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.markovmodels;
  
  import java.util.Map;
  
  import org.apache.bigtop.datagenerators.samplers.SeedFactory;
  import org.apache.bigtop.datagenerators.samplers.samplers.RouletteWheelSampler;
  import org.apache.bigtop.datagenerators.samplers.samplers.Sampler;
  
  import com.google.common.collect.ImmutableMap;
  
  public class MarkovProcess<T> implements Sampler<T>
  {
  	final ImmutableMap<T, Sampler<T>> transitionSamplers;
  	final Sampler<T> startStateSampler;
  
  	T currentState;
  
  
  	public MarkovProcess(MarkovModel<T> model, SeedFactory factory)
  	{
  		Map<T, Map<T, Double>> transitionTable = model.getTransitionWeights();
  
  		startStateSampler = RouletteWheelSampler.create(model.getStartWeights(), factory);
  
  		ImmutableMap.Builder<T, Sampler<T>> builder = ImmutableMap.builder();
  		for(Map.Entry<T, Map<T, Double>> entry : transitionTable.entrySet())
  		{
  			builder.put(entry.getKey(), RouletteWheelSampler.create(entry.getValue(), factory));
  		}
  
  
  		this.transitionSamplers = builder.build();
  
  		currentState = null;
  	}
  
  	public static <T> MarkovProcess<T> create(MarkovModel<T> model, SeedFactory factory)
  	{
  		return new MarkovProcess<T>(model, factory);
  	}
  
  	public T sample() throws Exception
  	{
  		if(currentState == null)
  		{
  			currentState = startStateSampler.sample();
  			return currentState;
  		}
  
  		currentState = transitionSamplers.get(currentState).sample();
  		return currentState;
  	}
  }
  