! 'Detection_delay__GaussianGLR__Gaussian_T1000_N10__params14_3.pdf' created of size '17507b', at 'Mon Dec 17 19:29:36 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.63 s, sys: 425 ms, total: 3.05 s\n", "Wall time: 1min 52s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " GaussianGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_GaussianGLR,\n", " argskwargs2str=argskwargs2str_for_GaussianGLR,\n", " savefig=f\"Detection_delay__GaussianGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_GaussianGLR)}_3\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments for `CUSUM`" ] }, { "cell_type": "code", "execution_count": 282, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_CUSUM = tuple([\n", " ((), {'epsilon': epsilon, 'threshold_h': h, 'M': M}) # empty args, kwargs = {epsilon=0.5, threshold_h=None, M=100}\n", " for epsilon in [0.05, 0.1, 0.5, 0.75, 0.9]\n", " for h in [None, 0.01, 0.1, 1, 10]\n", " for M in [50, 100, 150, 200, 500]\n", "])" ] }, { "cell_type": "code", "execution_count": 283, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exploring 125 different values of (h, epsilon, M) for CUSUM...\n" ] } ], "source": [ "print(f\"Exploring {len(list_of_args_kwargs_for_CUSUM)} different values of (h, epsilon, M) for CUSUM...\")" ] }, { "cell_type": "code", "execution_count": 284, "metadata": {}, "outputs": [], "source": [ "def argskwargs2str_for_CUSUM(args, kwargs):\n", " epsilon = kwargs['epsilon']\n", " M = kwargs['M']\n", " h = kwargs['threshold_h']\n", " return fr\"$\\varepsilon={epsilon:.4g}$, $M={M}$, $h={h:.4g}$\" if h is not None else fr\"$\\varepsilon={epsilon:.4g}$, $M={M}$, $h=$'auto'\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, for a Bernoulli problem:" ] }, { "cell_type": "code", "execution_count": 305, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = 0.25\n", "secondMean = mu_2 = 0.75\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 408, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 125 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 54.2 s, sys: 1.89 s, total: 56.1 s\n", "Wall time: 3min 48s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " CUSUM,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_CUSUM,\n", " argskwargs2str=argskwargs2str_for_CUSUM,\n", " savefig=f\"Detection_delay__CUSUM__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_CUSUM)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now for the first problem ($T=1000$) and the `PHT` algorithm." ] }, { "cell_type": "code", "execution_count": 306, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 125 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 51.8 s, sys: 1.8 s, total: 53.6 s\n", "Wall time: 5min 24s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " PHT,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_CUSUM,\n", " argskwargs2str=argskwargs2str_for_CUSUM,\n", " savefig=f\"Detection_delay__PHT__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_CUSUM)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, for a Gaussian problem with the same gap:" ] }, { "cell_type": "code", "execution_count": 307, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.25\n", "secondMean = mu_2 = 0.25\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 308, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.25, mu^2 = 0.25, and horizon = 1000, tau = 500...\n", "with 125 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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7GZXU+LeXPx7q+D3oXtm1+tRTT907R1J+zqTTTjuto98zbHN6Na8It/e1HmN4nA5lUs6rVPm/E5iIGqOEx9wwUmZmZrRlyxZt3Lhx72vbtm3T5s2bdemllw6wZO3ll8dtKBqe2m+Li4uan5/Xjh07ND09rbm5uaHtFBhHo3YOj1p5JbV+vELa7xELrCwjtex3wiNCQF7KtXrz5s0699xz9etf/1oHHnigTj311P0m325uszS3Vxq/56STTtr72vnnn79fndCvR836FVP2OtBsEI971fnYWBWPNfOY23Cq8jG3oepMMrNTJL1N0oSkf3L3vymKpzNp5RnJOX/4wxY5o3YOj1p5G4ZlNCCGQ9kfyMOmk/kxAKm6a3VZR0x+BNRJJ52k888/v+WcSSnncFkMnUkYFcPU8UJnEspU2Zk0NI+5mdmEpL+TdLKkqyRdZGYfc/dvD7ZkK8AI3QGdnp7W5Pzt93ltMnt9aCW8lyl3CzEeRu0cHrXyNtAwQd71L9kjKf/4wfB2hErLS7M30JGEMo05EfMjk1rNidhrR3ujw2jz5s17/201p1fKOZyyNHirDqdupOyHRzUx6obpHE4pyzCVF13KT8I1yC9JD5f06dz/Xy3p1UU/c8IJJzhWlvPOO8+PP/54P//88/2WW27x888/348//ng/77zzBl20rr34xS/21atX+5ve9Ca/6aab/E1vepOvXr3aX/ziFw+6aOiDUTuHR628ALASpVyr161b55L2+Vq3bt0++4k/Ddr/v9V+mvfRqVa/IzWm1eudxAJVKDu3ejnHO42pShXHhOEkabtX1IczNCOTJB0t6crc/6+SdOKAyoIhlb8j1pjzZ9Cr3PTq3HPP1dlnn60zzjhDkvb+e+aZZzI6aQyN2jk8auUFgJUo5Vq9/wg9qZtRejt37tzvMbJBqmr0EjAowzRCJ59PRY+D5v8ddJkxOEMzZ5KZPUXSKe7+/Oz/fyTpRHd/cVPcCyS9QJKOPfbYE6644oray9qzlDl0+hnTzSNsKfspi6nqmFJUUd6UmArKa2a66aabtHbt2r2v3XzzzTr44IO5OFdlHHOuzmNKMUI5hxqQc73FAJ1KOK+SJgyu6lqeKGVi/LKYlEf3RmoCfoyMTs+9djGd/i7OYfRiLCfgNrOHS3qtuz82+/+rJcndX9/uZ5iAG+NgzZo1et3rXrd3RJIkvfnNb9aZZ56pXbt2DbBkAAAAAIBxMZYTcEu6SNI9zex4ST+W9HRJzxhskYD+O/XUU/XKV75SknTaaadp69ateuUrX6nTTjttwCUDAAAAAGB/Q9OZ5O63mtmLJX1a0oSkd7r7twZcLKDvGvMinXnmmXr5y1+uAw88UKeddhrzJQEAAAAAhtLQPObWDR5zAwAAAAAAKFflY26rqtgJAAAAAAAAVgY6kwAAAAAAAJCMziQAAAAAAAAkozMJAAAAAAAAyehMAgAAAAAAQDI6kwAAAAAAAJCMziQAAAAAAAAkozMJAAAAAAAAyehMAgAAAAAAQDI6kwAAAAAAAJCMziQAAAAAAAAkozMJAAAAAAAAyehMAgAAAAAAQDI6kwAAAAAAAJCMziQAAAAAAAAkozMJAAAAAAAAyehMAgAAAAAAQDI6kwAAAAAAAJCMziQAAAAAAAAkozMJAAAAAAAAyehMAgAAAAAAQDJz90GXoWtmdp2kK3Iv3V7Sz0p+bBxjhqksVcUMU1mqihmmslQVM0xlqSpmmMpSZ8wwlaWqmGEqS1Uxw1SWqmKGqSxVxQxTWeqMGaayVBUzTGWpKmaYylJVzDCVpaqYYSpLnTHDVJaqYoapLFXFDFNZqooZprJUFdNq+3HufmTJftO4+9h8Sdq+EmOGqSwc0+B/D8fEMXHcw1cWjmnwv4dj4rg5puErC8c0+N8zbDHDVBaOafC/h2Pq/zH18sVjbgAAAAAAAEhGZxIAAAAAAACSjVtn0j+u0JhhKktVMcNUlqpihqksVcUMU1mqihmmstQZM0xlqSpmmMpSVcwwlaWqmGEqS1Uxw1SWOmOGqSxVxQxTWaqKGaayVBUzTGWpKmaYylJnzDCVpaqYYSpLVTHDVJaqYoapLFXFpOyjayM9ATcAAAAAAADqNW4jkwAAAAAAANBHdCYBAAAAAAAg2epBF6AuZtboOHMveLavEefut3WzvcoYoBtmZlKc6N1srzhmVUoeqCAv64wZBWZmo1z+UZNyng8Tcq565Fy9RinnOsmDOtqNtC3RL7Qt948ZlzpOop6r2yjVc2VWxJxJZRedUZVdxCz7720FF7paYkZFJxfM/HG7+55WP1tnzKgwsyPc/RclMaV5WWfMqMkqonxDZuiOr5PKsux8T8mHqmJGETnXf+OUc3Xm0xjn3Nid4xJty24NY9tynNqV0nDVc2Oc/9RzfYoZVyulM+l8SXeR9C1JL3D369rE/Zuk4yR9TdKcu1/TyfYqY7K4iexbVwe93ma2QdI33f2GOmOaGgUtL0Apx9SvmEahctv3XjCzSnbS3Xeb2V0l3dTqPDGzx0j6maQbaorZKek6SfeVdL/s/5dLusTdb2w6lkMlTUt6gKSfS/qBpO+6+81l21P20UHMGknfk7RL0tfd/Q+bjy2LK83LmmNKz98srufzs9t9mNlaSfeQdLm739TiZ05193M7Oa7EvK0yZpW732pmaxvnZkpeFm2XdGvZPlJisvw/XJFv95V0jCJPvydpKXeOr5Z0JxXkZc0x5NyQ5NwA8qmjnJP0qypypaqYUc25LC7lHK+t3UjbMrarj23LNteIpDqsxrblAZK2l+zjW0o7x2lbtolJ2UcWRz1HPTey9VyKFdGZJElmdi/FBepfCxL58CzmQZL+qfHhpm6vKqbxR1abMj5J0ick/beiQrlc0qWSLpG0w92vNrMLJP2epM/WFPMid/9Gm/I+w93PSzymNTXEvFJxDlzWYtsLJX1U0qcUldovJF0j6TuKyuKTkj4k6a8lvb6mmAslPUTSDyV9U9IRkg6SdLWkdzUqLzP7XUkvlXS9pG9IOlzSpKTvSnqPpMcUbXf3/y3bR2pM7v28g6Tj3P2iVp9FFpOSl32LyS6mR7n7j9v8zDPc/bzs+yrO4f9sVVF3sI+zJb1MkYd7JF0paYekJUlHSnqpu6/PKrg7FBzXsyRtKzpuSR9UyXuTEPMWxXn8bW+6m2hmr5J0rrv/3MweKekad/9+i328UHG+7yjY/lFJdy/ZR2rMmYpr8xcVOTkl6RBFp26jvLOSnqeCvKwzJncM5Nz+MX3POUkPk3RHd7+q3TGpmnyqJOck3UfV5Ao5V5IHdbUbU2JoW/YUU3YduV7ST0vO87ralnfMfnXRPu6shHOctmVpfrfcTj1HPdciZmTruTIrpjOpwaJne52kn7v7LWath6VZ9HIeJukG95Z3Hgq39xDzYEmfU/RW/kTStxUX1q9mP/Jvkp6exfyR4sSclnQ3SbdT3JE4VtL6mmKOz8r1HUXF9X3FxehLipNyi6T/k3BMszXEHKSoULdLWpOV9/Lsta9K+lfFBeESSU9W9IRPK3ps762ooB+luBOxVFPMDyU91t0vM7PbKS48x0p6gWLOs83u/jMz+5qiIrhQcYE6VNHzfZqkWyTNSHpxwfaXKRo6RftIinH3a/PnupkdlcX92N1/pRZS8rIfMdl7/HEVnL/u/huJeVl2fn5K0h163MdXJH1E0iskPVBxrswo7i48UdIH3P35ZnayovJqd1zvUjQmi/L25WXvTUnM0VlZt0m6veL8uDo7pm9K+jvFOXRQ9ll8Q+3z8tCS7fdX5EqvMQ9Q3JU5SpLM7GBFo/Yekl6kqPT/JDvexxflpaQv1xWTXQPIuQHlnKTFsmNS7/lUVc7dRXFHtI58GuucU05iHvSr3Ujbsr9ty7LryEcV14Wi87zOtuWVimtT0T7+S2n1Cm3LhJgW2x8j6jnquTGr59pZSRNwH6GoKB+oGMp1tZl9oLnnLfuAT5F0gqRbJV1pZh9q9HyWbe81RtJDFZ0dL5H0cEVF8AzFxfp+kr6uSLZ/l3Spu38+219j2OKzJf1xjTF/KekCSWdkxzIt6cGSHifpkYqL10MlXaw4edsdU8px9xrzAEk3STpJcYG8m2J43z0lPUVxQbpN0lbFUMpvKi4Ojc/syOw4bqwx5nuKilXu/nPFH2NXSPqimX1HcedGirt7/+vuuxQXVSnu6HwuawzcXLL9sIR9JMWY2XVZ4/Xe2ft+iKIX/ioze5+778wdZ2le9jNGUQF/TNGIeYhan79S3JHp9fy8Ltv+MkkndrmPKyV9XjG3xAWK3Gsc3/u0fA6dUHJcP8q2vzx7vdVxr1dU5i8teG+KYn5Tcb4+VnFn62jFnaK7S3qh4i7Obdl58m1FXh6lqFzzeekl229R5HMVMROSLjKz+7v7Nz3u9N2kOJ++LOk77v4SM7tc5XlZW0yjYUvODSznes2V2