Java tutorial
/* * * * 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.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_Deliv2 { 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 = 234235; int numberOfGaussians = 100; int numberOfMultinomials = 100; int seed = 125634; 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.setNumberOfIterations(numberOfIterations); mpeInference.setSampleSize(parallelSamples); mpeInference.setSeed(seed); 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 bestMpeEstimate = new HashMapAssignment(bn.getNumberOfVars()); double bestMpeEstimateLogProb = -100000; int bestMpeEstimateMethod = -5; mpeInference.setParallelMode(true); final double bestProbability = -171.81983739975342; // BEST MPE ESTIMATE FOUND: // {DiscreteVar0 = 0, DiscreteVar1 = 0, DiscreteVar2 = 1, DiscreteVar3 = 0, DiscreteVar4 = 0, DiscreteVar5 = 0, DiscreteVar6 = 0, DiscreteVar7 = 0, DiscreteVar8 = 0, DiscreteVar9 = 0, DiscreteVar10 = 0, DiscreteVar11 = 1, DiscreteVar12 = 1, DiscreteVar13 = 1, DiscreteVar14 = 0, DiscreteVar15 = 0, DiscreteVar16 = 0, DiscreteVar17 = 1, DiscreteVar18 = 1, DiscreteVar19 = 0, DiscreteVar20 = 0, DiscreteVar21 = 0, DiscreteVar22 = 1, DiscreteVar23 = 1, DiscreteVar24 = 0, DiscreteVar25 = 0, DiscreteVar26 = 0, DiscreteVar27 = 0, DiscreteVar28 = 1, DiscreteVar29 = 1, DiscreteVar30 = 0, DiscreteVar31 = 0, DiscreteVar32 = 1, DiscreteVar33 = 1, DiscreteVar34 = 0, DiscreteVar35 = 1, DiscreteVar36 = 0, DiscreteVar37 = 0, DiscreteVar38 = 0, DiscreteVar39 = 0, DiscreteVar40 = 0, DiscreteVar41 = 1, DiscreteVar42 = 1, DiscreteVar43 = 1, DiscreteVar44 = 0, DiscreteVar45 = 1, DiscreteVar46 = 1, DiscreteVar47 = 0, DiscreteVar48 = 1, DiscreteVar49 = 1, DiscreteVar50 = 0, DiscreteVar51 = 0, DiscreteVar52 = 0, DiscreteVar53 = 1, DiscreteVar54 = 0, DiscreteVar55 = 1, DiscreteVar56 = 1, DiscreteVar57 = 0, DiscreteVar58 = 1, DiscreteVar59 = 0, DiscreteVar60 = 0, DiscreteVar61 = 1, DiscreteVar62 = 0, DiscreteVar63 = 0, DiscreteVar64 = 0, DiscreteVar65 = 1, DiscreteVar66 = 1, DiscreteVar67 = 1, DiscreteVar68 = 1, DiscreteVar69 = 1, DiscreteVar70 = 1, DiscreteVar71 = 0, DiscreteVar72 = 0, DiscreteVar73 = 0, DiscreteVar74 = 0, DiscreteVar75 = 1, DiscreteVar76 = 0, DiscreteVar77 = 1, DiscreteVar78 = 1, DiscreteVar79 = 0, DiscreteVar80 = 1, DiscreteVar81 = 1, DiscreteVar82 = 1, DiscreteVar83 = 0, DiscreteVar84 = 1, DiscreteVar85 = 1, DiscreteVar86 = 1, DiscreteVar87 = 1, DiscreteVar88 = 0, DiscreteVar89 = 0, DiscreteVar90 = 1, DiscreteVar91 = 0, DiscreteVar92 = 0, DiscreteVar93 = 0, DiscreteVar94 = 0, DiscreteVar95 = 0, DiscreteVar96 = 0, DiscreteVar97 = 1, DiscreteVar98 = 1, DiscreteVar99 = 1, GaussianVar0 = -4,551, GaussianVar1 = 14,731, GaussianVar2 = -1,108, GaussianVar3 = -6,564, GaussianVar4 = -2,415, GaussianVar5 = 10,265, GaussianVar6 = 6,058, GaussianVar7 = 6,367, GaussianVar8 = 26,731, GaussianVar9 = 0,807, GaussianVar10 = -19,410, GaussianVar11 = 18,070, GaussianVar12 = -14,177, GaussianVar13 = 7,765, GaussianVar14 = 3,596, GaussianVar15 = -7,757, GaussianVar16 = -1,705, GaussianVar17 = -5,476, GaussianVar18 = -17,932, GaussianVar19 = 