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.bigpetstore.generators.purchase;
  
  import java.util.Map;
  
  import org.apache.bigtop.datagenerators.bigpetstore.datamodels.Product;
  import org.apache.bigtop.datagenerators.bigpetstore.datamodels.inputs.ProductCategory;
  import org.apache.bigtop.datagenerators.samplers.markovmodels.MarkovModel;
  import org.apache.bigtop.datagenerators.samplers.markovmodels.MarkovModelBuilder;
  import org.apache.bigtop.datagenerators.samplers.samplers.Sampler;
  
  import com.google.common.collect.Maps;
  
  public class MarkovModelProductCategorySampler implements Sampler<MarkovModel<Product>>
  {
  	final ProductCategory productCategory;
  	final Sampler<Double> fieldSimilarityWeightSampler;
  	final Sampler<Double> loopbackWeightSampler;
  
  	final Map<String, Double> fieldWeights;
  	Map<String, Double> fieldSimilarityWeights;
  	double loopbackWeight;
  
  	public MarkovModelProductCategorySampler(ProductCategory productCategory,
  			Map<String, Double> fieldWeights, Sampler<Double> fieldSimilarityWeightSampler,
  			Sampler<Double> loopbackWeightSampler)
  	{
  		this.productCategory = productCategory;
  
  		this.fieldSimilarityWeightSampler = fieldSimilarityWeightSampler;
  		this.fieldWeights = fieldWeights;
  		this.loopbackWeightSampler = loopbackWeightSampler;
  	}
  
  	protected void generateWeights() throws Exception
  	{
  		fieldSimilarityWeights = Maps.newHashMap();
  
  		for(String fieldName : productCategory.getFieldNames())
  		{
  			fieldSimilarityWeights.put(fieldName,fieldSimilarityWeightSampler.sample());
  		}
  
  		loopbackWeight = loopbackWeightSampler.sample();
  	}
  
  	protected double productPairWeight(Product product1, Product product2)
  	{
  		double weightSum = 0.0;
  		for(String fieldName : productCategory.getFieldNames())
  		{
  			double fieldWeight = this.fieldWeights.get(fieldName);
  
  			if(product1.getFieldValue(fieldName).equals(product2.getFieldValue(fieldName)))
  			{
  				fieldWeight *= this.fieldSimilarityWeights.get(fieldName);
  			}
  			else
  			{
  				fieldWeight *= (1.0 - this.fieldSimilarityWeights.get(fieldName));
  			}
  
  			weightSum += fieldWeight;
  		}
  		return weightSum;
  	}
  
  	public MarkovModel<Product> sample() throws Exception
  	{
  		generateWeights();
  
  		MarkovModelBuilder<Product> builder = new MarkovModelBuilder<Product>();
  
  		for(Product product1 : productCategory.getProducts())
  		{
  			builder.addStartState(product1, 1.0);
  
  			double weightSum = 0.0;
  			for(Product product2 : productCategory.getProducts())
  			{
  				if(!product1.equals(product2))
  				{
  					weightSum += productPairWeight(product1, product2);
  				}
  			}
  
  			for(Product product2 : productCategory.getProducts())
  			{
  				double weight = 0.0;
  				if(!product1.equals(product2))
  				{
  					weight = (1.0 - loopbackWeight) * productPairWeight(product1, product2) / weightSum;
  				}
  				else
  				{	weight = loopbackWeight;
  
  				}
  
  				builder.addTransition(product1, product2, weight);
  			}
  		}
  
  		return builder.build();
  	}
  }
  