nJOcb7XlU9jm3O5cyAlD/rabqRt2fe2Zdl15HLFef7birxrdZ7X1bb8muKP6bb78Bj5lXqO07YsiGm3XXGdpp6jnhuLeq7M2HcmmdmEu+9RVE4PU/QS/lgxxPZxZna6u/8wF/ckSU9TDO/7vuLE/R0z2+zuVxRsP13S1SX7KI1R3OH4nKQfuft3smNY7fEc6DmKZzMvMbOXKJsHyJZX79hjZjdJ+q8sZnPufehXzEWK3uEb3P3jiuRvxP6TpKsUveCfk3RF7phWZX9InqNo8PxE0vl9jnm3YjTGoZJ+4PGo26ezmCdK+hN3/6WZvSn7jJodJukjWcwbvfWw2EpjFHcU3m9mFyruav1YUemulXSLu/8ki/8rSf9mZl/IPpMrFBXMhKL3+wUl2/8nYR+pMasUw2Rfq+hlPz/7/6mSHmxmr1CcL4V5KenKGmLulb3HNzbO39wf5o3zV5J+qpK8zGLOL4g5TjG89Up3/3aX+7jV3d9vMYS62WWKXJTijshOtc/LtYo7k78oyNtvK861otwuijlPcS4crug4ulpxziirqJ6WhV4v6RxJh7r75YoKLp+Xr5L0TkmHt9n+J4pK8O8K9pEU4+5XmdkHJX3YzLYrrs0/yz6b4xrll/S3KslLi2HjdcVMiJwbZM7dVXE3sttcqTPnKsmVqmJGOec6yIO+thtpW/a9bXmsYjTQj3LXkfz2gxR/fB9WcJ7X1bb8N3d/Q+P9bLWP7Ps3iLZlLzGb3f1/2m1X1HHbRD1HPTfC9ZxSuftYf0mayP79hKTfatr2HklPbYr7gKTfb4r7R0nPKdn+bMVFz3qNyb63Fsfy15KelH2/qs3xHihpXcl70teYRtmz8m5oVd6mmJbHVHPMyZJm8+dCq320+3+fYw6U9IeSXq1oAHxYMWzyvk3HNyXpuYo7em9XPB//hdxnULi9ypgsbkeLY/uyYkRYaV5qOVf6EpO9lr8GNL/vfy1pQxbXMt86yMvCmA728eSimJJ8LczLsu0Vx5ycciwqz8vC7Z3GZJ/1hKLR/XuKhsJZkt4n6c8kHZE7d0rzss4Ycm64ck715lMlOaeK82nMc66TfOp7uzElplXOdZALtC3b15dF15na25Yp+8i9z7Qtu4t5WtnvKck36rn+xFDPVRyTch6smDmTzOzPFUPg3qHomf2JmX1eseLFBbke479QDFM7R9JP3P0XZvYJSf/g7h83s9cohum3275KcWIU7aMo5j2KYaobFM9eXqlYzeDy7O6zKYbL3k4xHLARc7m7/zR3vKvqiGncnSl579sul5jtf5WkPXXFuPutza+XHcMgmJkpLlC3Zp/9YZIO8DYrtGQ/s1ZRIR+oOM93d7K9qpjs7sZbFXfs3qm4KyLF3YX7NT6jlLxU5Eq/Yi6Q9O7sONYo7oh8y2N4+N5z18wOUeTrI7NjapWXa0piDlE8393LPqz5/G2l7JxOzNueYtrlffPrBXGNO7+F21P2kRrT/Loi51Z77rnxlLysMyYXS84NKOfqyKeUmJScqzOfxj3nsviUfPpzdd8mpG3ZRrvzK7f/vrUbU7anHMMgdHqOZz9D27J1TLvf80bFyKqHiHqu0hjqufrruTIrqTPpIEnzip7B3Yrews8oLgi35OIOlfQWxdKTv1A8e36JpJd7LFFauD1lHwUx31JU5g9UXBgbJ9wtkj7h7v9lsUrTXymeFf2M4iJxiOI56g+5+8V1xhS83xOSjnb3H+Ves2wfyiXhEb7v8/61xTSVt22jZBBKGkmmSPpfZ/+/q9ovE3uy4nHWdsvEnizpWhUvNZsc4+5fz712O8UjSq5oNN5P0eh9V64iLc3LPsY8TpFjOxWPAEwoJlCcUKx09vHs5w6V9CYtP8PdKi8LYxSN+J724e7/1fSe5yuDO0k6yGPYdVut8rKT7VXFpDayy/IyJW87iTGz31c0Tnd4bv6FLOZMxfn783b7UEwce0tZ7lYV07gG5F4n54Yo5+rKp5SYxAZ8pfmUEjMGOZeSB/1sN9K2rKnd2Gm7svEztC3Ht23ZZntjBbSjFfMmUc9Rz410PVfKOxjONqpfioT+A0XP7BMl/b6ke7SIm1I0dg/O4p+t6FVelbK9gpgnSzo/i7l9Vu4HKGZ6v0gxydsGxaz7qxQrHdxdMbHayxRLuj6lzpim92+VloeY3k+xnKek1sPkFM/ffrDks6s8RlEBPiT7/qiCn3lA9m/bIdn9jFHcQdjvPM22vVCxRKcUjcnLFZMzflJx1+b5ivmhLsi2FW1/XMI+kmIan7diIsr/T/Hs7SmSHiFpqtO87HPMKxVzEEjLd3burRhmvSTpGdm2J6k8L8ti/raCfTy9cf62OB+eLumMRh52kpdl2yuIOVjSo7LvjynIgcK8LNvea4xipZs9inkFvqGYrPJjks6W9EzFH2arlJCXdcbk/k/ODUHOqf/5VEnOqc/5tEJyLiUP+t1upG25/+d0V9XQtmzeriFrW7bbLtqWlcS0265Ybp16jnpuLOq5lK/kwFH8UvSmSlGRvqlp2wMkPSv7fjL792GS3tEUd7yWn41tt/0pCftIiXmLpPe2OZanK56t/ANJH24T8yTF8461xWTfFz3zu1rRWHisYlWO5ylm4m88H7q2zpjs30dIeq/iovrGpvI2/sCeViyDuVbSfN0xiovkLsUyqd9QPDf+LsWynY9SDJ89LIv9H8XSlA9QTDz3l4p5E/5LcTfyipLtd07YR2rM7bMynaPsueXc8f6R4q5NaV7WFPOqrOytnjN/oaR/yr5/hsrzclNJzFJ2zrX6Xan7+JfsOM5QPNv8asWz14eXXQsH8dU4VklPkLSgaHi/rUVcIw9a5mXZ9qpiFNeKLyhWsDhK8UfYHykm+/yCYmWZlLy8Y40xh4mcI+c6yDnVlE9jnnMpeVBFm5C2Zftzva5246EJ++gkF/ratkzYTtuy95jnlWz/e8UjeNRzffgS9Vwt9Vwnn8m4r+Z2HzObk7RR0o/M7AzFc6KfVvSGH5HFTZvZn0p6jKRrzOyZipUxtilOgsdazMjebvtDJH3XzP5vjzFrJMnMLpH0KcWy8NcoHgk4RXHCfVLS71nMBv8RxYoGP1YMnztJsSpFXTE/N7OHS5oxs6MVM9N/T9KSu9+cvbfPUVS+P5T0TcVKPg+UdKKZvcfdrzWz2TpiJP0sG8L3QMVdkAcpGgmy5ZVZTDF09jey47mfYjnKWmMUy1wWLjXp7jdaPG+9VT0sE6tqlqNtLDf7ODN7leIu4wlm9puKc+JzigrtbUrLy2/VEDOhuJN7sZl9UdGg+bniInuylpdu/ahi9Y6ivPyIpCcUxJyvaNR/zcy63cc9FKuWLClW17idovJ6iJlt9WzYr5kdrjhX7qs4v/fJy7LtKftIjLHs/XuA4s7jQxXzIORzQFrOg5Z5mW1ru70pl7qOyT7/tygaUNdk733zCiH30hAtN5tdA8i5AeZcjflUVc71nCvknD0oIQ/uU0GbkLblgNuWipXwyvaxSum50Ne2pZbry3bbS/OJtmVpzL3N7AkF23+p2WOsmQAAIABJREFU6BygnqOeG+V6LvkR3bGeM8liorZJRc/s9xV3IO6jeONuVCzX90UzW6O4+/B3kq5WNHjvrVgZYLdiArYvFGx/lSJBD1f0mncd4+4fNbNHKJLjDooezhMk/bOkhcYHrLggrc+2317SiYo7Plvc/ac1xRyVlf+LiuSYUgzpvE7Sue7+czO7TNLj3f0yi+ecD1Ess/qC7PN4iaQv1RTzMne/xsy2Kp4bnpa0293fZsuT/U54LE37BklfV0xUd7S7n5XbVkfMXRWJ/klvWp7RlpeafIzt+0e5muLuLuml7v4Sa/Eccdn2bmMUd27fqZjws3GReqDiLtasoqItzEvFyhwHKM77fsfcIOnhivN5StKMYoLgD3nuueGscXuiivPyYSUx3e7jY5Je6O53zmJWZ+/z3bL3dI+7n56V8+2KyqxlXkp6TdH2LG8L95ESI+kX2fl9nuLa+XhJl7n7u23fyQsbedAyLxX1VNvtTXnbU4zasJi3Ya3iEYyTJf1Hu7xU/OFRS0zjGqCov8i5AeRcFblSZ85VlSsrPOcOULTfivLpQlXQJqwqhrZl1zEnSnpsyT6u7yAX+tq2lKSS7UcrHhWjbdl9zCskfbXo92R/Wz5Qcf7cUdRz1HMtDHk91/YasB8fgiFr/f5SNmSx6bUD1PS8teIC8buKHsJjFROmHazlIXWF2yuOOUS5oZ1tjmudoke6aB6SvsRk5bsmt/3grLy/qRha+3ZFg+ZTkk5ss8/vKHpI64q5c/b9f2bv+/uULenZOBdyn/XHFHd0/l5Ny0zWGdPmWE5WbjlKaf95A/KvlW2vMiZ77VBJr1MsN/lb2XneKgdL87KGmMZncKCiwjq03fueO+/L8rIwppt9KIbx/mv2fq5pij1S0vcS8/IdJdvfrmhIVBHTeMb9guy9/bik32hxfjY+g5Z5WbY9ZR+Jv6fd/BtJS6NqAMvNNmJEzg0k55RWD1aVT5XknGrKp3HPuQ7yoM52I21Lr7zduC1hH53kQl/bjSn7SM0n0bYsjCnaLuo56jkfj3ou5WvcH3OTmR0o6blmdq3ijs3VipUBftUUZ4oL1W8phi7ukfQrRa/o35VtT9lHaowkufsvFb3syu6CHeXuV+bL7DEj/M4sZpViCN3OmmKOk3SRmd3f3b/p7jdJuknS1Wb2ZUnf8bibcLak95vZhYrhiD9W3JVeqxhK95MaY67ODmedYpnO4xTDluVZ5vhyD/IdFcMV76WY4G7vtjpiFEMm95P1cn8297m07DVuHE/z9+1eqyomcztFo+YERcV6mKQDzWzJ3f8hO47SvKwzxuMuUWMFk7Z3EhLzsjCmy33skfRBxYoc3zOzqxR3wBrPYH8q+9EpFeflZSXbv6N4Zr7nmCz/TdIh7n69mR0laUd2fPnzs/Fet8tLL9meso+kmFbc3XN3oIqWgP1s9r21yst+xGTl25PdMSfn2sT0MeeOUE35lBKTknNV5Qo5l5QHtbUbaVv2p92omL+ncB+5wxmKtmXR9pR8ymJpWxbEJOyj8blTz1HPtTQK9Vy7sjcb+84kxcVnWjFkb1JxAbrNzC51961Nse9RnAx3UDw7+CxJX8gaDla0PWUfncRYDEc1d781i/m/kp7T7oRT9IaerXj2sp0qY/5CMUHehy2ee/++4rnWW5U1BrLYLyiGfz5JMQR0RvFZrFJM/CZJn68rxuIZ7GsVd12O9BZLMZrZwZJuc/ddZnZXd/9B3THtLkBFF51hkJXth2b2OkXuHamo+J8m6X+zmFVKy8vaYrIyTbj7bkl3MLO13mIp1JS8LIvpdh/u/hwz+4jij4XjFA2p2ynmHXhvVsQrVZyXX5H07wXbL3L3q8zsg73GZOU5VtKlFvMvrHH3X7c6f8vyMjFvq4o5WNKDPYapH+PuV+Ya8qV5WWdMVl5ybkA55+6/MrPCerCqfKoy52rOp7HLuUxq27K2dmNKDG3LzmLc/dtmVtauNMVjQ0PRtiza3uE5PjSGsJ5Lyn/qOeq5XMwo1nNJxnrOpAYzazyveqTi+eynKJ4j/Scr6Cm2eJbx9939Ra3iyrZXGZPFrVYMqb2vYvjqTsWdh2+4+y/MrHEnpZYYj8l8D1U8f31XRe/13RXDIre4+05b7nWdUFygDvCYgCx/XLXFNOIUDYET3f2CVsmTXaB2SXqCx1wDA4nJPvMHuftFZnZUq+PJ7ecB7v4NM1vnTXf/UrZXFdMmV94g6Zvu/t7c51Sal/2OUTy/flu+vGb2dMUjkW8uyse6We75ZYs7Sqt9eTLS5tiyvCzbboqhzT3H5Bo593L3He0qq7K8TMzbrmNy58oTFEv8/pXi2fGXtihraV7WHEPO9UFqztWZTykxKTnX73xKiRnlnMviOm5bWo3txpSY3PHStmwRk7KPfKyGpG2ZsJ22ZY8xik7bdtsnPTqQ8mWlnqOeG7l6LsWK6ExqZmZ/Juln7r7VzFa7+61m9jLFJF5LiuFpF0l6seKZ2Jea2Z8oZulv3r5G0suzXW9us4+UmMMUowvuJ+kuaj3DffOqE0coJtf7saT3eOuVKfoZ05h0zBTPta52918kvP8mFQ//qysm5YI+iJjcRegRkl6kmDTtVe7+iqb4Rty04o7erKQ5d59L2d6nmE8r7mp8R7FiwGWKuRxe5u5fbnURzn4+n5ftGsdVxfyNIucuUzRsd2blPd/db8jF9bzyRBX7aC5/rnyrFKd3vpFYmJcpeVthzD7vf7vPvtVxFeVKP3IydzyvVgwDv0bSSe5+Wv5Yy/JyQDHk3IByruZ86kvODaoeHMWcKzm2P1OcR+cqHnN5ibpvE9K2LDjP27z/dbUbU/YxNG3Lppyjbdmnei7bfotiBbW7Kx41pJ7rTwz13IDquVbG9jG3xgdpMVzuDopnnb8p6VJFb2djbprGCfH57N/jFEuOnpO99pe57d5qe3YirCraR0LMHsWzoo3Z64+UdLykB5vZuR5D5l6l9qtOvM3MXlJnjLtfJ+2tUBvP4JZWsmUJ388Yy+565ba3qjD2uSgNKMak0uUo83FdLRPrFS5ZmzuOFyqGg99b8bz+4yT9g6QLU/IyyxP1OeZ5ihVArlAMo51S01Ko2bH8lfZdVaJVXhbGaP/V0zreRxYjy4bJKv5guIs3PQ8vLT/n3C4vy7an7KODmNtavd6sLC8T87aSGEn31/IKIV9phDZ2ofK8rC2GnBt8ztWcT5XkXJ35NG45l5JPFbUJaVsWnOet9KvdmLJ9mNqWJdtpW/YW88asLO83s1bbb1NMFr2k6Oy6najnqOfCyNRzLcpeaOxHJlkszXhfxcXnHopnbN8j6W3eNAl37mcao5Xa9XIXbu8i5hBJl7v7UdnrByuWeb2HotfwOkXP4b9Leo27f7XFvr6jmHTxXXXFeExeuFrxDOiFZnZHd/9pm2OtZRhsq5jc+/wcSRsUk7l9XtKX3P3a7GcaMY+X9FjFhHnbJF2Yu9jWGZO01GQu7g3qYplYr2g52lYXHzM7xGOyv1afUWle9jGmsTTqHT2eBZ9U66VQU/LyzxRLkraL+YXiMYNe9nGdpDOyc6Z0mGy2j8K8TMnbXmOy93XK931kYJ9HOIvyssO87SkmXzYzu0DS7yjOoz9390ty24Z6uVlybjA5V0c+pcQU5Vyd+bQSci4lD5o/N6+23UjbUvW1LVtt7zAX+tpuTNlHth/alhXEtNn+r5Ke6+5HZ/ugnqOeG+l6Lpl72rJvo/6leM653bbVkp4q6V8UvYavkPRMZcuBlm2vIOaJkj4h6f4tyjapuOhIURH/QDE54aslPVvSkyVtUjxvXluMljsiH6EYQn17SW9sKnsjZlqxpONaSfMDjLlOcefg2Yq7C29TDEO3XMxPFBP6naoYvTafHfOqOmOUsBxl9n0jrutlYquIyb3/95D0ZsXKCv8iaauks9R+ad22edmvGCUshZp9fxeV5GVCzA8r2MflWl4K9dWKxxmeKmlr9tpELr4wL8u2VxWjGJr8V5L+WXFu/46kI9p8LoV5Wba94hiT9PXs+wslHdhU1tK8rCtG5NzAc0415VOVOad682mscq6TPFD/2420LfvcbkzZRwfneS1ty4TttC0rjMlvF/Vc32NEPVdLTKdfY/mYW66n746KobOnKJY8vFpxof2yu2/P3dl5uGJVhgslnS7pM1o+Wb9atN1iuVD1GqMhWr0iMcYsRhemDKXr+zDYdjFmdoykJygqoP9w989ZTAL5ZUmPUtwxn7KYqPK7ki5w9w9YPGd8N0XD5xmSPlNXjLsvalnhUpO+3IPc1TKxqfsoi8nuVO1R3OX8b8WKBndQ3D05XdKPsjyw7PWWeSnp4rLcrShmu4qXQlUWV5iXXr7yRCWrp0lqDC0vGiYrqXj4qpaHpvd1mKzievEUSb+ruO6dLGmTmX3J3c/J8vLxirkEWuXlSZJeYHGnul3e3iW7C9tTTD6fFI2vtiuE5PJg4MvNknNDkXNDMzRdBTkn6aMqzrfK8iklZoRzrrRtqSwPVEO7kbZlf9uWKn6U6zjF51+UC3W1LZ8jyc2stF2ZkgdNcbQt9//bsWgfHxT1HPXcCNdz6sJYdiYpKqU9kv5YsZrB1xRDEW9R9LyeqWjYNuIeJOlLio6jO3lMuP0FxQRqUlQe7bZbFlO0j9IYj5UIPirpMYre7dtrefb6F2U/79kJ+K9qv6qE1RGTJf5tFkM9P65I/C81ytkIyx33V7KYxkVKNcUcpKicjpF0q5k91d0/qHiW+TJJ7zSzuyuecZ6WdK2ZPcndP6aouP5b0pvrjMm9x6VLTWZxB6vLZWIrjGl85sdIeoviYvt2j0kR10j6kSLfXMV5+d9mVlfMIxUNrWPVtBRqdiy3ZXn5ES2vKtEqL98r6d/axXg8atnTPrQ8t9txio7RV0n6UNN7Ly3nQbu8LNueso+2MWZ2iqTjFUPs3+fu3zWzHyjugDxaMT+GFHeX7qV47/fLSzNbUsyP0HK7Im/vVbSPDmLyFfqVkp6ZNVpaLmGdkpc1xZBzg8+5vuZTSkxizh2k+CzqyKdxzrmUtuXXst6QntqEVcXQtuwppmh7o21ZdJ7X0m7MPt+XF+1jn4OmbdltzJzib8e2+3D3Ldnn8WhF3UE9Rz03avVc57yL4UzD/qXloXmLihPxbEmPy147W9LvZd+vzv59neLDfZbiWca1kt6uuDAUbX919kGsqjDGFEnR8pGQFse693GHOmPU2VC6vg6DLYrJ3tPjFcN8z1X0dC9l7/kjFX/UTiguQM/PzpkfKu7sbVZ0JB5UZ0zTe75K0en7yPx72+KzOSSLe3KruLLtvcZoOefepaj0z1ZMAPoIRY/3tBLyUst50O+YP8i9vwdIWltyLWmbl2UxVewjt63tMNmUvCzb3kVu7xOjuFP0OsWduQ9KOqXNcaxSNGza5eUhJdsPTthHUkyr9y9f1jbnRWle9jtG5FzbmCr2kZJz6nM+VZhzleRKVTFjkHMpeVBnu5G2ZX8esSraPqHyXKir3Xhw2T66yYPsddqWyzF/kPp7steo5yqKEfXcQGJSv7r6oWH/yiXP+xQ9h69XDJu8m+KuwsOaTt41iiQ7SjHM7zOKiesacyYdVLJ9VcI+kmJaJHjXH25N7/XXsmNbknS7NjEXZsf+OUnHD0HMwyW9NPsM7t1i+1rFKhGvUjz7er9BxuRiV7Xb1klcyn56jZF0tKS3Kirdc7JzuTQvFRWx1RHTyLH88aTkW0pelsV0sw9FQ+p9imtIY/6JdhVSYV6Wbe82Jnvfj5P0h4o5Db4v6fzs/H6kiueuK8vLwu0VxyRfe+vIJ3JuuHOuX/nUz5yrOZ/GIudS8il3LJW0CauK6fa9HtRXYi7U0m5M2UfKea6a2o0p++g0D1Li6shLDbaee0TZ72nkV/PxpORbSl6WxXSzD1HPUc8lXgPa/nwvPzwKX1oemXKupI9Ier+yXnrFc43NPb3rJD1UUXEUbk/ZR2pM9tpqSQ/Nvr9jwTE9oLGPQcYoJpT7lOJ54O+2+fmDJX0l+/77g4jR8kiIfKNwSjE55bqmmPwF9ijF8+p3rjsme63lHY0Wx11acfS6j6IYxfwHr1BUxvnzeZ1iidGO8rKumOx8eVT2eRxTcNyleVkWU8U+cnETkqbbfSYqycuy7VXGZHEPkfQySVskHZKal2XbU/aRGpO9NinpqLJzXgl52e8YkXOlMVXsIyXnqsqVqmLa5Vyd+ZQSM2o5l5pPqrHdmBLT4XlO27KD7R3mQl/bjSn76OIcp23ZJqZou6jnqOfGpJ5L/apsR8PypbioPl1Ndw+yk/NeTf//pqJ38yPZa6tSt1cc08kM90OxMlouNmUoXV+HwXYaM8xfWn708jmKSuqtionm7tAm7vGKO09vUNyJul3K9opjTlBMfPc1Sa/IXttnhTEl5GVdMVoeRvsESQuKoapva/FZdJKXfV09rZFrrX6um7ws295LjJYr2Pw5MCXpUYPOr4L3q3CFECXkZc0x5NwQ5VyrPKgzRuRcHTEpeVBnu5G2ZU3txpR9DPtXyjneFEfbMheTuA/qOeq55uMYqXqu26/GiTY2zOwhil7KdZI+4O5/YcuzxLf7mcPd/Yb8ZFmdbO81xsxWeUw4+CLFnYQLJD3f3Z/WKHsuZpPiwvD/JP2luz9uEDFtjm+V77vKw9DGZBNkqt1nlYuZcPdb644xs+sUz+LeWdIDtLyM55amc+cnip75wxQV3i2SLlWc+7eVbU/ZR2pMkZS8rCsmd56/WtIvJV0j6SR3P60prpO8bBmjOMV62kdzvqWcu03xhblSZ06WKTu2DvK2oxgzO1HSu7W8QsiDFQ2vL7n7ObmfK83LOmOKkHODyblhqeNS9CufUmJGLeckrVcHbct+txtTYjrMJ9qWCTEJ24embdlqe2q9UtbmS2kTVhVTpMY6LGUf1HMDiin5eeq5CtuWrYzVam5ZRXqRYkikzGxVtmm/kzD7wFe5+x53v0Ha9wQp215ljNTRDPeDXBlt3yCzte5+c+P/rZK9uXEz6JjcNs/Oj7aJk+2rbUVedYyZ3ckSlpo0s9splsPseplYVbMc7TPcfTF7Hy07jHafS2Fe1hmTU7QUqtRZXvZ19TQzm5Q05e7X5CqEdp3fhXmZmLeVxORiG0OBU/KgbV6m5m1qjJk9VnH38Ba1WSHEzI5R3GncoSFZbpacK4ypNefqzKeqc67qfEqJGcWcy8pfeo7X2W6kbVlPu7GTdmW2fWjalo3tWT7RtuwypsM6TqKe63tMLpZ6rsK2ZbvjKjNWnUlNjezbGidgqzcoe63taKWy7VXGaPnEvKdi2PIzFEPQ8tsa/95HsVrH6ZI+PYCYVZL2mNlzJG0ws5skfV7Ry3qtJJnZane/NTtxH2tmv1ZMBnmhZ8sP1hnTzLIe+TYXsUZP/hMVq8F9TNLn3P1XNcRsl3RflSw1KekI9bhMrFW0ZK3UUeOqbV7WGaO0pVDz36fkZVFMT/uwWDb3VZKOt7iL9wVJX3T3XzR2nsuDlnlZtj1lH6kxWdzeuzNmdqC7/1rtG16Nn2mblynbu4kxsxsU+XagpGPM7BR3/5SkKxXDkmWxBOw9FZNUDstys+TcAHOuznzqV871I59SYkY151LalnW2G2lb9q/d2E27MtvvwNuWbbb/UAnnuGhbtoxJrOMk6jnquRGv51qc00nG7jG3UWZmX1MMpf6C4tns/SotM7tQcRH+hKRT3f0Hg4ixtKF0tQ2DTYnJlf01kmYUz+x/wt1vaXHsM9nxHa14xvjS5kTrU8x5ipULTsq+fiLp3xXnxCXufpPFENd7KHqTH6MYOvk9xZ2HCxR3e+5SsH1H9v4U7SMppqmRc7CkB7v7F83sGHe/svl9HRZZhXSJuz8wO88flVVKrWJT8rIwppd9WOIw2Sy2MC8T87bSYbJm9tYs5r2Szit4nwvzMjFvO435D8Xw74cqzvHflvQjxWpIX5R0keLO77GK975lXkr6VV0x7n5T7ljIuQHmXJ35VHXO9Smfxj7nRhFty+5iOmlXZvsbmrZl0/Y3SfpflZzjtC17Rz1HPaeVWM95j5MuDeuXYtTVQ7LvjyqJLVx5omx7FTEaotUr2sUoek9fqBii957stbWKntX/I+kfFB0hT1cMY/5wFnN49v8zFJVFnTGrFBebu2t5pYX7Ke6uvF7Sodlrx6vFJGSKiRcPqDsm91rKUpM9LxPbS4wSJx1Mzcs6YpS+FGrPK090uw9Jp0g6TZFbf5a9dkBW9mdJOi177TgV5+U/l2z/B6XldkrMQZI2KHLw9lnMXSQ9U9IrtZwDZXl5WMn2QxP2kRTT4rNoufpcF3nZtxiRcwPLuez701RPPlWSc6ooV6qKGcWc6yQPsm2sjJYYk3ie19Vu/FTZPrLXhqptmbKPLvKAtmXn26nnqOdGvp7r9GvsRiY1eubN7BGSXiTpTyS9yt1f0SZuWtJfSJqVNOfucynbq4zJxa5SnKAnuvsFrYacmdkhknZJeoK7f7TmmHspEn6XpDtK+rTHMLn8z9xdkUzXKbuIeQxfrTXG4lnQWcXF6UpFA+ZWxcV9axYzpXg+/LcVd20ax3uJopd7l8XzxL9fR0xWpsaKBfLlu2dTiskqL3D3nU1x3vjczOwoRQ/45xU9zm23u/vVZfvoIKZ00sGUvKwzJhc7oViJY0erHMhiUvKyMKbLfTxcsfLDgYrK9p0ew2TzP2OKCrdtXibmbU8x2Tn6MsWz2Zcq8k2SviXpnOzzOFgx7LptXmZ5e3q2n3Z5e5Skp7bbRwcxh7n7jblz3Hx5gswpRYP2i7n3uTAva44h5waXc33Pp5SYxJy7veIub6+5Qs51lgd9bzemxORiaVsmxiRsH5q2peIxx9J2ZVYm2pY9xHRSx2Xx1HMVxFDP1VvPqUvj2JmUNHt9Lq7lqhLKJnxrt91X0OoVjZjs27KhdLtUwRDXCmLWKCbyO0fSDYpe6yOzuDtJeoViGOWcpA9KukJxx+FIxSR7h0k6U3G3p5YYd7+i6DMYVrlz/TwtTzp4mbu/O3f+97x6RT9icseQ9KxwFfnUQb6Z4s5L22Gy7n5LFluUl19XDF3u5zDZdYpz/PWKDqMDJd1B0hMVI6deljWqniLpuZL+Xq3z8rOSHluw/RWKu0FF+0iKcffvFX0Gw4yc6yymypzT8AxNL805xZ3S56qGfEqJGfGcY2W0PsVk3w5L27LsUa77KOaeKcqFutqWH5P0/KJ9jGq7Uhquek7LfxMm/W2ZOwbqOeq5sTeOnUmNC8NWxcV/WtJud39bPtFycW9Q/LF1kKSj3f2s7MKhou3ZtsJ9pMZk5dln9vo2x1Z6UaozJot7uOKCdC9Jb3f37zZtX6vowX2gItH+0d2/VUPMUyT9t7uf2qLMb1bc4ThA0p3c/Y9z21Yr7u6cJemnisq5lhh3f32r990sbbnOLK6jZWKriGmU2cwuUNzteI+kP3f3S3LbSvOyzpjU9zYxLwtjqthHLu4hkh6pGFr7anf/ZZu4srws3N5FzKykz7r7y1vEvE3SD939LWZ2tmICy1e3iHuz4pn9Lysawc150MjbqZJ9JMW4+9ntrnOWsEJISl72K4acG66c60M+VZJziru8PedKVTEjnnOd5EHf240pMVm5aVv2ENNi+8GSrivJhbraltOSvi/p9Nx5mtSuzGJpWybGKP5eTqrjUt5b6jnqueZtuZiB1XPdWlUeMnIab0hj9voHKSra/Lb89/dR9Iw+OPu3sa1se8o+ymJWSZLF7PVvM7O3mtnvmtkdGoXMKgZZrCrxVjN7g5k9zmIJz0HEmMWyh6skyd2/rJj87JOSrm2KMXe/2d0/Keldkr4qaWdNMR+WdC8ze52ZPdrMjrGsk1DRi7xKMUHaMWb2p2b2YDNb5+63eqxksEdx0WjEvNzMfsPMjiiISdlPUczecyIvS3Jrfr1VXNEFqmx7tzFZhW6KZ4CvV9y12ZEru5SWl7XEmNmkxVDVxrE0yi9pnzxIycuWMVXsI9vWOL8bHdwXKe5AfzBf2ZflZYd5203MVkmPMLO/N7Mnm9n9zOyIrHh3l9SY2O88SevN7G/M7CQzO7YpL78u6QRJRXn7vpJ9pMZIy9dga5wDFiuE3JaSByrJy37FkHODzbka8qmqnEvJg/MknWDFdeWKzzl1lgf9bDfStrT+ti1Ltr9f5flSV9vyp4rHlDpuV2bvMW3L9JjSfRj1HPXc6NdzXRm7kUkNljDDfRZXuPJE2fYqYmyIVq9IjRl2WXI/QnE3Y50isY+QdH9Jn5N0tqSrFBP7/bakCcVdoMMUlcUOSX8r6dtZzBMUd5tuVdyZysd8SzHk8jEF+ymNcfdv2P538dsOV8+2d7NMbD+Woz1G0t8onnv+krs/IGuINY8u6Xn1il5iLLcUqmIY7ReUWwq16ed7Xnmiin00l2tYmdn9FOfGnRSTJx6puJv7Pklvyt7/VYq8fIxiUtODFEOWG3n5t4rh10V5e2XJPpJi3P2KNudoygohhXlZRww5R86V5Zzij+eyXLlK5XUlObe8jZXRaFsOS9vyTYrHg9r+nlbtysZx0LasbvU06rn+oZ4bXEyq1VXsZNiY2ZGK3s4JSUcWdCQdrBiutsvM7tqiI6lwey8x2QXy8ZK+I+k/3P1zFsNpv6x4VvtESVNmdrLiGe4L3P0DZna4pLsp5gN6hqTP1BXj7ott3sfUoXSVD4MtiDGPZUQvVvxxerTimfufSfq5x10OSfqYmV2fxexWXJRc8Vz2VVnMR83sfEUv+J0l/TqL/Z+mmJ8n7KcwpkWD6s8slnhtuRxlLv6HinP+DyT9QFJjmdiy7V5RzJWSnpk1tJ6mFlLysoaYGcUjkI2lUE+WtMnMvuTu5yTm5V0s7rC2izlJ0gvMrJd9nNgunyxhmGwWVzbMuuphst8ys8sVz7EfongO/lozfHKSAAAgAElEQVRJv/RsItDsPFqymID1KEk3Kyr5nZKuzfLyBwl5W7aP1Jg1ZnaC4u7Wle7+M0lvVDzWcBftO5o1rzAv64gh54Yr5/qQT5XknNLyIKWuXPE5l5gHfWs3psTQtuxPTNP2YWpbXlL2e1q0KyXalh3HlGynnutfDPXc4GKSjPPIpFUqmeE+iytceaJsew8xQ7N6RWpMEUuY+K0uZvZgSWe4+7Oy/x+ruAivk/Qhd78qu7g+X9GY+b5iwm5J+ra7vyP7ueMVq6M8P/v/EYoLwZ0VQ0F/nrif0pgsbpWiQrpJ0tXu/iuLHvlTFBMov87d/zdXtpvc/dqmYz9Ecbfv6KLtHhM3F+4jNaZRdvfySQdT8rIfMYqlPO+avTd3dve/NrMDFJXBoxXDqLea2X0knaoeVqdIzO2UmMacAHsrXYthsvvdzWinLC9T8jZhH6sl3SYtr1iRvT7R9P/GqhwPl/Q1RR6sUkxueo6ypZdL8rZwH9l71ElM4epz7n5TSl5m22qJyV0DyLnE7R3E9JRzVeRTSkxKziXmQUpdSc5lOdf4bMTKaJXGFKFt2Xo/qe3KbP+0LSuKabH9FFHP9SWGem5w9VynxrYzKS+1Mko4scd19YqkGM8NTc0dS+mqIdbnYbAtYv5UcYfmdyQ9RNJLFb3AP1LML3CGIqmeK+kdkm5UDP88Mvsc7pTFPFDSs9z998zsgZL+r2I447ckHa9oIP1Gwn7KYs5UNAgerYKlJhW98T0tE2sVLEfrueVmm+Urp3Yxudg6V4x5hGK4eNFSqCbJ1PvqFJWsnubujXmG8mUsHSabxZUNYa9smKziHD/Y3V9rcad8o6Jj6GbF4wtXZbEvzLa9IfvxNYo8eILijtPnFUvpPs/MflOt8/bRJft4iaTf6jDG1XqFkCtUvgR07cvNtvgMyLkB51yV+VRVziXk20sUw/V/k5zrLOdynwUro41x27LF9pskTbn7swvypa625WcUfwgW/Z6rVZ5PtC27jMny7UTF3xrUcxXHiHpuKOq5FGPZmWTps9cXripRtr3KmCxuKFavSI35/9k773BJiqr/f87dXZKAsIQlSZYVUBdYVxFR5AWUFySo6KsoChL1RzCgvqIiLyI5C4iSzWIkJ8UAAiIogiggKChRgmIAFNnz++NU36nbt7uqZqanp+du1/PMc+9Mfaemq7pOndNV55yvw30KU56lbnJibnRvx040jlXVX+fHpGLM2Zjy2xNzi31SVY919Sdi7rQrEs7I/zAWm/u0Wmb+A4FlMryIHIdl9l8moZ0YZjlsh/xzwF8poZoUkdfSJ00sVEJHO4Fu1hkIM1X1Ee+zohOfSpgnesE4xRSkQi2au+67fbNT9NKGu74JbrIisgodN9kTAtcclMtEuU3FvBszVD4E7IMZL7/FFOjCwEdU9UER+Sx2InJEQTsnYcbzN7CEi/9HsdyuBTykqoeXtHEvFj5RBeZPNJRutpW5ZslcxfJUicwBHyMsb/dixu3vVPVUEfk0rcyVypy7zpYZbQG0Lb36NwLfVtVPBuSlLttyQ+AGzMMpnw8p+53fkzDHW9syDVNU3+q5gWJaPTcEPddLmTKbSSIyXVX/I5a9flNMSH6EJWz7cwFuG+D1WIzyD4Eb1VxLg/UpbXSByXarx+OTxVzkNsFiy//iYdRTbLOwifAj95BWCwZTTlE3OanIxbVXjIhcgC2cV7lr30dVL3J1ZwFXY+6HxwA3AVdgSvdBNerPL2HJETfEdve/gp08fEZVv+raOQFzLb4GSwCXtXNPQTuXRn5rVeA64BOa262XiVSTn6RPmliqoaMdp5vFjKLSpIMpclknJje28yigQu1SLgsx2KZgr228DmPmmUPETdZ9J+YqewTmhj0oN9mL3DgfAHwT+Kiq/spd22XYunCNw56OMaFcgc3VB1T1ryJyMWbELI8ZYVtRLLe3RNq40I1/FZi1aCDdLK3MDVXmEuStDtf0qMwBTxCf49ti3rutzBVgnMx1Iwd12I2tbTlg25LyUK6LsXUiJC912ZZPYDbfzcDlJb+zlBvz/yVXWtsyDdOtjnNttnqu1XOjpueSDibKypTZTMqKJGavlwirRKw+pY1UTNOL2ElSzCVvJiakA3eDjWHcta2HLZrrAOep260XkVuBt6sldNsU2B5TuNMw76D1McPsSGzRnoft4L8IW2xvcu38HPgAtgm0CfDf2IPwNNfe+tgC5Wf/L2MRuBBzsyw1PpyCnQccjCnVHwB/UNWM6vZ4bGFeGvixa/deT/Eej3lrfR04JNBGEkZVPyx2IvMy4Fw6SQc3ctdxnaqe4qZQklzWgcEU7Hj8tZu366vqNTSkyES33UI3WVX9bUwuXf3/w9ywB+YmKyK3Y+6+f8Du//qq+pD77g3ATtoJdfNZOTIWjIyV40Qsxj8mt+tj821F97t+GxlrXAqzXIwhZGX392bMCMmMgm42in+DnazF2oli3BrQytwASorM0SDX9FSZi8jbcdhJZYquXKBlzn2WIge12Y2tbVm53ZgSyjWd5tiWx2DyEmKnWoqIrLS2Zf/sadml0Oq5Vs+NuJ4rmjcpZUpsJsnELPnvUdV3OyW1Mp3s9XuLyDKYUroTcw3dSSaySrwOW7zK6jfH3MVCbSRhVHW7kr5E44Idpk5mtLcA7yLsSrcSFbi4VoVRz1XW68vzMDfJiz0FsiR2X5bGDIcnMSaNUOz2otjie6G6uGIRWR1bnFfFFsMnMYXrh30sRgGLgMOGNpsyqslphOlmj8ditt9A71SzqZiV3H34FyVJB7FFbFv3nUK5BA4jIrtVYVR177J7GitdyGUV7GknE3HbVdUTROQtmBtwmVxehZ1c1+FK+7TDrYg9zPxdRFYAfqKq6+T6sAg2T5dy33sYeEpL3Mud3G6JyVsmt9Ow+bgkFj//MHaS7OfDWIQw+0cQI3YytwkNoZvFTodXJ5Dok1bmem0jKnM0zDWdRJnrVt7cd1qZM8x84nJwEBXYhFVhWtuyZ0w0lKvIrnR9GZptWWZXquoTTmZb27J3zH9hHmID0XHQ6rkETKvnBqzn3BrQeiZJYvZ6bGEvZZWQlr2isIjIUcRd6RaiAhfXCjFHYsJHN7utbp68VJ0rZQlmccxN8T7M82Nr3O6xeon3xBgDliXCIuDhS42C3O9vSoAGVkSWIEw1G20jhhGLwY4lHayKeaJSBovcZ2NEqFB9bEwuY5jENl6M5c8qdZNV1S8kyOVGGB3sQQX9HribrNjJ3DqqekNWJ85lvKTfgsn2GDbXni25X7MwpTyJeUI6Lul5Jjmf/SOKybW5NWEK2CTZTWynFNPKXG+YqmSOEXBNz8scaQ/0rcyVy1zKHK/Nbmxty/G6QdiN0VAu7YRgDdu23AbYOcWudPWtbTk49rQgu1yr51o913Q9VzZu3ZSpspk0RkL2erEdxxCrxJ3YTVtg2StKMLe6vzdS7m53Jekurj9ymIG7ypYI8JuAT2Au4b/F3Jh/o7Y7Ow/YF7jAw9zuML9V1T+KyFxgP+zEYktscd7IjdFnVPX7IrIF5p58O2EWgTOBN8WMAonTxK5Bn1SzKb/jjeE0IkkHsdOrftkrKsO4NWD8BEcSqVBDcpmK6bYNSXOlfSlh99WUnAq1uMmKyAxVfbakbi3g+ar6i5L6NTGDHuCj2H2+D/i6ql7sMLOwufpW4uwfH07AzKRBdLOu/VbmusBULXOMkGt6grw9D9NnZZhW5kizLWmZ0frBNMm2zEK5rsFO7LuyKx2uLtvyQGyD7D3YXChip1qMhIPM1rbsjz1NO7krWz3X6rmR1HNVlCmxmVRUJC3D/WIEWCVi9VViHO5TNIS9IodZBVO+L6bc3e5+wm6yx2CLx7buNVBXWVW9VUSWxmLf56u3Yy+WcG1xbEHcGNuJXx5TLOth1JiLAktgi10ZRoDbtBP/uxNGDfkG4B3AbPeTIRaB5bETnt2x5H6TjAJVvcO1vSvlNLBXAFtqf1SzH0zBqOrdFBQpSTqYw6TIZS0YSaBCdbgUuQxiemkDk7NSV1qJu8oeg8luXW6ys7CwTTR3SiTG5LYV5i5+Jx0D+hYROcS1vVxJ/f9hLtAbYUbHZQ6/NWZg3+DG7n5MaV6rqueKyP4Us38cmoDZjgbRzRbNbzeurczVJHMJ8jYM1/RCmUuQt79heqEM08pcuczF5nhtdmNrW1ZqWx6HPUyW/o6zK8fc5/9geLblXsAZwOexDbIidqonichKa1sOjD2t1XOtnhtpPddtmTKbSSLxLPk5nGbKTSaySjwUqtcpyl6RgsGUa8zdLsXFdaCusm4854u5UB+FLQI3quqj7nufwU6dznTvBXO3fA5LMHgM5tFxr6qeEcC8HrhaVc8Rt0MuIgdhBsmqWEb/3QmzCKyEnSqW0qGr6okSdwd/CZYj51hsoemFavbhFIyqHu0UjkI46WCKXNaEuRmbc/8kQIUq1TBY9MOetg02z45U1Se9sQ+dhgbdV2XAbrLAX1RVReQL2NzbEbhMHbOQ++6tmBH5GJ0kiC9w1S/DToOOxgyGDQvqt8coYN+jncSJH8AU7htF5OvAl7HTpQNU9RcicjnF7B+HY0r0lwHMW2kQ3axajqxW5hogc4myMlSZS5C37d2YnI6FTJVhWpnryFzLjDYgDA2yLUP10vGsGrZtuQwmq2V25dWY3RmUlda27J89DZuvc2n1XKvnRljPaZ/hz9P7+XKTilOqmvvsCWwXNoZ7BGNzGP8oVJ/SRhlGRL5HQaZ8Ecky3N/uFqptRGRLHKuEiExglcAU5ZsGjcFOSjJ3u3+7utXouNs9IWkuruOuss6Qude1sxoDcJV15Y1Y4sXPYAo3K5+kQ4mZuaU+4f7+CXsI+moC5ifYKRhO2U9X1cNF5GhscT8QeDMdFoEHMffCrMzDFsFPYCcdWwH7ePVLYAsg2InC0SJyODm3UmyneRbmHr0Mdnr1Ga+dMUyxXA4cJyJHkKOadW086vp9TOB3susRt/gUydw1/riVYC6uA+PKy93YjFOhirFEnKKqXxFzR50uxnQRkstZ2MlaIUZE/oDlOTi7jza2cX15UnJusiLyDc/gDbqvAhuKyMmq+k5VfapEbqsMBxFgM1X9h4h8AvhW7h78BJPRH2MUr5ksL4SdFv3Iqz+/oP4W7JRp8ez3nBJ8oVOua2Eu1zOBb4jIfZjM+Q8vS2OsIDOB8yOY04HTxcgdrsDLLeB+KzuVfavYqWmZ7F7l2lkl0M7pCRggKAetzA1Q5hJlZdw1vQEyF5O3Wxzm9gimlbneZaUyuzEF09qWvduWIrKYmCdB0K7UzgPXsG3LOdgGR5ldeTz20BqTFWhty5Rnx0n1InIdxrr3Clo91+q5Eddz/ZYp45lUVNwkDGa493ClCbxi9V1i3kJz2CtSML/ETgN2k3J3uxcTd3GdA7xTa3KVFcv2/w03rter6ryy+5K7R2uq6u9TMVKwo+sWqr2Ak8rmnnTYPy4jjbJSMDff7Smmmz0NU/r9UM36bqcx9o+ekw6myGWFmCgVqqr+Vjqu3v0wT1TBnvYMcKkG3GTVTqf9fsFk99UfYQkKQ3JbmZusiMwGTgJ2Aq5U1U3yc0QK4tvFGC8uUtWtEupXwk42H/bbFpHvAjuo6ph7/wIi7B+JGD+3wNDpZikprcwNXuZooGt6TOZi8pSCaWWuuHQhKwsqM9qo2ZZXYps00RCsJtuWnl15MWbftbblADCufi9aPdfquSms57opU3ozCYoX5GEWaRZ7RQpmLnai8DksHrTI3W7FSJ8exibx02qZ+Q9k8K6yWwP/g1F/nqaqr69zLvi/5RTPGJDM/iEFVLPu8yXpgm5WeqCadZhU9o9xpSuJSQfd9/pmr0jBiMVUp7h5psjlzAhmI/pnT3sj8CbstOQyCtxkVfUa16+Qq+wGmMFbGj5JNW6y96nq8SLyNuwh4TzgE6r6ZklIDOnaWU5dmEA39ZnSFzvh2kJVvxkwRGeSY5ZLwUjD6GazfkMrc3XKHM1yTe9L5mLyFsIsqDJX8putbTl1bMsNgRswD6f8Bk72O8cAgnkKvI0h25a92JXu+61tWZ2eC+qEVs+1eq4MMyp6LrWMVdVQ04obYN8tNV+f7TS+QUTOEJHt3OKYVN8rBosHnScih4vIZiLyguxasR3DMSx53wtE5MMispGILK2q/1FzTXsOWzQyzIdEZAMRWSqASWmnDDMLC8M6F2OO8JN1Ze52XwXmBvqUnZDsKyJHArth1JRZyVxlvwK8TESOEJHXlrQT+60sdHMOdvL1EizxGpghUEvx551aea5s8RMr07x+oKr/VNWL8guTqv5NVW9R1R+q6vXYycLsfJtemYa5jS4qIh8XkWuwk4R1VfUaVb1eVX8DLOvu/5fd7zyFKfrVsIX86byy9/qWXeNRInK1iOwmIguX9DUolwPAnA78t4icJiI7iMj6Ymwk0MnnAjavXiYiR4rIf4nIqgVyGZt7v8JisENtfCXyO4thJ7lXYaFCRW6yuH5tE+jX8zG5PZtyuU0Zm9Txeynmnr8Blgw1ucQUfll9Nu/cvPyW/1kB9glVvaEbjFhowTOq+jtV/bmq/lpVH8uUvZPbGSKysIgsJDJu/P5TVS9wbYyJyIrAYqp6h6re6LXztNjDFc44ekZV71TVm4E7HWaSsm9lbigyF5O3f4J5YojIuTRY5mLyFsIsqDLnl5AcyIDsxhQMrW3Zj215K7a59ZnA74xhNmQjbMuYXQmtbTkIjDeWKToBWj3X6rkR1HPdlinrmSQJGe4dLsg8EavvAdMk9ooUzPGY4p/rPitzt9uUcjfZI7EkdvMYvKvsMar6BxH5DnCyG587VfVMyZ00uIVrEiOHX+rElBWJ080ejW0e9EM1+9/uu1H2D3dNi5KQdLCgL1G5HADmWmxOL0+Jm6dUw2BxtOt7qI0/JfzOfCJusq5/IVfZE4EVGLyb7PHu3l+BJSjcF7hcVb+Vlzf326WMbyn1dWCkYXSzXrutzA1R5iLyVqdrerLMNUGeUjBNlbmS30qRgwWdGW3UbMtj3BiH2Knux9aJb9MQ27Ifu9J9v7Utu8Tk6u/B5kFpCFGr51o9530+Mnqu2zIlNpMkIUu+qv7dYdcgzCqxcqhepyB7RSom149JrrLSpZus+85AXGVFZDPgRixe/juq+pvMCBMJM3K479eGcbhSo0DidLMPYKdxl2MnDvn6L0GUajY7gYq5r34RS5o3IekgZixkifWep6r/TJFL4uwVVWGOw1zdYyFEfTNYJLYRxeSuq9CVVrp0lZUBucmKyAbYHPwA8DVVfSD3u5nslbFyBOvrxEjz6GafoZW5Rshct/Lm2qld5pokTymYpsmcqj7orivJtqzCJmxty3H80GzL2DrjMEO3LVPtSodtbcvBsqc9S1p4eqvnWj3XSD1XRRnpzSQneLuTy14PZBnuMyX0NBYrOs4qgQlqxirxXKheVZ8RkRn9YpjIXoGIrIrd4CwL/v3SJXuFe7+UG4OVGAAzmkjPsdkCvFRdXG4JZnFMQd2HsRVsjWXOv0JVL/dwq2KLd3D83OfTVPW5TLADv30XRtN6M7BViSIeKCbRsIjRzd4LXASck/Wb7qlmT8dkaR5hutnFiSQdxO5lTC5nYTvnA8MAZ6rqf8Tc9P/qjeeEeGvpksHCfWfC3MMUZSXsFalFzFU2RBow3bWvwLP59p1BNgtT3H8va79gvJJyIZVc0x2qOltE7gA28A2uWH1dGGke3ex6tDI3dJkLyZurb5TMNUWeUjANlLmbicvTTExPDtRubG3LyaVi23IbYOeYXenqGmVbhupb27J3jJu//w/b2Cqqn4550vwztE63eq7VczRYz6nlXw2uZallehzS6LINJRnuReRC4EBVvUtEXosptiJWiS2AC0P1bmFco18MFmf9LIBMdvX8gohk7BWvo4BVwvUpY69YxrWTZ684W0Qy9opYO1GMqt7tJlpZvp9SN1ls4u8rIhd4mAmustipReYq+2KM/WMj4KMi8h+d7CpbNn7nOQE5SZ2LYETZrwA8hJ2qPFuiyAeO0TjVLMTpZj+GKbz5JfUpVLMHAm+m475aRje7D/ArVf1F7hovFEuat7W7lqBcYq62A8UAvxKRq1V1PymhQnXX/hZswf6oe++zSlwutqs/h4DsYsk7Y228NobxlEOpK21msBUpfDFX2ee7+zPJVVZK3GRFpNBNVuwk8JDQ+DnDYQybfyG2lXWAPzgj/wn3kOQrslh9XZim0c2+lVbmhiZzCfLmu6Y3SeaaIk8pmKbJXNS2xDZYBm43trblwG3L/YCHRUSAVxeM3ZHYw9z5TbItY/WtbdkX5ipso6ms/rdYrqfgOk2r51o912w9F2W7Ty2jvpk0D3OvvDT3+fnSyV5/FKYgfqeqZ2QAsZ3lCzBWid2B32GnqlpQvzM2VqE2UjA7Al91SmsrjFbUd/XcGosdvU1VL8n16RuuT2/CdsRvcJ9vBfxRVd/h2jnOYZZJaCcFc7QT6CUpdpXd1o3dDzDDZnNgeRHx3WC3xU6nMswWBZiNgB+r6k+An4jI/cDBInIjJpzTsUX3Jq/f/vhdhp1o7CEif8Tccq8G7tKJ4XGZUM8Bfo+deGQupOMsGXVh3P8pRsHfc59lC8D+WkA3W1Tv7mNWny3Kh2DK/bfuO7e613hxi/3HsVwKpwOni9FfXoHd1wfUvBDWwgy6FLmcWQPmNux0BEzGMzfP1bFEhpmb54spNmIucEbMNti9K5t7J2InxpcBt+Tkw29jTexkq/R3xE4aBfg0zk1WRCa40gLHi0ihGyywC2bwLFdS/246brI3ud/ZAthbRB5TSxK4D+YmOxvLfVM0fueJyEXA59RCCLI+FyaldWOygbumF2LzHWDMiUlpPfBcrI0qMaq6r5gnwHhR1adEZD52/+7HHmSCGBHZPT8eqvo+MbrZuar6MLC+TKSS/YcbsxWAma6dVuaGK3MxefsbsGcAU6vM1SkrU1jmUuRpIfq3CVvbcvi25QqY981YydjtiYVrfbIJtiUuN3WoDe+aWtuyN8ybMfa0y7z++vWbA6c5eWv1XKvnRlLPUWEZi0MaXWIZ8LP+XU6YVeJh7ORhQWKvmJbyW05JCHZCsSSWfX857/ceBq5S1fNU9b3Ybn/mSXQvZpikYJbE4vKzHfJvuft2NJaU7moIsgj8FUuyeB/wPXfN7wZ2FZG1XbvZLjeEGTlqwThFlNUXGQ3RUqTsy+pziij77Cng5ExhipVS9g9VvZ2OQtgSU0pfFNv9vhVLThljlRgjTXZT2glhlsfmHpjC/ZSqfkxVd8eM3bVc3en0z2BxsfuNUwJtnE6EvcIzXDZT1X9g68U/mVg2A05yfZ8P7ISdoN4IHIzF3J