# -*- coding: utf-8 -*-
""" The naive Empirical Means policy for bounded bandits: like UCB but without a bias correction term. Note that it is equal to UCBalpha with alpha=0, only quicker."""
from __future__ import division, print_function # Python 2 compatibility
__author__ = "Lilian Besson"
__version__ = "0.1"
import numpy as np
np.seterr(divide='ignore', invalid='ignore') # XXX dangerous in general, controlled here!
try:
from .IndexPolicy import IndexPolicy
except ImportError:
from IndexPolicy import IndexPolicy
[docs]class EmpiricalMeans(IndexPolicy):
""" The naive Empirical Means policy for bounded bandits: like UCB but without a bias correction term. Note that it is equal to UCBalpha with alpha=0, only quicker."""
[docs] def computeIndex(self, arm):
r""" Compute the current index, at time t and after :math:`N_k(t)` pulls of arm k:
.. math:: I_k(t) = \frac{X_k(t)}{N_k(t)}.
"""
if self.pulls[arm] < 1:
return float('+inf')
else:
return self.rewards[arm] / self.pulls[arm]
[docs] def computeAllIndex(self):
""" Compute the current indexes for all arms, in a vectorized manner."""
indexes = self.rewards / self.pulls
indexes[self.pulls < 1] = float('+inf')
self.index[:] = indexes