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.
   *
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   */
  
  package eu.amidst.core.inference;
  
  
  import eu.amidst.core.Main;
  import eu.amidst.core.models.BayesianNetwork;
  import eu.amidst.core.utils.BayesianNetworkGenerator;
  import eu.amidst.core.utils.Utils;
  import eu.amidst.core.variables.Assignment;
  import eu.amidst.core.variables.HashMapAssignment;
  import eu.amidst.core.variables.Variable;
  import org.apache.commons.lang3.ArrayUtils;
  
  import java.util.Arrays;
  import java.util.List;
  import java.util.Random;
  
  /**
   * Created by dario on 01/06/15.
   */
  public class MPEInferenceExperiments_Deliv1 {
  
      private static Assignment randomEvidence(long seed, double evidenceRatio, BayesianNetwork bn) throws UnsupportedOperationException {
  
          if (evidenceRatio<=0 || evidenceRatio>=1) {
              throw new UnsupportedOperationException("Error: invalid ratio");
          }
  
          int numVariables = bn.getVariables().getNumberOfVars();
  
          Random random=new Random(seed); //1823716125
          int numVarEvidence = (int) Math.ceil(numVariables*evidenceRatio); // Evidence on 20% of variables
          //numVarEvidence = 0;
          //List<Variable> varEvidence = new ArrayList<>(numVarEvidence);
          double [] evidence = new double[numVarEvidence];
          Variable aux;
          HashMapAssignment assignment = new HashMapAssignment(numVarEvidence);
  
          int[] indexesEvidence = new int[numVarEvidence];
          //indexesEvidence[0]=varInterest.getVarID();
          //if (Main.VERBOSE) System.out.println(variable.getVarID());
  
          if (Main.VERBOSE) System.out.println("Evidence:");
          for( int k=0; k<numVarEvidence; k++ ) {
              int varIndex=-1;
              do {
                  varIndex = random.nextInt( bn.getNumberOfVars() );
                  //if (Main.VERBOSE) System.out.println(varIndex);
                  aux = bn.getVariables().getVariableById(varIndex);
  
                  double thisEvidence;
                  if (aux.isMultinomial()) {
                      thisEvidence = random.nextInt( aux.getNumberOfStates() );
                  }
                  else {
                      thisEvidence = random.nextGaussian();
                  }
                  evidence[k] = thisEvidence;
  
              } while (ArrayUtils.contains(indexesEvidence, varIndex) );
  
              indexesEvidence[k]=varIndex;
              //if (Main.VERBOSE) System.out.println(Arrays.toString(indexesEvidence));
              if (Main.VERBOSE) System.out.println("Variable " + aux.getName() + " = " + evidence[k]);
  
              assignment.setValue(aux,evidence[k]);
          }
          if (Main.VERBOSE) System.out.println();
          return assignment;
      }
  
  
      /**
       * The class constructor.
       * @param args Array of options: "filename variable a b N useVMP" if variable is continuous or "filename variable w N useVMP" for discrete
       */
      public static void main(String[] args) throws Exception { // args: seedNetwork numberGaussians numberDiscrete seedAlgorithms
  
          int seedNetwork = 23423523;
          int numberOfGaussians = 20;
          int numberOfMultinomials = 20;
  
          int seed = 634634534;
  
          int parallelSamples = 100;
          int samplingMethodSize = 10000;
  
          int repetitions=10;
  
          int numberOfIterations=200;
  
          if(args.length!=8) {
              if (Main.VERBOSE) System.out.println("Invalid number of parameters. Using default values");
          }
          else {
              try {
                  seedNetwork = Integer.parseInt(args[0]);
                  numberOfGaussians = Integer.parseInt(args[1]);
                  numberOfMultinomials = Integer.parseInt(args[2]);
  
                  seed = Integer.parseInt(args[3]);
  
                  parallelSamples = Integer.parseInt(args[4]);
                  samplingMethodSize = Integer.parseInt(args[5]);
  
                  repetitions = Integer.parseInt(args[6]);
  
                  numberOfIterations = Integer.parseInt(args[7]);
  
              } catch (NumberFormatException ex) {
                  if (Main.VERBOSE) System.out.println("Invalid parameters. Provide integers: seedNetwork numberGaussians numberDiscrete seedAlgorithms parallelSamples sampleSize repetitions");
                  if (Main.VERBOSE) System.out.println("Using default parameters");
                  if (Main.VERBOSE) System.out.println(ex.toString());
                  System.exit(20);
              }
          }
          int numberOfLinks=(int) 1.3 * (numberOfGaussians + numberOfMultinomials);
  