/kxjhf/0nsVHAN4FI1FohTMfnKTrXWxahmZ9Mx/vLjtzZ2mniPGMvKh0VkrtgJSr7487yIlSNWXzcGLUiAqOaqvHMqRlUfdEo9O9HNfnt34H9g/AHkT6p6iaqeqR2X8H8D73L//4ZW5oYpczF5u8thTi7B1C1zTZOnUZS5FFnpxiZsbcvm2pb/dmN3VsnY/cz1+T7guwzftoy20dqWfeu5GHtahhmj1XOtnkvHNErPed/ru4x6zqRphDPgH6XGSjCNMIPF8dhm0htYcNgrUpjRsvETbIErzPmDZYwfP91wk1hF5GLgw+67Mcw/gYVU9W5Xn8XUHo25pb4YW6iCLAKYIL/djdXd2AnCXlhM/AXSiXkvZXurC4PJ33MikiXZ/SOwuaoeID3EDtdZJJA0T6phr0hlpwhhlsVOSu7B5sz6qvqQu/4bgJ20407bN4OFG5od6Z29IsPMxpT6TsCVqrpJJi/u907Bcjn82LsfvhvspcC5gfq5wBnAJ1T1Zk8WT8MUzavcffmB+07mJuuP3y3YfP2TuxdrYWvoPcAPnGGY/XYmD4WsHGDGaFm9qwu2USWGGop/P7v4TitzQ5C5BHmbC/xvAqYWmatTVqaqzKXYlrTMaFPFtpyDPQiWrjNu7HamAbYlHbsxpY3WtuxNz8XY07bE5kzROt3quVbPjYSeq7So6si/MMFZB1vcX4kp05kFuE2Bt2EujP/l8Kt49Utgu4nbYIvIa/z6lDZSMTn88zABE++zJd21ZH1aD1gkMg6LYjG6C3ufrY4tzK/22lmsm/HD3IB/jBkwP+/ivqzZDQaLiS26t+/3xyYwfmPu/YbYCcerPMyM3Hc2c+P1CWA995nUiQGmuf+/g8U8HwPsERiLpdw9mFSXUt8vBpju/k7LfT6tpJ2tsRO8t7h59SJychmbe120U4jBlNC2WL6JJbz5fFfB9S7ifn8etnm5LDlZKZl7O2T3HDOsX4Qxn2RtLFrwO7Mxxbuewyzizd//AU508/jbJWM+o+T6r0qsXwlYIT9fsZPX+d774PhhjDY/wU6xt8CM2yeAFQt+fwM3PgcCK3dbPwTMLEwOpgfu/0AxtDLXCJkjIk9NlDmaJ08jIXM9yEqKTdjaliNmW/pjRwNty1A9rW0ZnXsp7YTqafVcIYZWz42cnqviNbKeSRLJgK+dXeEgq4QsmOwVScxo3q7y6zED5jDgNFV9fc07rX5+oUnjJxZ3vQ926rcMZixtgi3eb1HV7+faizJy1IzZjBKqWb//0gdNbBUYEfkwdgp4iOSS5gHfcHMmyiqRIruJ7UQxJeM9k8lUqH0xWEgF7BXeXDkcO22ejhnEn0w9TRSR5bQgmWes3huHmcAWqvrNsjnrMOtha8u/sJjs12Dx3D/O40eteGPRBCrZVuYaLHMxeQthWpnrlIbJXIqstMxoU8i2LBu7ptqWKW04XGtbdqnnHLZrHed+r9VzBf1r9ZyVJum5yvsWkItGFxHZDaPC200mZ8B/Ebag3CEiOwG7UsAqgbkDXgFsqUapl2evWANbhDeItPHBFIw6N9tAn0rZK+xrPKgAACAASURBVMQSQu6LJVwsZK8Qkbl02Cu2dNeyEba7/RmdzIwWHD8s4WPjXWVFZBMsmdsVGNvJb7Ed7lnAdap6m4isjJ3QjDNyFLRTG8bDJhkFDtszTWwVGBE5E7hWVc8VY5DIkuYtj9G4fgQ7ydsccxOGiawSqwH7Y8ZYbO5tltDOa2MYde7CgTGdEbiPazGRwaIIsyZm9LwJx14BFLJXEDeWRsJNVkRehsVu/wWTs+Uw9+W/Ye7dt+fbkQJWjlh9ShtVYvJjQDOoZFuZm4xpZa5A5pomTyMsc1HbEvMM2JWwTTgHeGdrW7a2ZRWYbuxKh29ty9703E+xDY2edZzrU6vnJvep1XMN0XNVl1Fmc5sD/Nz9X8ZecQed7P9lrBI7Ale5SbCgsFfEmNGy8fudu96N6cRmZwkZJywIYqduRX2qA3MH5gq5JXai8DuMUvEyOslgX+vGJsTIUQumyChIUPZ90cRWhJkNnOr+3wb4qLrTTDE2vbWohr1ia9dWkJ2CBAYL4E7Xp8dgIhWqK1UwWGwLfBk7Xf0vumev8Nk/wJT9be73rnfXXcSSVkjxmlpfhsnPxQLMqzE5+7rr9x3Aylgox2PeV7cSkZWAr2kxK0esvm6MX5pCJdvKXENkrld5SsFUJHNNk6dRlbkU23JFWma0KWVbltQ3ybaMttHalpXoua2wvJMh9rQ73fxdjvL1vtVzrZ4rKk3Rc5WWsaobrLGsAXxCyjPgZwvV1whn9p+FnbgcwYLDXhFjRlsCeFQ7p0MnYYwWT2LJF8EpfOkwQ5T1aeAYVX3CjccuWC6BH7gx+QRwoIisDvyICCMHFrtfB+a1mFFwm4hcKSIfFGN6mcSEJRJm5YjVp7SRisHyoZwvIt/H4rb/7F3q0sAfqIa94lES2CkSMQCHYic4bxORZXJDvBn9M1gshhkfd2pv7BXj7B9uob9FVZ9T1WNV9YHc9fr36tNF/YrVV4D5OjafBfgs5hr+QixBou+GvQNwNuWsHLH6ujH+XN+AYnrXWjG0Mjd0matBnlIwKTLXNHkaVZlLsS1bZrQpYluG6htmW6b8zua0tmW/eu77RNjTUtZyWj3X6rmJfW6anqu0jHKY23QiGfDVucRhbnJlDBanYRtKCxJ7RRIzmhu/1NjsgbrBxjD563OL/zzMZfJkVf2lG/u3E2bkqAOzL3Z6sDzwPUxhrIKdel2pqndLIisHWIx/Wb2rizJ7pGDcuL4Ai2NeEXOh/bvYidNPVHUdh+mbvcLNvfXpk8ECM1pK3TylGgaL24EPa+/sFePsH5LgJuu+E3RfjdVXhREzurfCZG0G8CCW++KvYswhh2InSpNYObA8FKX1bg4E26gS4/oTzS1QJ8ZdUytzkzG1ylxd8pSCKZM5zI5pjDyNqsxJum3ZMqNNIduyrD5/fTJ827KUVQ7zyjmS1rbsS89hIVYxZrTYOt3quVbPNVbP5e9930UHmN17GC8K2Cvc510xWLAAsld44/du4B0UZO0v+U60T3VhyvoGcUaOOjHu787ARVgc9/bACZixtoN3zUFWjli9+1sJJnD/ZwIb++NPBewV2XXRI4OFq5uNnZwujuU4mDRH6JPBgv7ZK+7HcmIsXDY2Bb8d7Fdiv3vGFOCWwR6ITi24P0FWjlh9nRg68nAFpi/OxIwxv642TCtzzZC5xD41QuZokDxNMZlrmdFKxokRty0T22iCbZnEKkdrW/at5wjoOFff6rlWz005Pdfrq/IG63y5iTeNEvrIhO/OCdQvju1ELwV8HEs8eAKwdQ63KrYb/OXcZ7tiJ0+r+IKEuQ6fg+1eXjHIm1vSrzHv/8Lxw5R9lovoSjpJIPPGTbRPdWKK7rH7+zxM0A/AdpBPxBgu/oIlX68bk7TZlOvLZvRIE1s1JmGOBSkoXf9nYIkVFypqHzNmVsQpxrLfoIRK1hvfIBVq5DqX6wXjzbuZGONL6Rg6zLcwebsfO43+MGbATDKQYv1K6XdVmISxWYjOBsMcLJx4s9T6ujE5fFUUsJVgEsa6lbkByFxVslIVph95ayJmWPIUw9CjbUnErnSY1rZsiG3Z69hRs22Z0oY37q1t2aOeI6LjcnOr1XOtnkvC5PCN0XNVvkY2zC2lSJzB4mjg+bTsFROKWBK/qKsstojV4gab6ipb0p8URo5aMFjMeDLVrLv+vmliq8I4XGHyPamGvWJx9/aj9MFgATykdbt5FvcphUklyU3WYYPuqzDuml67m6xbE1RVVSKsHJhRGWPtSGH2qAQTuj9NKK3MpZcqZa7prumZzGEPCI2Rp6kgc2Ulwa5smdFKStNsS4iHckX6U5fduERCG8+jtS371XP7Y8nBC3WcWrhY/SFExX1q9Vyr5xpTBpKIqc4iIkuJyHTpJJzyi89gsSp2mnOBiFyPCdYsV/9DzM34Q8CFrv5qjNlhnL1CVU/EXO0OFpElCbNX7KSqH8Tix7d21zqGsVfcik24UvaKQJ8GjlHVB9QSDp6HJXRTN0YvBH4lIjvoRAaAaJ9qxuTLHZjr4WOYK/aB2MnFZXQS9NWFmYO5+W6NJRX8OnA4Fjv9SHbBIrKyiLzDKdHnYCITQqy+SoyHjSXWO15E7hCR74nIUSLydhHZwNXtghnOIcy7HeYjmDztiTFO7C0iGzvMPg4zG0sKCsZgsQdmmK6HGasruLq52Dx5FfAr91mR0TTLyUEpw2UMU1RfMN5FmDtV9X+wjcYPYMbUhcDawDUisqLXRNZeWb9i9SltpGImFFWd7/U3Y+VYF7gEm99vw9bYxxLqU9qoDOPNbcSSj07P/nqf14YpwLYyl1hfsczVKU/9yFyj5CkF01SZ83Bl9lPMrvw7E5nRVqe1Le1immtb9mJXQn12Y0obrW3Zv557NXCvu55JOk6MtaurdbrVc62ey/rRRD1XWdEBuj0N8kXHVe4ozJ1vW3JugcBncLG57r1gO/ZLYZP3AmyxmFZS/06MGWE3V5/lujkIYwS4FItHvgBLDnYu8ACwnfebZwHvyK6ZgKtnYp8GjqFLV9lQnwaNcdc6IQ7aw22BJYv0v7828F7gC+51ELB6jZg5mGL7EjbHvoW5e67qXysRd/BYfZWYXJ8EY5bAfcePIb/V9fctGDvitzFX7Bvdb7zBYd4XwfwUmO21+wHgu+7/rzvMNcBG7rPL8UILMAPr1e7/aDiD+/sF7OTqncAy3WD6bYMu3WQT+1VXOEi2TuyAJQVd1r1fETPyvoadgl2KsavMxoyzYH1KGxVjXkckt0CdmFbmmiVzsT4NEkMu3IaOjnsjTuZonjyNrMwRsZ+I25XvdJg9I5jWtmyIbZnYRiNsy7J6N9atbdmfnrsdm5djhHWc0Oq5QduW2Zq4I7ZB0+q5im3LKl+1/thAOmCnM9MwN+JJSo1cXKwneBdjmxFLMFGp+PXrYgvx2l59FlN7NLZArYedIL3U3cTDmbhY3Ypl1wdv06rXPg0aQ2Jstvf9aJ/qxHjYvTEX8287gc8bBkthJ33nABsWtTtoDAGjAXuI+BLm1vxed09OwJTl2rF618YqFWH8XAilifUwGs/Ncn1fzPX/EfdbKZjLgLm5tk9zc/HnmAfE7djp8PeZnAjvBlw+icQ1pNSIScX02gbwMuB4jAb2VMyF/QHM/Xf91D7U9cLLM5H7fC/sxP2rTDZ2lsNcgU9w9/8gYKnU+jowbuyDuQXqxOSuuZW5BVjmAmO4K7bBN0Hm+pGDVuYmXHeh/UTcrlw3EdPalg2wLVPHzsMPxbZk8sFpaRu0tmUZJqbn7qQiHZf9Bq2e6+rF5HxOe9HqucowA7tvw544fU66FeiBvcJ9N8hgwRRnryjDYDHW87FFdzfM/XdHTIG+xGFWjvWpDoy77m1zn80CXum9XxcvaV/oftWFyX9O2CiIsXKksHZUgUlKmldyn5LZK9z//TJY3JVds5sjYwQSN1IBO0UfbXwAk7evYm7/a2Kus9sAs4rmTFG/YvVVYnJ9Gmd9ce/X9OZUkJUjpb5GzGws78+pmMfHgcBJWA6F7IGtNkwrc8OVOWqUpxAGO+zaJdentYAtfJmjXlmZsjLnXWtPtiUtM1ophgbZlgn1jbUtI/OitS0DGBL0HBEdl10zrZ6rUs+NYWG/uwKHYeGaBwKbeN9p9VzFeq7K18AaHuhFdxbDlr2ieswyRFxlU/pUBwbbef+lwy0B/D8sR8Fnsd34RYvuW37Mh4UJfc/Np75oYhPbSHI7p3N6dzjm1rkb8Gm/LtL/ntgrcvM1hcHizVjc/cKx+0KCERPDVNDGykTcZB0u6L4aq68Cg1FXvxd4DS6czatbAjg0do+b/qJZVLKtzA1J5uqQp0SZ2xK41v2/PPAp7JT2VCxP1ZLDlpkpJnOtbbkA2JYJ9SNhW5bVh75Ha1sWyUZQz7n6/ek+VLbVcwkYzHvmu1ho7Mcwj8VjsZDfvYiw+I3CiwbpuUG8MkEaqSJ9MHvVXaRh7BWpGHfta2OnGhu67tyL7XI/m9CnVbC45UFiFsMMkfcDb8Liak/EdroPAC5R1TP7u4P1FJEOM4NEWDmwxIFB1o5YG6kY7/rGVHW+iFyBsWHsC1yuqt/K6gY3Op3ij1NJ/anYhseDmJv9ZZi7529V9akctm/mCaiGPc1dz3KYvM3DjLEHgdNU9a+xfqX0u18MdtJ1AOYOvgrwHPAwltByDezEb6vQGiwe41sv9YPCiMhCmGH2jIjMwTbMblXVH3v42jDZ9bUyNxyZw3LeDFSeEmVuD2zj9mAR2RNLVno49oD7EezE/cyyezQseUrBNFTmWttyAbAtgadDv4PZQS9T1f1FZCda27K1LVs9N0g9dyAWvneQw+6F5QG7FNvoPVpVbwjcm1bPJWIGVQaf4XuwZWNsx21bAgwMIrIUxp4xv2hhitX3g1HVB4BdRGRnLFnmD91rc+AYEdlZVS/IFo/EPg0Uky2qqno3cLfr1zzMXXVpVf2liLzL9SfUp0Fj9sVOuJ6PKarrVPVady/mYG7I4ws7DS65hSlj5fBpYH+EuVk/klCf0kYqJru+bM5/FJsrd2CGB3l5kBJ61yow+QW8AHMylqjPp0J9F3CPiIxToWbdcn/nuv7vi7kK+3UpmL7a8OTtUeCrzqh6OZYw8l8OF+xXYr/7wmDydJKqfkZElsWM7/Ww+bIPdmoLdiJZWGKGYYrhWDVGPHpXEVkBj95VRCZRwA4aU9CHVubql7mBy1OizL0OWFhE1sMS//5QVe9y9+F27PQZLExgko4bhjylYJoqc16J2k+DtBtTMK1t2TsG2FlVS8cOO9XPxr21LRcw27KkjVbPDU7PCbCsiKyB5ftZH7hXVa8VkR0xhsIbpGRzsdVzPeu56oo2wP2r1xdxhoYYK8cCy16ROH6lLrSxPtWBwVw7L8E8Jf6OYzZxdV/HBGv8++7zSYwcBeMyMEzivA6ycsTqU9pIxXQpj5mnY8/sFVVh6MLNk2qYJyphuEgY41iYxiDdZJ/E4u2L8hJcDGzv/s+zTxUyvqXWDxpDWm6B2jCtzDVH5lL6NGDM34EvY/lnHsTL44I9NGyDbSQV6Tif7S2FEa5OTKNljpYZbUGwLUP1O2JMer/EZPDtXl3ttmU39ZH1rLUtA5jENlo9Vz3mL8AXsTX6NiyMe1WHvwqn95gob5PY3gr6NVQMDddzVb5GMswNJrgRBl0THfYu7CThZmArtV3a5PpeMcBTJLp6pvapTkwOL6qqUpGLa1WYwPWeCxyuqncV9VFE9sZoMu8E9lXVhwraGCjGuwebYV6C16jqv3PfLXQHV9V7U+r7xQD3ZWMn5ro5hi1YY1pw6iMiAtyhqrNF5A5gA1V9pi6Md221u3lWWSTnSisR99VYfZWYkuvdA/iOqj6Rv3YPsxemZB8G9lPVxwP1+wOPR9qoCnMYdrK3DXZqdydm/P4U+JNaGOGKWP6aOjB+WEIrczUVGQHXdE9nTMfCbg5T1YdL+rMrsDvwJwrkbciYRslc7jqTbKNB2Y2tbTlYu5G0UK5lsjks5tm1kKo+7d6fDRw5bNsyVN/alr1jyup7tY2aVpqq59x1bYx5rj3gcDMwj7XPZ+uiiCzmr9nOltsFT/c0CNNYPVd1GanNJLEY8dcC56vqs4nfWQHLbr4FcL2qzuumvh+MiGwCXMtEV88VMFfP61T1tpQ+1YmJlcQ+1YG5HjMC5rn3MzD3yV9giRLnY8rp9ap6iXf9s7CY4uvd+xcDa6jqRTVg1gV+l1eURUZB3kiRie7gJ2N0u3jKeEK9mrt4sI0UDOYquRLwNVXNQq5Ki4jMxtgDdsLyF2xS8BsDwWAbBztjpxwT3DyBcTdPv50yIyaGqaKN2Fh6/Rx3Xy3qF3YCHOx3rI0UDPCblIeDgusfz9vh3q+pqr9Pra8TI4