/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.mapr.stats.bandit;
import com.mapr.stats.random.BetaBinomialDistribution;
import org.apache.mahout.common.RandomUtils;
import java.util.Random;
/**
* Multi-armed bandit problem where each probability is modeled by a beta prior and data about
* positive and negative trials. An arm is selected by sampling from the current posterior
* for each arm and picking the one with higher sampled probability.
*/
public class BetaBayesModel extends BayesianBandit {
public BetaBayesModel() {
this(2, RandomUtils.getRandom());
}
public BetaBayesModel(int bandits, Random gen) {
for (int i = 0; i < bandits; i++) {
addModelDistribution(new BetaBinomialDistribution(1, 1, gen));
}
}
}