          BayesianNetworkGenerator.setSeed(seedNetwork);
          BayesianNetworkGenerator.setNumberOfGaussianVars(numberOfGaussians);
          BayesianNetworkGenerator.setNumberOfMultinomialVars(numberOfMultinomials, 2);
          BayesianNetworkGenerator.setNumberOfLinks(numberOfLinks);
  
          String filename = "./networks/simulated/RandomBN_" + Integer.toString(numberOfMultinomials) + "D_" + Integer.toString(numberOfGaussians) + "C_" + Integer.toString(seedNetwork) + "_Seed.bn";
          //BayesianNetworkGenerator.generateBNtoFile(numberOfMultinomials,2,numberOfGaussians,numberOfLinks,seedNetwork,filename);
          BayesianNetwork bn = BayesianNetworkGenerator.generateBayesianNetwork();
  
  
          //if (Main.VERBOSE) System.out.println(bn.getDAG());
          //if (Main.VERBOSE) System.out.println(bn.toString());
  
  
          MPEInference mpeInference = new MPEInference();
          mpeInference.setModel(bn);
          mpeInference.setParallelMode(true);
  
  
          //if (Main.VERBOSE) System.out.println("CausalOrder: " + Arrays.toString(Utils.getCausalOrder(mpeInference.getOriginalModel().getDAG()).stream().map(Variable::getName).toArray()));
          List<Variable> modelVariables = Utils.getTopologicalOrder(bn.getDAG());
          if (Main.VERBOSE) System.out.println();
  
  
  
          // Including evidence:
          //double observedVariablesRate = 0.00;
          //Assignment evidence = randomEvidence(seed, observedVariablesRate, bn);
          //mpeInference.setEvidence(evidence);
  
  
          mpeInference.setSampleSize(parallelSamples);
          mpeInference.setSeed(seed);
          mpeInference.setNumberOfIterations(numberOfIterations);
  
          double[] SA_All_prob = new double[repetitions];
          double[] SA_Some_prob = new double[repetitions];
          double[] HC_All_prob = new double[repetitions];
          double[] HC_Some_prob = new double[repetitions];
          double[] sampling_prob = new double[repetitions];
  
          double[] SA_All_time = new double[repetitions];
          double[] SA_Some_time = new double[repetitions];
          double[] HC_All_time = new double[repetitions];
          double[] HC_Some_time = new double[repetitions];
          double[] sampling_time = new double[repetitions];
  
          long timeStart;
          long timeStop;
          double execTime;
  
          Assignment mpeEstimate;
  
          mpeInference.setParallelMode(true);
  
          for(int k=0; k<repetitions; k++) {
  
              mpeInference.setSampleSize(parallelSamples);
  
              /***********************************************
               *        SIMULATED ANNEALING
               ************************************************/
  
  
              // MPE INFERENCE WITH SIMULATED ANNEALING, ALL VARIABLES
              //if (Main.VERBOSE) System.out.println();
              timeStart = System.nanoTime();
              mpeInference.runInference(MPEInference.SearchAlgorithm.SA_GLOBAL);
  
  
              //mpeEstimate = mpeInference.getEstimate();
              //if (Main.VERBOSE) System.out.println("MPE estimate (SA.All): " + mpeEstimate.outputString(modelVariables));   //toString(modelVariables)
              //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate());
              timeStop = System.nanoTime();
              execTime = (double) (timeStop - timeStart) / 1000000000.0;
              //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds");
              //if (Main.VERBOSE) System.out.println(.toString(mapInference.getOriginalModel().getStaticVariables().iterator().));
              //if (Main.VERBOSE) System.out.println();
              SA_All_prob[k] = mpeInference.getLogProbabilityOfEstimate();
              SA_All_time[k] = execTime;
  
  
  
              // MPE INFERENCE WITH SIMULATED ANNEALING, SOME VARIABLES AT EACH TIME
              timeStart = System.nanoTime();
              mpeInference.runInference(MPEInference.SearchAlgorithm.SA_LOCAL);
  
  
              //mpeEstimate = mpeInference.getEstimate();
              //if (Main.VERBOSE) System.out.println("MPE estimate  (SA.Some): " + mpeEstimate.outputString(modelVariables));   //toString(modelVariables)
              //if (Main.VERBOSE) System.out.println("with probability: "+ Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate());
              timeStop = System.nanoTime();
              execTime = (double) (timeStop - timeStart) / 1000000000.0;
              //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds");
              //if (Main.VERBOSE) System.out.println(.toString(mapInference.getOriginalModel().getStaticVariables().iterator().));
              //if (Main.VERBOSE) System.out.println();
              SA_Some_prob[k] = mpeInference.getLogProbabilityOfEstimate();
              SA_Some_time[k] = execTime;
  
              /***********************************************
               *        HILL CLIMBING
               ************************************************/
  
