# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================

"""Contextual bandit algorithm that selects an action at random."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from bandits.core.bandit_algorithm import BanditAlgorithm


class FixedPolicySampling(BanditAlgorithm):
  """Defines a baseline; returns an action at random with probs given by p."""

  def __init__(self, name, p, hparams):
    """Creates a FixedPolicySampling object.

    Args:
      name: Name of the algorithm.
      p: Vector of normalized probabilities corresponding to sampling each arm.
      hparams: Hyper-parameters, including the number of arms (num_actions).

    Raises:
      ValueError: when p dimension does not match the number of actions.
    """

    self.name = name
    self.p = p
    self.hparams = hparams

    if len(p) != self.hparams.num_actions:
      raise ValueError('Policy needs k probabilities.')

  def action(self, context):
    """Selects an action at random according to distribution p."""
    return np.random.choice(range(self.hparams.num_actions), p=self.p)
