content: /**
   * 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 org.apache.bigtop.datagenerators.samplers.wfs;
  
  import java.util.Map;
  import java.util.Set;
  
  import org.apache.bigtop.datagenerators.samplers.pdfs.MultinomialPDF;
  
  import com.google.common.collect.ImmutableMap;
  import com.google.common.collect.Maps;
  
  public class MultinomialWF<T> implements DiscreteWeightFunction<T>
  {
  	private final ImmutableMap<T, Double> weights;
  
  	public MultinomialWF(Map<T, Double> probabilities)
  	{
  		this.weights = ImmutableMap.copyOf(probabilities);
  	}
  
  	public Set<T> getData()
  	{
  		return weights.keySet();
  	}
  
  	public double weight(T value)
  	{
  		if(weights.containsKey(value))
  		{
  			return weights.get(value);
  		}
  
  		return 0.0;
  	}
  	
  	public MultinomialPDF<T> normalize()
  	{
  		double sum = 0.0;
  		for(double w : weights.values())
  		{
  			sum += w;
  		}
  		
  		Map<T, Double> probabilities = Maps.newHashMap();
  		for(Map.Entry<T, Double> entry : weights.entrySet())
  		{
  			probabilities.put(entry.getKey(), entry.getValue() / sum);
  		}
  		
  		return new MultinomialPDF<T>(probabilities);
  	}
  
  }
  