  
              // MPE INFERENCE WITH HILL CLIMBING, ALL VARIABLES
              timeStart = System.nanoTime();
              mpeInference.runInference(MPEInference.SearchAlgorithm.HC_GLOBAL);
  
              //mpeEstimate = mpeInference.getEstimate();
              //modelVariables = mpeInference.getOriginalModel().getVariables().getListOfVariables();
              //if (Main.VERBOSE) System.out.println("MPE estimate (HC.All): " + mpeEstimate.outputString(modelVariables));
              //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate());
              timeStop = System.nanoTime();
              execTime = (double) (timeStop - timeStart) / 1000000000.0;
              //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds");
              //if (Main.VERBOSE) System.out.println();
              HC_All_prob[k] = mpeInference.getLogProbabilityOfEstimate();
              HC_All_time[k] = execTime;
  
  
  
              //  MPE INFERENCE WITH HILL CLIMBING, ONE VARIABLE AT EACH TIME
              timeStart = System.nanoTime();
              mpeInference.runInference(MPEInference.SearchAlgorithm.HC_LOCAL);
  
  
              //mpeEstimate = mpeInference.getEstimate();
              //if (Main.VERBOSE) System.out.println("MPE estimate  (HC.Some): " + mpeEstimate.outputString(modelVariables));   //toString(modelVariables)
              //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate());
              timeStop = System.nanoTime();
              execTime = (double) (timeStop - timeStart) / 1000000000.0;
              //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds");
              //if (Main.VERBOSE) System.out.println();
              HC_Some_prob[k] = mpeInference.getLogProbabilityOfEstimate();
              HC_Some_time[k] = execTime;
  
              /***********************************************
               *        SAMPLING AND DETERMINISTIC
               ************************************************/
  
  
              // MPE INFERENCE WITH SIMULATION AND PICKING MAX
  
              mpeInference.setSampleSize(samplingMethodSize);
  
              timeStart = System.nanoTime();
              mpeInference.runInference(MPEInference.SearchAlgorithm.SAMPLING);
  
              //mpeEstimate = mpeInference.getEstimate();
              //modelVariables = mpeInference.getOriginalModel().getVariables().getListOfVariables();
              //if (Main.VERBOSE) System.out.println("MPE estimate (SAMPLING): " + mpeEstimate.outputString(modelVariables));
              //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate());
              timeStop = System.nanoTime();
              execTime = (double) (timeStop - timeStart) / 1000000000.0;
              //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds");
              //if (Main.VERBOSE) System.out.println();
              sampling_prob[k] = mpeInference.getLogProbabilityOfEstimate();
              sampling_time[k] = execTime;
          }
  
          double determ_prob=0;
          double determ_time=0;
  
          if(bn.getNumberOfVars()<=50) {
  
              // MPE INFERENCE, DETERMINISTIC
              timeStart = System.nanoTime();
              //mpeInference.runInference(-2);
  
              //mpeEstimate = mpeInference.getEstimate();
              //modelVariables = mpeInference.getOriginalModel().getVariables().getListOfVariables();
              //if (Main.VERBOSE) System.out.println("MPE estimate (DETERM.): " + mpeEstimate.outputString(modelVariables));
              //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate());
              timeStop = System.nanoTime();
              execTime = (double) (timeStop - timeStart) / 1000000000.0;
              //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds");
              //if (Main.VERBOSE) System.out.println();
  
              determ_prob = mpeInference.getLogProbabilityOfEstimate();
              determ_time = execTime;
              determ_prob = -34.64743236365595;
              determ_time=0;
  
          }
          else {
              if (Main.VERBOSE) System.out.println("Too many variables for deterministic method");
          }
  
          /***********************************************
           *        DISPLAY OF RESULTS
           ************************************************/
  
          if (Main.VERBOSE) System.out.println("*** RESULTS ***");
  
  //        if (Main.VERBOSE) System.out.println("SA_All log-probabilities");
  //        if (Main.VERBOSE) System.out.println(Arrays.toString(SA_All_prob));
  //        if (Main.VERBOSE) System.out.println("SA_Some log-probabilities");
  //        if (Main.VERBOSE) System.out.println(Arrays.toString(SA_Some_prob));
  //        if (Main.VERBOSE) System.out.println("HC_All log-probabilities");
  //        if (Main.VERBOSE) System.out.println(Arrays.toString(HC_All_prob));
  //        if (Main.VERBOSE) System.out.println("HC_Some log-probabilities");
  //        if (Main.VERBOSE) System.out.println(Arrays.toString(HC_Some_prob));
  //        if (Main.VERBOSE) System.out.println("Sampling log-probabilities");
  //        if (Main.VERBOSE) System.out.println(Arrays.toString(sampling_prob));
  //
  //        if(bn.getNumberOfVars()<=50) {
  //            if (Main.VERBOSE) System.out.println("Deterministic log-probability");
  //            if (Main.VERBOSE) System.out.println(Double.toString(determ_prob));
  //        }
  //        if (Main.VERBOSE) System.out.println();
  
          final double determ_prob_FINAL = determ_prob;
  
