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 eu.amidst.flinklink.core.learning.dynamic;
  
  
  import eu.amidst.core.learning.parametric.bayesian.utils.DataPosterior;
  import eu.amidst.core.variables.Variable;
  import eu.amidst.dynamic.datastream.DynamicDataInstance;
  import eu.amidst.dynamic.models.DynamicBayesianNetwork;
  import eu.amidst.dynamic.models.DynamicDAG;
  import eu.amidst.flinklink.core.data.DataFlink;
  import org.apache.flink.api.java.DataSet;
  
  import java.util.List;
  
  /**
   * This interface defines the Algorithm for learning the {@link eu.amidst.core.models.BayesianNetwork} parameters.
   *
   * <p> For an example of use follow this link </p>
   * <p> <a href="http://amidst.github.io/toolbox/CodeExamples.html#learningexample"> http://amidst.github.io/toolbox/CodeExamples.html#learningexample </a>  </p>
   */
  public interface ParameterLearningAlgorithm {
  
      public void initLearning();
  
      void updateModelWithNewTimeSlice(int timeSlice, DataFlink<DynamicDataInstance> data);
  
      public DataSet<DataPosterior> computePosterior(List<Variable> latentVariables);
  
      void setDAG(DynamicDAG dag);
  
      void setSeed(int seed);
  
      DynamicBayesianNetwork getLearntDynamicBayesianNetwork();
  
      void setOutput(boolean activateOutput);
  
  }
  