G8VXVhMEOglbkhyVwVslKhzN3hLkvVc6+XzsPiEsCOqvolr24tjAb8B+79S7AHoqZgmihzz6ML22iQdmMKprUte8dg+cZC9fdjuVrOAi5U1T+76/OZp/5DDbal2Kbx7sBZ3hqXZFe6uta27BJTUh9dy913Wz03ANvS9XED4KXA2hjZxyOYzF/nMGthKU+agmmcnvMxlRcdsOtTlS8S2SsyGXN/C5knYvUpbSRgGsNe0e34Be5BbW6wEcwrMWPgC9gu8HuwhKSfc38XJ4GRo0bM4nRJNevPr8D9GChrCOZuOh8zsK4GPozttOcZY/pmr+gXQ6KbJxWwU1TRRgG+MEwr1q+UfleFKbnuVYE3uf9XI8z4dlKk/tCENirDZHMfJoUaDJNKtpW5Aclc1g/v/UiFYNGxN16ArRNRtremYRoqcy0z2gJiWybUb47ly7kcSxL8hoJrrctufFVCG9Npbcu+MZH6DzJCeq4A3/gQLLx12vu7GkauFGV7axom3yfvXgxNz6Wuxb28RtEzKcpeocYMEGSVgPFM+QsEe0UKBpcITTvJw4KusmV90orcYCOYtTE32d1c/XRgaYz28kNYDpdniTByAP+uCfNrzCjZ0J0mvwtbJH8D/Bk4Vp0bdVnJn7B0W98Lxp3SHMrEpHkvwObKeNJBSWCVGDQGO2HeCls8S908pQJ2ioraSAoHwwzcUvdV7IRk4G6ywNP+tWbzRETmAq9Q1dNE5BOYt0oZ49srsYfOUkY4LFFnkDWuKoxG2OeGUVqZGyxjYcmY70oDQ7CIyJy7D0G2N0zPNwajAfa5YZVU2xJaZrReMDTUtiypXwj4p6oeIyLvxkLiBDgF+Kaq/kssuXcdduMT7lpCbdwCnNHalgNlT1sZ+C8iIURN03MyIiFYxPXcskTY3rANtsZgNMA+NxXLSLG5aTp7hc8mVMY8IZF6v/SMyYRCm8+MljFcXOBdc/aA9SKcq6yI7As8HOoTcG+s3xVg/hdYTkReAdzmFsxHgUdFZFtgHcwbaA0RWZpyRo5FasJsDVzjlNbr3Zh/io5R8A4gSDUbU9Qpxnm3GFW9E/gfEfkQdrpwDpaUcgfgEBFZXy1WP/tOv+wVPWPUXLi/rKpflIlunhsBD4pI5ubZNztFFW1ojl3Be1j5AvAFMTfZLPfIl4AvFfULc18trXf9fijURgoGOE0s7v05VR3PQ6SqN2O5PMAeJkKMb/dgc+goYKmC+s+6/z+rqp8uaaNKDHR0wYQiQ6KSbWVuYDJ3PfAiLQ7BOhc4Vzqu6b9hwPJUhcyJyFeA5SXM9rZOwzBQwj43RJlrmdEWINtSRO4DKKlfGfima+M8Efku8GbMw3WOiJyAHYqsLmG2t8UqwOwGPCQiMwNt/BsL24PWthwUe9pDCTZWU/Tc1diG6EuBtUXED8HybctR0nN7AS+TMNvbgw3DlLLPDUvPlWEqKTpAt6eqX9Ade4X7bDPCrBLB+qoweXw3faoDgyme7XPXFnSVLetTvn+DwLj+HIotpOcBx2M7xp/HWDheg7mrXkyY7a0uzJXYrvVMd60f9OoPAI5z/08btpzlxnkhbAcejO51P2CzAL4S5oleMHTp5kkF7BR9tPFXLLFgMAQrm/uxfqXU94n5FnACJmOnuz7sx2SmkznYSVIZ49v+rn4So4ur3961sXGgjcow7v+xfH0rc1NS5v4G/MzVj0IIVlTmMKP1DMJsb43CNFTmWma0BdC2LKoH9qDDJOWzR22CpVbYHNiOONtbFZhjEtr4HOaZshStbTkQ9jRGS889iT1rjEoIVoqeW9bVfZUStremYfLrx1R/jUyYm/TA7CURVolYfZWYHD5z4WsMMxr2cNuzq2xK3weBEXM/3QALa1kUE/LvqeotkTbOJcD2NgDMLZib5yzsNG4fVf2Kq/86lujxq/mdbIm7g0+qB571r6MPzEtJSDo4akWqYZ7oqw0R+TgjEoLlTrauwjZE78RCLpbB1o51gU+r6i8T2hlnfOulvg6MdMKkd8BOja9S1cfqwmCu0q3MDUbm9mdEQrB6lTlvHS9lexs2BvhziY57I+bhexXwRI2Yp2mZ0RZ427IbuZcEtjcs79IT7n70ijldVW8UEcG8+YramI3bAMPSP+ylql9zmNa2rLE0RM8dzIiEYHWj5ySB7a1pmFxfZ6jqs2KeS4sA3y+wCWvDVF1GaTMpyuLgcEFWiVh9lZgq+lQXBjtNCsZ3q2rQVbbu4itHZ6zO996PYQbNPMrZ3qgLowWujl4/ziW+IdUzTWwfmF8Bh2AnYF/GkmuujD183ayqj/jXK30wT1SFKRlfnwq1CuaJvtnTgL2BR7Q4HOxjdMKzppf1TSLuq7H6LjAvB85R1fW9z6ZhSn9njL1pV/d57bkX+sSk5K3aH3i8JsztwKdpZW4QMncQ5pF0gPv/h6p6lrvegzF76P+kZAO3QnmqTOakE3alWvCg6H2vMZhAn3elk7dqP+2E+A4a80XsQatlRmtty9Iinc3/pYmzvS1aAWYmluS3tA21B9joxlZrW5ZjetVxHr5peu5VWGL3I7AQrGOxEKwTRORY4B+qeoj//FTWp5R+94lJ1XNNsxu72XgO5a3aT1UfrxOTcs09FW2Ae1TKiwQWB4cLskrE6qvEVNGnujCYm96lWMz5KLnK5l0n18RirucRZ3urC7MEsCkWF34kcBy2YfB6OkYBRFg5XP3eTHQHz7uLvxjYrqyNLjDrYhscuwBfw3LdXOr6NpuOPFXCPFEVJmG+9M08UVEbsXCwxoRgYUbel7EH8DnAEl7dppjxB4G1AY/xrZf6ujCYITnNe79mnRhgxVbmBiZzIxOC1avM0TkgfAGwQ0nbQ8NgunCXHGYtYAvv/UtqxKxJmv3UMqP1bluOZBhWwZiu6uZwCttb35jENpYGrsc8k5b3Ph9jYhhZa1v2wZ6WOD+aoudGJgSLLvQclLO9ed9pBAaTv43cvDoMC1c9ENjE+/5aNWIm2Z9Vv0bGM8kvEmBokAgrB+bSPHCGCxrKXhHA/A0ztkvDsLCFP9gnqnODjWJy45edGmXZ/1cAllLV/V39dCazva1WE+bXGIPBfa5vM7AFfw3gD1gC5X+4E4eQO/jVwCmB+mOxRHBBl/IUjObczmVi0rwZuKR5wGeogHmiKkzBfJgQZiTVME9Uwp5GSZFAmFZZv1Lr+8A8iT0gCPAvTFmuiq0bF6olKp2GeeH5cptn5fgcE9eQIka4vDv+QDAYE862mHz+JjeOS2BUxWdh9/D2QWNU9eDcPWi0zAHPaCSEqMky511vU0OwkmROPY+fYcpToszdBRyiqpuKyPLY3NoSuBXLCXO4u3e1YFT1b7k5UWg/0TKj9YN5BMthVxqGRY22pXYfypWfw9OxTZqjpZztbX9scyLECBfEYJs7sTY2x9a4n2NJpE9V1YvJlda27I89LXe9hSFETdJzMlohWH9j6um5n2E57u7CwnzfiOW2WhyLWDkb2zyrBaMRD7sqykhtJmU3zHvvMzCcrMZekd1Un5XjbmyXfy86rBKxemEiM0XPmNw1T3AHxbFXpPRpGBiHO5dAGFa+T1qNG2wK5lMYy0KRG/uOGAPHKXTY3rK6I7AF63rMLfSzA8a8BmO7+oB2Hl4mbEip6lkSp5tNoYn9d6SNJIy63CEwkYFDRJbBFNgbsN3vVYnQu0oCBWwFmKuB2wvmph9CNMHNs8yIUWPlSMJU0UaoSFoY1iT3VRlMCNZ+WOz9StipJ5j79M257yyPY+Uo6VOwvi6MJOStAn5UF0Zdjiwmb8g1VeYmMRK6692VkjCjIcvc6dgGjerohGBFZa4p8pSCkYS8VdjmSS2YgJ4rtI2oyCasCjMituV5qvoj16fCvD/Dti0Lxu45yufw+djcOcO9XxKzNV+JrS8nYPZUv5jZwEWRNnYisuHkvtvaliUYLNdXVMdJFyFEo2BbpvapLgxTS89F81ZhHnG1YLSGHFkjtZmUlSKl49fhTjhEZENsl/A2Vf2pq5+BTYBQ/X8S2ijFiG0WbKOqF3rXNQtzNbvevV8X+J0WxPX20u9+MJKWX2gMeL2qXhLo04uBNVT1okFiRGRjYE9MAS6BKbDfYadkWez4DOCTrl8PYO7C/8F2/lfAQs2urwnzM8y191QsSd6kDSlV/aiInIYtAu9w7d2vqsc73AGYG+ND2AL88YL6VbGd/jUCbSRhVPVDKQ9ZXj8+hLmAnwP80f3/TiCjd60bM4bJZ5bLY01V/X2iEfNMCqbfNrQg6aiIrIoZY98pGefCfqXW94FZS1Xv8eqLDP+NMWNXsSSsE+QyVp/SRsWYk4nkrcLyETyqlr9qoBiN5MjKlyHL3E7YOvRCD7MWsLqq/sC9b7TMZXNYRF6AuYlfDeyoRgGdYfJ9egmWx6EOTFDmGihPKZivEMlbBaxTF0ZzObLK7KdEu7IvuzEFM4K2ZUp+of9Qg23pxm534CxvbPJtvA3LNzOf8jm8B+ZF9keZmLdzE+yA80hsQ+IqVb2vD8ytmO64H6Ckjfdi3h9fcHWTNpxU9UER+Rxmj7cG/wAAIABJREFUW74dOJjJNt9umG35DszLochuXCzSRhKmqbZloP5t2CbH2sDCmLfPdap6nWu3cXrOx3h6bjVgLubtuAGWBL2sT2thzy+1YKagntuLSN4qbOOvFowGcmRVVUZyM8kv/sSTOKvE9aF6XcDYKzIMNhEPIxCGhYW+9e3iWgHmcczF83ISWG4kge1t0BgstCW4IaWqP3GGzDspDzX8MxYfHQpFfA57yOsLowXsH14//aSDfbNX9IvBXOm7CiEadilQmEWutKtj1Kll/ToMy6tRSwiWWytmqAsTyK29MVaOIzHZLqv/NCbfQWaPqjBqp+lzsI3VX2gu0a2IXIzlQLsPk+WbB41R1QtHSOZm0GUI0bBLTOZoYAhWmcwlyFut8pSCcTK3NWaIr+Pq9lS3iSAil2O00PPrwqjqpQGZy8a6ZUbrEYMlEg+GYUk8BKsq2zIWynUycBF9spbWWSRhY0tVfygi22MbaWWhhn/C5CQUivh3IqxxKZim2Zaheqcbhh5C1E1J0HONC8GagnpuWcw+XxLL3fcT4Cgnp1dh3no/qwujqpeUyVxVZfqgGq6r5BTaHCz5lM8q8SPsofmRhPqUNlIwG2KbMmBJljfHFvbM1fMdQCl7RUxJV40Rke2AO7U858/jWM6AWJ/+XQPmU9jp02FZH2Ri9v/MgwcnPA8AD0iO7c37bl2Yg6V4s+lz6jakVPXrGLtFUXkGM4TKWDmeAW5y9eeX3PtkjPu/cP54Bss4q4SITGCVEJFJzBODwmAL+9txIUQiUhRmhAROw6QknKwbTLdtiOcmm31Hza03c+19B15oVEG/XpnQ739F2kjFgJ04biAilwDrquqvvK69FFgmIJefjNQfgHnu1YXZNXf9+fI94FotyFs1KAx0ZCtfGihz38UezsBOcNfAHlayEKK3AmeGHjqbJnNiIVixPi1WI+ZM7KFsowKZi8lb3fKUKnOXYwdBPiazOe7GNnYfrgsDQZnL5lRddmMKZtRsy5dgGxpZGNbHReQTTAzDWo96bMvVI/UfJmEOl/U35WGtCoxfrx4Lnv8dNa+P13tf+6k6bzbXp4W8uqewe3Gje6gfK6i/CXsOuNjd16I2UjHQENsSjz2tpH5bLHdSFkL0ODaPvoyFEN0KBEOIGqjnDk7o0+tqxNzA1NNzj4nI+yjOW/Vj4MY6Me7+D2wjCabAZlKu3IG5a24JvB9zPfsmllDtYWw3NFSf0kYKZh9gDTEX300wl75rAcR2vtd1/ye7eg64/AJ4s4hk7tRPAY8Cj4rIttiJxeLE+7RIDZjXALNE5CBsvO9W1b8DfxaRmzCPm/GxzR5mtOPWvCbwElW9AEzA6sBIZLNJ4qGGuwOvdPdjllso/Pr3uHY2BeaJuW/3hPEMlQmhkJpL2owxSLyfYnrXzLulDswbgZO0OFxpH8xVHEAoKSkLbQyT2oaIbCwi426yIjLJTdaVWYT7dQ/mkn0UxpRT1O9ZdEKoysYmhjnFXc/2GNvYX7CH3fd5mwUPAr90cnkJ8PucXL7H1X8cS1qfr9/FtXFLoI0qMcH1NzPSvf7VgckndSxK2twUmdsdm7vrAZthIUR3ueu+3eHAHkzKxrhpMvcKYPlIn9apCbOKu6YdsBCLCTJHXN7qlqdUmZuBeQypqs735E9VdV+HmeZ9NlBMYqnCJqwK8z5gdbGcRKNgW26KrVuoJdL9LuYt9hpgjoicgB2KxPq0WAWY3YCHRGRmSf16xHVUaGxXEZENM5uwpFSBibYhE72UlgYuFhE/1PBp6YQaHgR8N1D/PmzT5VLgLBHpGaOqRSFak5I2U5+e2zlS/zDwMhFZAwshWh8LIbrWXe/WwA153e2XBuq5lD49WBPmv7HNpKmm57I14rrcPXoW8yLKbML5dWDqKCMR5pY3xD0jezMKGBocZm0CzBOx+j4xo8aMdj3wv4TzAmWU1QMPsUrA3IGd5HaT/X8C25sWZ+QfKIbcyYJM3GyaRzjU8AYsT1IoFHHdSBtJGDVmuZTEz4dhMdj9Mk/0i1nKXV5puJIWhBBJwQaZdsl81kMbS2IhLZcScaWXeBjWlZiL6y8L1r8qQ7DOVNXvichnsQeDtTGX5KN8ORORV2EJYgvlEvMG2Ns1XSa3r8bc5EOyXRWm8KFAInmrhoDZlU5C5qbI3Iuw/AyxEKL8OtwLq9n31WOf6aGNxUmQOWlWCNYpqnpxSOZi8jYEeepa5jLbTlzeKi14QB4kplvbcsB248gxoyVg1gYu03B+ocWpIHwqAZMSynUfcZmqhVmKHphPvbZWBTZ0c3xzAqGGsfoqMTl8KCFzLXqOCHsaZtMdRpchRFIB81kPbSyOberF9FyTQrBOVtWLpqKew3R8Yd6qzN4T6eTZGjRm4EVVR/aFPZj8Gvg2sKL7THKYpTCFfA6mlMXH5OtT2ugV4z47F1gnUD+pT3VhsB35bbEHl32BQ7AkobH7EOzTIDDuPs4FtsOM8jdjguNjl8dcEkPXPnBMwVwZc3/nYqc1YGwSJ3uY6ZjL7aZYSMmF2EOIlNTvntBGEqbsmoFp3vs1c/XLYac8J2CeLAcBSw0L42H3AGZ6fSiSub2wkJOvYhtrMcyk+9xFGz/HYseX8T6f5ubP+4FzY/KW71cv9V1ilnH/X4W5KH8R2KJkngTlMlZfJ6bo2gvkcqwODHYS+q4cZq1snJsuc7i1AVtPTsHY6ULzalfsYbRQnlIwXbTxS3qQuZQ+DRCT2TNBmaNB8jRqMleE8bB522ggNmEPmNd64zwNWNTDnU1DbcvIWpCXy0l9wh6KpU/My1PHjobYlgn1KfJ0AJb3EODd2OHxDVh+zoUxJtfSevd5VZgxjOxgV2yT4RgsYfQmXh+GoufK6t01b4Lp40UcdgaWC2i53G8slnu/FzkdFcP00UaynkvpU02Y5d37Vs8NQc9V+Wq8Z5JYSFCM6WECe4X7LJggMFbfK0ZGjBnNvV8XCxd71r2fEIaV2CfqxGjBiY73vlGZ/R0uSDXpThbejCmxLNQwqzsCy1/1T8y76FcF9WOY8t4JC10qaiMVcxq2qfhr4gmZyY39MvTBPNEPRgOJR4uKVMB81m0bYnmzTsXimH03WcTCD09S1bnZycYw1rAA7ibsxPQnwBvVMfP4/YzIZbC+boz7vClUsluSnpC5CTK3HZZj5Gkt8Ahy/y9Bn+xoro091TEC9dhGqsxNCMEq6dOE0KlBYtz7UplrmjyNoMx1bVvWueYWrJ+NYUbrAjPJNs+uWc1bN6VPi1aAmYkdlpW2oZZnJKbDarEbU9rwrilmW56PeUyf4d7nGd9mAxcF6k/AvExCbaRiViQ9IXNtei5UrxG70s2hvtjRXBvbYptJvbaRbFtqgUd2rk+NsS2bpsNGTc/VVUZhMynK9JAg7LVtLDFazGgTxq9Aca6JuSY+mNCnSkKsUjBqYVijkv1/YdI2m2YQZnw7EUsYGQpFvD7SRirm1XgJmbGwwwkJmVV1qxSlNMwiXgiRmMtnv6xmh1ENe9oVdOlKX9avXup7wYi5i1+MYwNR1bkl3ymUy9T6ujB1PRR0gdkfWFZtk3ZPLAno4XQSMl+lqsGE1sMs3vrrhxBFN8iIs5pdB3ykzzYOx/RYr+ErwwrBispcU+QpBdNAmevLtqzSbky0LV9Lc5jRurItC/qzKhYp8LeEPvUdYpXSRm6sh2lbRplP1cKVUg8y9yDM+HYrdrh4P0xIgu2HIq6F6aD7StpIxbwa8045yNXthXmkXIolZD5aVYMJresubj7g5sCkECJJYHwjzmp2C/CxPts4G9MVUT1Hg0KwWj03eGeGOsr0On+sx5LC9FDKXgFx5okU4zwVI6PFjHYA8A4ROUtdcdecKYKlsdC3uQl9Wq1GzFmMRvb/D2CKssgoOENExvPjqHmFxRjffhipJ6GNKEaMxaLnhNZSAXtFPxhvYV+ODhPZLpgbdj+sZpWwp6ltGNyAjf9KdBhP7lVj3EC95PFl/XIKTTSXU8Hvt5SfonSFwXIHfMm9f7joe66UyWVq/cAx7qHgaAJyiRn8tWDcGpCS/BlKEloPW+a8srx3revTP6vZasDVbq73zIymthF3HRZiUShzdDxjS/tUMucHgiFN5oYuTymYhspcX7ZllXZjCkaaxYzWlW2Z9SOnW1bG5PsoVT060KeUfgcxiW1kZdi2ZZT5VET2Jm2Oo+mMbxNKrv6HCW1EMSLyQvpIaF2Xnsvq3fUXYZalYxu9gf5ZzV5WQRu3qupPU/RcrE8B/VQ5hlbPDVrP1VJGYTPpFcAa0jKjDQzjDJUVsGSKj2dCrB0qyR0T+nQ9sFNNGBiN7P97Yw8oR+jE8AbfsNg1mziSwPgWqq8QcxawiFhyv8ewMMNb3He3ALLFtfDhq0R5DBQjASpUB12e+AZZjNWsEvY06YS9ZfJVeDLt5DLYLxFZTkRK69VCCYJtxDBubb0H845DLKlxPtQ0U/7bYw8JebmUUL3ra7CNqjA0a8M5WwO+hJ0wfQ4zBr7mTYVNsHxpQJhSOVSGIHMvp39Ws50ww3P9PtpYOUXmUuStCnlKwcRkri5ZmeIyl8Ii1iTbsknMaEkYNzd82zI/z89P6FNKv4MYXChXpI2HI3O4Ltsyhfk0RZ52LZlHGb6UgSylvkfMd7CNjs/QScj8bVc3B7felZW69FzOtonpuYfon9VstvutNbHE3z2xp4nIjf71tXqu1XPUVEZhM+kn2OnFlThmL6/ulVhiYsgZ2ZkBIOWsHINir7iSzmL5gNjucREzWqxPz9WE+Y2IHOPGr4xK8pKEPl1fE+YEd00vxIyHjbCQOLC49wdE5HOYq+eLgH+5B4nM1fNkhzkdSy48SMzpWO6n/xVjxQrSSLqFV9RKlqNhnPEtVg/ji3dfGC3eic/K94Brs3ayD7NFVvpgRusR81Jso+YpyucvpG2Q3QcsGsBcibFTjPXRxq/ctSMiC6nqv52SCuZmyPcrVp/SRuLv+PmgxlR1vuaY47JLdn/XoUAuPVxZ/XMJbVSFadKG8y7uuq5S1cvHB9Pl1BHb7L0b+EXecPd0TzfMaFVgXkyazKVskP0sgvkmtp6e2kcbJzuZGyuTubrkKQWTKHN1ycpUlrnLsQf3H+LYvbzxfSVwoXtA6NcmrArzAyxEOpsXfwPOEZE7MY+g2Vi40B6hPtFhPRs0JsW2vNK9Qn26ErdR1AfmVuAKZ59R0sYjrr5wDtdoWx5Hh/l0dlEbdE9RXlRWEZENtSDsNrG+a4yqPiYi7wM2xvL9PKCWq2oG8GMs30/etuybGS0FU1D/FyxcOjR/IW2D7GcRzNnYQchhfbRxYqvnho5pnJ6LrAGVlcZvJqnqN4BvlFQ/g4WPQG4zyRu8FwH7AXeKyL6q+lBZPcZg9lCojS4wnyccahTsk6reJSLn+8bFADD/xhKpXkDATU7TQrCoA4MZBWDjfRx2cn2e+yzzqIq6eqrqNSJy7aAx0qG1/BDFRgEA2YLvLfoTQg2dUeuHNOVDEf0FuSxcsSsMBUWdy7Tk3IQ9/CzgbcB2YnlgHh8gZmU3rvdhyvYxyl29oxtkqvpEBPOdACa1jWvd/y+gxJVW4q6yR0bqK3WTFZH3A+eo6pOBvmXrS5lcSqQ+pY1KMDU+FCRh3HWNSSe1wHxPr6iq7ktB8TDPx8K6tsN00OMDxCyDJS29l7jMXY49sAPFG2Sq+nAEc4bD9NPGL1xVocwlyFvtrukJMleLrFSFaajMXZc9BLs5k+lusI3Sm9zD2PiHNdiNMcyFrq4sjGgZdQmxy/qEyfXFrm+DwjxDmm2ZEoJVSYhVUcmNXXZI1QjbMvY7InIaAXnK+uhvJuTeL0exbZkPRZxkE1aAuS53H57FNlLwvr+Yqj7l6sC8y3cBtheR/dQ2JPrGYCQS+fp3YRsGZ2CHgaEwwugGWQLm+9gzYT9t3Oiuv9VzrZ6bsAbUURqdgNsNzCYEmL3c4j+dAIOFq98dOEs7nhgtM5opqnNUdX2vP76b3Aaqumt23V4fQuFTdWFGKfu/EI5hznAxVo6hZv6XQNJm6YSR9MyM1i0GG9Nzge0yTGD+9p0ItaI2MoN1P8zAuhzL6eK70s7DaFzL5HILbM0plVvMkyPURirmPVjS8dnAzMw4A56nqo8W9C/G+BasrxOTIpd1YvLX5ubCC9z3rqZPZrQqMJiRci5pMtc3q1lFbQRlDstXUYWs1C5zTZKnFExTZE5aZrRBY+YBX0ixLYtK3q4aFKaovgm2ZUob2eeMmG3prjkLt5qQkBk7cOuLGS0F4+ZpKXuas8G+oqpZWo2Rty1p9dwCp+fy1z3I0nTPpLlYMrr7mMjstSPm1nca8A9sYh4GXCI5VgkxRpmrgX1U9fMl9cdibo2HAhf1g1HV30JH8DUXRoS5psX6tG5NmO8Sd8nzjfKBh1ilYMSy/4t7LZIX5Oz7uGz7vtKqG+MZBaUxzNKQkCbvemKJn1fDFPGvcYxl2tn8WwLHaiYi78VjNRsA5knM9fRtYqcwXbt5irdBVqaopQv2tFgb2EkthF1pY66yKTkVqnKTXRdj/RPMM/CtmJF2KLBnro9BuYzV14lJlMs6MWWGYpaQ+RVY2MOXJMdYJiJbYYxlswaNwdaOX9ODzHl9XElENlILe4hi+m0DkxEYHdf0JJlrkjylYBomcxtg82EnLB/kqap6sfvOv10bGTNvmV1Zmd2YglFnWxaUVUQkY0aL9emVdWBE5F66sC3L+pSX/wFgJtTH5nBddmNKG4ly0Gjb0itZQuYNmchY9jrsgf1dYhuuZ2P5KvvF3ILZUWX1lwM3ps5fd68KN8gyuzCG6bcN4rZlq+cWPD1XW2n6ZtJ2pDF7xRgsVo/UL8jMaEGXPP9mBPo0/lkdGEYo+3/MKJCGhTS5aw4m6BPbxAgylpHAalYR5hlsPq8LPFM2fwsMsa5Zzdx97Jc9Td01hlzpY66y0ZwKWp2b7D/cb52I5XrDYVdx/fMfBhYlLJex+towKcZ6zZiYzO1P/8xoVWF+j827rsIrvJLMapaXuV7aICJzCfJWt2t6qsw1Rp5SMA2TuZfQMqMNEhOVKQpKQZ9iOjUJE9Hd422QNs/rsi2D9bE5LqNpWx5MBaxmCZgU9rSk+RvQc8msZgE91w0zWqvnGoBpmJ6rrTQ9zG1HbLf7FDrMXlndEVhSvI+KxQ6vjinlTwL3q+rxDpdlNH8I2w39eEH9qjjWs0AbSRhV/ZB47BW99AnHaIaxQw0U48ZvGgGacoev0w22FCO5kyxxiea89ylhRLVh3DWthp0SFOXHaUxIk6ruKrkTJszIzZ9CnQw8osXMaB/D5ttywKNazGpWNeYwOqGcPbt6p2AqnOMprrRB99VYfVUYsaTLc4F/YvNhQ+DPqvpB8cKRInIZrK8b4z4vlcs6MYky9xVsc+QA4CCMsewsV3cwdkq2To2YQzGDfAWmiMzVJU8pmJjMwYScOkOXpxGUufOxpPdnuPdLYjL4SuBJjOjjk5hd+XbgYIptwsXqwiTYlil9OrEujKo+mCgLtdiWCfWNsC1xmwoxu9Jd01SzLffC9MoRGGPZsdh8OUFEjsU2IB6sADMbeyY8EmNPm9SGqh4itgGyIX2GEaZgWj3X6rlBYeooTfdMSmERAxOaVSlnLLsGWBNblFtmNA8jnUTKhW5yeQVQ1Ke6MO7aRib7vxvK+QSoJmlQSJMkJOhTO2E6i/6Z0arC3OoMtJvUC4/EKxXNvSpdvVNcaYPuq7H6KjGq+l3guyLyfOAD2Kndt111NualcpkitzVjsj43gUp2IdJkrgpmtKown3V9u3mqyFyd8lSFzGX4hsjTSMmcG7sribOItcxofWBE5GGY4HUyFNsysY0m2ZbE2kic46NoW1bBjFYJe5rIOPNfz2GEKZhWz7V6blAYaiyN9kzKihQze31PO+xfoe+eCxyuxmpWNOGD9V1gvoiF2hSxV6wLjLt6pvapLoxIx00ue+8m68KYMitSAOsCmYtrLRhNyP6fCZiIXIXF3R4KnKeqP8gL4aAxmOfXcyLyWde3td04HyUTE8i+CjMeBQvr8t1BL6QT0kRRvaqeJyKvxozRwjZSMO4eRE+Yisbdw+9BmPWscgy2iE6av+7/KubwkdjJVd/z113TmpgC+BbweVXd1lMMfv8K5TK1viqMdDacs5j96dohGMhkoVAuY/V1Yxwu86Yqlcu6MLgk8t3KnNfudMx4Pkw91rNBY7BkpVNK5uqSpxRMmcw1TZ5GUeYyvRsrYsxo2cPcNGAhVX3avT8bm6OPA0+4MRgU5lxgfRJtyyaVsnle0RpR1TpyS+IcrstulIQ2kua4jKBt6da+jZnMWPZRbO1+tAoMJnOxNkrX6VbPtXquADdyeq6yoqqNfmEP5Nn/0/33WT2wKbareSTmkfMxLO58OjAtUj+W0EYK5uXA7blrm4aFC7wfW0iT+jQEzGrAG7FT8jne5/NifaoZM4YlzBNgGYdZDFiuoE83ue//FFi+ZG4NFJONNSboqwNfBLZwn0muDcHcPbfDTiDejCXeS6qvAoPtbn8ZC22ZAyzhfW9T4ObsviTIrdSIKZy/Fc7hi6qYv0Vjhz00dN2vlPoqMZHxD8plrH4YmJhc1oUhXeZmuPmU13/T/P9rxEw5mQv1qUkyRwPlKQXTFJmLja37uzTm2b0HOZ3u7tkiNWJeRaJtGerTMDAM37aMriOpc9j7fi22Zai+mzlOa1v21EZo/lY4h1s9Vz72jdNhKZiYXNaJqfPlu1Q2smgnwRSq+h/3fk0R2cFB5mKx7etijE+3Y66kO2KJ1F4VqV8soY0UzF9wLqUiMkdEllDV59TcDG/CXCmznctYn1L63TfG7QqD7WS/Agth2Ntdp+C5yQb6NO7iWgMmn/0fLGTvcH/OSEMy+2vnNGBpLC57LeA2V6deG2Nq5WY1auDvqOq3tRNXHKyvCqOqD7hxzRK0Hy8iZ7kTsg9QkjTTa39VEXlTvn8DxLzZvS2bv1DNHF7NtfHxfuavd03ZiQxaHG8dlMsEuY22kYpJLDG5TJHb2jApclkXpguZm+/m03gCWff5Stl67+oHjdnR/T9lZK5OeapI5holTymYJskc4bKKkwOf7e08EXlD1rZaotNn6sIA95JoW0b6lNLvSjANsi1T1hEwz6/G2Jax+qlsWzobJ1svs7+r+TZhv5hYfeI63eq5Vs+Nqp6rvDQ6Z5KIuZZlAyMddz2f2Ws7woxlm2KugQe4BaRlRutgxF1jYWy2ttn/+8akGBYaycgfq68Y81MRuY5wYr1J4ViujW5YzSrDEMgtUNEcPo5q2NNURPYXkbO1xE0262akX0Tqq8wLllL+gcXvF8ol8FSoXuwhKNhGlRg11+BGbDhDbzLnlW5YzSrDMLVkripZqUvmapOVqjBNk7m83svplZVpmdH6xTTFtoyuI+6akuYw9dmW0Tamqm3pf8cr3bCaRTHuWqUAk7URnL+0eq4KTKw0SoelYJqm5+osjc+ZJPEM+DF2tNlYpvjTgF8V1I+x4DKjReO7HU5os//3iknNj7MagYz8sfqqMAEjMd/O0Odw6vzN+kH/c6+vNpwBcAe2Js1US764GPA8VX00tV+QlFOhkvwOZfek4B7F5HL7UL1TwimscVVhUnIL1IJpZW64MleVrNQpczXLypSTOYcJ2UYtM9oUsi0T22iEbZnShvu8tS0HxJ7W6rlWz/WBaZSeq6s0djNJEmgkHW4GFn62CeaC9jgTGctOBDYP1B+HbRSF2kjFXAsd97OCRSeFGrM2jHddpVSSKQqgToxXl2X/fwr4tqrekzNSCgWqZkyKYZEtuvvRI01srI1UjN83ypMONm4Oh+Zv0f3pZe5V1Mb62Kb3FsDXVfWtYicLh6rqnuRKQr+C9VViUkuCXAbr68DgEuN7uCZQybYyN2SZq1OeqpK5JsjTqMlcohzsAVypqn/055iIbIKxlR2JhRVcpar31YD5MbS25aDWkVx7Q7MtU9pwda1t2SOmqvlbdI9aPdc9JqU0QYeNmp6ruzRyM0kSsuRrjr1C4oxlwfqqMGWLZUqfqJkZzV3fihgt6CuAn6nq3IL7UaoA6saIjFb2/+yaY0aBlLM9EarXAWT+l5ITpibO4ZT5m90P+p97fbXhxvXjri8bquqrRWRz4COq+t/+fYj1K1FuK8GklDK5TK2vC5Mil3ViPGwrcwWYumSuTnmqQuaaIk8pmCbJXIo8acuMNuVsy8Q2hmpbprSRv/aQXpHWtuyJPa2b+ZvdD1o91+q5Bum5oRTVejN+p7xIzF7v1Y15/xcyvoXqK8asxggwo3mfr+k+WwW4JN/PUJ+GhQnMmzEakNkfxjdp3w88P3bN7u9VFLM9BetT2ugBsx+mdF8LnOZdS+PmMAnzt6q5V1Ebb8So1T+G5Vr7MnB81r9UuUzpd1WYqfIiQS7rxPhj3crccGWuTnlqZW6omK5sy4LvR+/ToDBl87wq2a4K433eGNsypY3YvaAG2zKhvrUt+8NcFGujm/lb5/xMaKPVc0N60TA9N5QxGPYFlAzWynRJI5kNsPd+TWCH1Pp+McQXy2ifHOYrdWDc/zNyfVko9z7Yp7oxCfNmfeCHmEI+3322InBG3Ri6oJp1dT3TxFaFoaNwPovF4X4Q+GhWR8PmsLsmfw2YRIVaxdyroo2C63o+cAjwEWCtXFtBitdYfZWYqfaiIRvOrcw1R+ZS5KBOzFR7VSgrVTysd21b5vqyKjmbcNCYBFlp1Brh/m+EbZnSRuIcrstuTGmjtS17x9zqfufjZW2kzN8652cKJnddrZ4bwosG6blhvBrJ5qZdsFdk7l6aSUzH5WtpYGXniiY6OdnpAs2M5sZlwpjo5HhLcX/b7P9dYphMNftWbJE/FEtIN16kT5rYCjHq9eM4HFWx+2x+0+awOhfrwPyFauYeFbSRha9lbrJPisj8S64vAAAJ2ElEQVT/4bnJqo4zoIxj8/2K1VeJmaIlT+9aJJd1YlqZG6LMQSep7qDlqZW54ctcN7alX7ywggWZGW0UbUsS2kgpddmNKcynL6K1LQfGnpY4f6HVc62em1gao+cG1L9gaeRmEqTRSHoTdjwrf6aUHSbLlL+ciJTWqyWPC7YRw2RCIyKFi2UXfbpGRK4dJAb4hXtw3U9EztFyKsmgAhgCJljUElhehmXbv1REPoRl2//tEDCpVLNQAU1sFRgPuzTwJywx6G2uTt3fRsxhNQaLYG4BV6qYe1JBG7g++f8r4OcV6junQlWYKVwas+GcWwNamatZ5uqUp1bmGiVzSbZlzt7LZDFkEw4E09qWA19HoqVGu/GPQLANWtuy7zlc9jukz19o9Vyr5yaWpum51I3ySkojE3BD8LTGx/Sdtb8qjHdNQ2evSMGIpNGUx/o0DExKkWZk9t+ROI1k3zSxVWHc57HEz42Yw93MX4fve+7VMX9j/Urpd1WYqVqkeVSyrcwNSebqlKdW5pohc9izVsg2apnR+sA00basyq50bdViW4bqE+WgtS0LMFXOX4dv9Vyr54Bm6TmteTNpLA4ZTlE7iRERWci9z7KnAyCW/f9o4Eng5xg7wC+wnfEzROTlkfoNE9pIwnjXFHT1jPWpZkzeTe7/t3f/oHYUbRzHf3NDmoiIhggi+AcFC8HCtDZirSCChZV1QAykErQQghALRUQLLQSxEkKaYOwsxM4q4h+EBMRSLKJgIzgWO3vv3nPP2Xn2nNlnZ89+P7D4evc56869z7Mz757dGal5TO6d7nFybfKOyQkhHKTO4Y6ktyW9H2O8JR19A+IVE2O8FmN8U9LHku5W8772R+lU/0uf7T6iqtjzStPq/pIxHWu/YWp3VpTDpvxN579z7nnlr6FdlnaXitlLubr0jhE1N2XNedYTNVdBzfXleCg0JiwV0/7+cnle0TVCqmxsWahfdhs3Wo7B2HL7mFL5m86ffo5+7lBN/dxYbdyk2tfckockPR1COLaMZPKUmomnLrc/CM23TmclvSLprcz+19X84kvEvJp2WR4H7WuTZ4z1MTmXx2AHxPTqxqaO99+pYkL+HeaqXmlKf/PbanKivct+rJ1JDTlc8lFvS4xL/ubaJducCrN8TNZLX112B/weMdTc5DW36EfTvdRUc+mUNuV4blxZctzI2NJn3FiiX3YbN1qOwdhy55gS+SvRz20VQz/n1s+5qfI1t7ZIQgivqZmA8GtJL8cYL3QuYA9KuiLpJzXf6NyOMf6dPv+Mmrt1N9W8a3x9zf4P1Kwq8K6kHzccwxQTYzwfQjjdvZiHk4+UWtrkFpPOKfc4uKLTY7CWmH0TKnqlaSUv1nZSteVwLn+j4VHv9DOXx8GtDHX5gqHds3tMdmmouTpqrlStUHP1y+W4mjlU+saVxcaNlhjGluNfR/YRY8uN9R1K5C/9HP0cjqv1yaT2kcONs9fH/Oz/2Vn7436uXmGe/T/GeE3StXD0bvZNSVdT3ImV69Zc5Jj9fze5GfldZv7vdMS5b5iqyuFc/pbIvSny11CXvfstx7DGYBzUXD0151lP1NzkenPcMK5c8sposxpbLnhcKTG23Gl1P/o5+jkMFGOsblPTqUtNsT8i6XNJz7X9z0psUHMH9Hk1Exm+JOm8df+uMe35SLoo6Z5d2uQZ08Z1PhMknV45zsY2ecbs6ybpYUmfqFmO+Nv0s2cl3Uj/+9HM/lOGY1hjDiT9mvLgbPr5GUnnas3hTflbKvemyt++dln2l4xhG7X+qbmB+60xW/wdXOqJmpu+5tI/c2OjUceNlhhrnnvXv/H3N+nY0vq729dNjC3Xxuyav575WTqH+9o0RQzbfm2Tn0DvyUnfp4vRd5LuX7P/YOXfV2809e4vFWO5WFrb5B3T87s3dQBeMfu6SXpR0mVJb0i6JOkLSe+lfady+y3HMP53npT0TfpbfJl+/oCkT5ecw+Qv21gbNUfNsU2z9eW4nMaNlpgheb6ka4QlZunXCDG27Pv/jlvnr2d+Lj2H2eazdR/7q0owzF4fd5+1v1RMVatXWGMymP3fQcyvPOM1879lYr0l5jD5i7FQc9QcnOVy3HHcyNhy3HHjoq8RjC3XxxTIX4l+Djim1jmTJPvs9bvM2l8qprbVK6wxfZj930HIr/bkNfP/byGEG2oer/8qhHBJzaR5P6+c8qJyWIVWT9vX/MX2YozUHDUHf6yMNvNrRKnryD5fIxhbjrq6H/0c0FHram6WGe53nrU/dwxrTDqfKlavsMZY5NoUmf1/L4WjSfP+kXQ1xngr1dTBEnNYhVZPG3ruWA5qjprD+HI5XmpMyNiyn9e4UYbryNBzx/am7udK5W/6LP0ckFR3M2mlE914t7hTaB+qmUztcTWTfF3pdrCb9sfO0o67xqyc19qLZa5NnjFD5drkHYNxhKNvmNpH7Q+/YSKHyV+UR81Rc/BhrIMiY0LGljZe40auEdOqoZ8bI3/TsejngFjBxE3tJtlnr1eBWftLxbRxnc8czl5vaZNnzMC/B7P/sy0+h8lfNu+NmqPm2Mpt1hwXK6PN5hphieEaUffmlZ+l8zcdi36OjS1tVU3AHePhe7wXJP0VQjgrSSGEMyGEcyux7V3leyX9LukxST+0x8nttxzDGtPGdT4T48l3kze2yTNmiE1tmioG01h6DpO/8EbNUXMox5rj0XHcaIlp49bl+dKvEZYYrhF188rP0vmb/pv0c0BS1c2kxDx7fSgwa3+pmAJtYmU01IwcBnxRc0A5rIzGNQL1YXU/YOZqvJlkWkYyWTsr/4D9JWN2bZNnDDAUOQz4ouaAcqw57jluZGyJpatmdb/iLQMWoroJuCUp2GbJ33nW/lIxBdvEymioFjkM+KLmgHJyOS6xMtquMUPPH/DKT7G6HzCKKm8mtcLmZSQP3ykPW87aXyqmVJti5w/hGQMMRQ4Dvqg5oJxcjo89bmRsCZzklZ/kL1BWlTeTgmEZyRDCRUmfxRjvrHy2d3/JmFJtmiIGGIocBnxRc0A5m3Lcc9zI2BI4zis/yV9gHFXeTMpJF4RfJD0h6b4Y458hhDOS7oox/pHbbzmGNQYAAADz5TluZGwJANgXNU7AbVFi1n5WrwAAAAArowEAMNBcbyaVmLWf1SsAAADAymgAAAw0y9fcJNOqHNlZ+3PHsMa4NhwAAABFlRoTMrYEACzFbG8mtUKBWftLxQAAAGC+PMeNjC0BAHM225tJJWbtZ/UKAAAAsDIaAADDzPZmEgAAAAAAAPzNdQJuAAAAAAAATICbSQAAAAAAADDjZhIAAAAAAADMuJkEAAAAAAAAM24mAQAAAAAAwIybSQAAAAAAADD7H0rQD81vXgMHAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 50.9 s, sys: 1.46 s, total: 52.4 s\n", "Wall time: 2min 54s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " CUSUM,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_CUSUM,\n", " argskwargs2str=argskwargs2str_for_CUSUM,\n", " savefig=f\"Detection_delay__CUSUM__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_CUSUM)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments for `Sub-Gaussian GLR`" ] }, { "cell_type": "code", "execution_count": 293, "metadata": {}, "outputs": [], "source": [ "list_of_args_kwargs_for_SubGaussianGLR = tuple([\n", " ((), {'delta': delta, 'joint': joint, 'sigma': sigma}) # empty args, kwargs = {delta=0.01, joint=True}\n", " for joint in [True, False]\n", " for delta in [10, 1, 0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001]\n", " for sigma in [10*SIGMA, SIGMA, 0.1*SIGMA]\n", "])" ] }, { "cell_type": "code", "execution_count": 294, "metadata": {}, "outputs": [], "source": [ "def argskwargs2str_for_SubGaussianGLR(args, kwargs):\n", " delta = kwargs['delta']\n", " joint = kwargs['joint']\n", " sigma = kwargs['sigma']\n", " return fr\"$\\delta={delta:.4g}$, {'joint' if joint else 'disjoint'}, $\\sigma={sigma:.4g}$\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, for a Bernoulli problem:" ] }, { "cell_type": "code", "execution_count": 309, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = 0.25\n", "secondMean = mu_2 = 0.75\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 310, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = 0.25, mu^2 = 0.75, and horizon = 1000, tau = 500...\n", "with 54 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, 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'Detection_delay__SubGaussianGLR__Bernoulli_T1000_N10__params54.pdf' created of size '23648b', at 'Tue Dec 18 12:56:29 2018' ...\n" ] }, { "data": { "image/png": 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'False_alarm__SubGaussianGLR__Bernoulli_T1000_N10__params54.pdf' created of size '23930b', at 'Tue Dec 18 12:57:51 2018' ...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 20.3 s, sys: 1.13 s, total: 21.4 s\n", "Wall time: 1min 20s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(false_alarm, \"False alarm probability\",\n", " SubGaussianGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=False,\n", " list_of_args_kwargs=list_of_args_kwargs_for_SubGaussianGLR,\n", " argskwargs2str=argskwargs2str_for_SubGaussianGLR,\n", " savefig=f\"False_alarm__SubGaussianGLR__Bernoulli_T1000_N10__params{len(list_of_args_kwargs_for_SubGaussianGLR)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, for a Gaussian problem:" ] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.1\n", "secondMean = mu_2 = 0.1\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 315, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.1, mu^2 = 0.1, and horizon = 1000, tau = 500...\n", "with 54 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 23.8 s, sys: 1.34 s, total: 25.1 s\n", "Wall time: 3min 2s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " SubGaussianGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_SubGaussianGLR,\n", " argskwargs2str=argskwargs2str_for_SubGaussianGLR,\n", " savefig=f\"Detection_delay__SubGaussianGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_SubGaussianGLR)}\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And for a harder Gaussian problem:" ] }, { "cell_type": "code", "execution_count": 312, "metadata": {}, "outputs": [], "source": [ "horizon = 1000\n", "firstMean = mu_1 = -0.01\n", "secondMean = mu_2 = 0.01\n", "gap = mu_2 - mu_1\n", "tau = 0.5" ] }, { "cell_type": "code", "execution_count": 313, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Exploring detection_delay for algorithm , mu^1 = -0.01, mu^2 = 0.01, and horizon = 1000, tau = 500...\n", "with 54 values for args, kwargs, and 10 repetitions.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='ArgsKwargs', max=1, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='Repetitions||', max=1, style=ProgressStyle(…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 21.9 s, sys: 1.12 s, total: 23 s\n", "Wall time: 4min 29s\n" ] } ], "source": [ "%%time\n", "_ = view1D_explore_parameters(detection_delay, \"Detection delay\",\n", " SubGaussianGLR,\n", " tau=tau,\n", " firstMean=mu_1,\n", " secondMean=mu_2,\n", " horizon=horizon,\n", " repetitions=10,\n", " gaussian=True,\n", " list_of_args_kwargs=list_of_args_kwargs_for_SubGaussianGLR,\n", " argskwargs2str=argskwargs2str_for_SubGaussianGLR,\n", " savefig=f\"Detection_delay__SubGaussianGLR__Gaussian_T1000_N10__params{len(list_of_args_kwargs_for_SubGaussianGLR)}_2\",\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other experiments" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "# Conclusions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
TODO TODO TODO TODO TODO TODO
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