  //        int SA_All_success = (int) Arrays.stream(SA_All_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count();
  //        int SA_Some_success = (int) Arrays.stream(SA_Some_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count();
  //        int HC_All_success = (int) Arrays.stream(HC_All_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count();
  //        int HC_Some_success = (int) Arrays.stream(HC_Some_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count();
  //        int sampling_success = (int) Arrays.stream(sampling_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count();
  //
  //        if (Main.VERBOSE) System.out.println("SA_All % success");
  //        if (Main.VERBOSE) System.out.println(Double.toString( 100 * SA_All_success/repetitions ));
  //        if (Main.VERBOSE) System.out.println("SA_Some % success");
  //        if (Main.VERBOSE) System.out.println(Double.toString( 100 * SA_Some_success/repetitions ));
  //        if (Main.VERBOSE) System.out.println("HC_All % success");
  //        if (Main.VERBOSE) System.out.println(Double.toString( 100 * HC_All_success/repetitions ));
  //        if (Main.VERBOSE) System.out.println("HC_Some % success");
  //        if (Main.VERBOSE) System.out.println(Double.toString( 100 * HC_Some_success/repetitions ));
  //        if (Main.VERBOSE) System.out.println("Sampling % success");
  //        if (Main.VERBOSE) System.out.println(Double.toString( 100 * sampling_success/repetitions ));
  //        if (Main.VERBOSE) System.out.println();
  
          if (Main.VERBOSE) System.out.println("SA_All RMS probabilities");
          if (Main.VERBOSE) System.out.println(Double.toString(Arrays.stream(SA_All_prob).map(value -> Math.pow(value - determ_prob_FINAL, 2)).average().getAsDouble()) );
          if (Main.VERBOSE) System.out.println("SA_Some RMS probabilities");
          if (Main.VERBOSE) System.out.println(Double.toString( Arrays.stream(SA_Some_prob).map(value -> Math.pow(value-determ_prob_FINAL,2)).average().getAsDouble()) );
          if (Main.VERBOSE) System.out.println("HC_All RMS probabilities");
          if (Main.VERBOSE) System.out.println(Double.toString( Arrays.stream(HC_All_prob).map(value -> Math.pow(value-determ_prob_FINAL,2)).average().getAsDouble()) );
          if (Main.VERBOSE) System.out.println("HC_Some RMS probabilities");
          if (Main.VERBOSE) System.out.println(Double.toString( Arrays.stream(HC_Some_prob).map(value -> Math.pow(value-determ_prob_FINAL,2)).average().getAsDouble()) );
          if (Main.VERBOSE) System.out.println("Sampling RMS probabilities");
          if (Main.VERBOSE) System.out.println(Double.toString( Arrays.stream(sampling_prob).map(value -> Math.pow(value-determ_prob_FINAL,2)).average().getAsDouble()) );
          if (Main.VERBOSE) System.out.println();
  
  
          if (Main.VERBOSE) System.out.println("SA_All times");
          //if (Main.VERBOSE) System.out.println(Arrays.toString(SA_All_time));
          if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(SA_All_time).average().getAsDouble()));
          if (Main.VERBOSE) System.out.println("SA_Some times");
          //if (Main.VERBOSE) System.out.println(Arrays.toString(SA_Some_time));
          if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(SA_Some_time).average().getAsDouble()));
          if (Main.VERBOSE) System.out.println("HC_All times");
          //if (Main.VERBOSE) System.out.println(Arrays.toString(HC_All_time));
          if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(HC_All_time).average().getAsDouble()));
          if (Main.VERBOSE) System.out.println("HC_Some times");
          //if (Main.VERBOSE) System.out.println(Arrays.toString(HC_Some_time));
          if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(HC_Some_time).average().getAsDouble()));
          if (Main.VERBOSE) System.out.println("Sampling times");
          //if (Main.VERBOSE) System.out.println(Arrays.toString(sampling_time));
          if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(sampling_time).average().getAsDouble()));
  
          if(bn.getNumberOfVars()<=50) {
              if (Main.VERBOSE) System.out.println("Deterministic time");
              if (Main.VERBOSE) System.out.println(Double.toString(determ_time));
              if (Main.VERBOSE) System.out.println("and probability");
              if (Main.VERBOSE) System.out.println(determ_prob);
          }
  
  
      }
  }
  