22,843, GaussianVar20 = -9,860, GaussianVar21 = 3,844, GaussianVar22 = 8,262, GaussianVar23 = -9,080, GaussianVar24 = 1,750, GaussianVar25 = 11,532, GaussianVar26 = 0,700, GaussianVar27 = 12,206, GaussianVar28 = 8,532, GaussianVar29 = -40,395, GaussianVar30 = 19,981, GaussianVar31 = -30,713, GaussianVar32 = 0,476, GaussianVar33 = -12,406, GaussianVar34 = 4,942, GaussianVar35 = -0,245, GaussianVar36 = -176,861, GaussianVar37 = 8,474, GaussianVar38 = -8,849, GaussianVar39 = -3,844, GaussianVar40 = -8,495, GaussianVar41 = 4,664, GaussianVar42 = -4,730, GaussianVar43 = 4,063, GaussianVar44 = -1,631, GaussianVar45 = -103,340, GaussianVar46 = -1,598, GaussianVar47 = -11,460, GaussianVar48 = 14,123, GaussianVar49 = -0,135, GaussianVar50 = 1,487, GaussianVar51 = -4,859, GaussianVar52 = 0,370, GaussianVar53 = -10,038, GaussianVar54 = 18,145, GaussianVar55 = 225,324, GaussianVar56 = 1,059, GaussianVar57 = -1,170, GaussianVar58 = 83,480, GaussianVar59 = 7,375, GaussianVar60 = 5,091, GaussianVar61 = 61,381, GaussianVar62 = 42,955, GaussianVar63 = -712,533, GaussianVar64 = 21,460, GaussianVar65 = -19,337, GaussianVar66 = 213,903, GaussianVar67 = -10,197, GaussianVar68 = -65,619, GaussianVar69 = 41,045, GaussianVar70 = 133,452, GaussianVar71 = -1,997, GaussianVar72 = 17,485, GaussianVar73 = -40,691, GaussianVar74 = -16,378, GaussianVar75 = -72,550, GaussianVar76 = -1,761, GaussianVar77 = 12,647, GaussianVar78 = -31,531, GaussianVar79 = -41,444, GaussianVar80 = -14,190, GaussianVar81 = 17,387, GaussianVar82 = -12,333, GaussianVar83 = -57,795, GaussianVar84 = -20,386, GaussianVar85 = 49,735, GaussianVar86 = 14,593, GaussianVar87 = -168,778, GaussianVar88 = -6,157, GaussianVar89 = 82,897, GaussianVar90 = -30,018, GaussianVar91 = -2,366, GaussianVar92 = -12,753, GaussianVar93 = -141,490, GaussianVar94 = 17,844, GaussianVar95 = 99,703, GaussianVar96 = -37,859, GaussianVar97 = 123,045, GaussianVar98 = -4,054, GaussianVar99 = 3,024} // with method:2 // and log probability: -171.81983739975342 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; if (mpeInference.getLogProbabilityOfEstimate() > bestMpeEstimateLogProb) { bestMpeEstimate = mpeInference.getEstimate(); bestMpeEstimateLogProb = mpeInference.getLogProbabilityOfEstimate(); bestMpeEstimateMethod = 1; } // 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; if (mpeInference.getLogProbabilityOfEstimate() > bestMpeEstimateLogProb) { bestMpeEstimate = mpeInference.getEstimate(); bestMpeEstimateLogProb = mpeInference.getLogProbabilityOfEstimate(); bestMpeEstimateMethod = 0; } /*********************************************** * 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; if (mpeInference.getLogProbabilityOfEstimate() > bestMpeEstimateLogProb) { bestMpeEstimate = mpeInference.getEstimate(); bestMpeEstimateLogProb = mpeInference.getLogProbabilityOfEstimate(); bestMpeEstimateMethod = 3; } // 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; if (mpeInference.getLogProbabilityOfEstimate() > bestMpeEstimateLogProb) { bestMpeEstimate = mpeInference.getEstimate(); bestMpeEstimateLogProb = mpeInference.getLogProbabilityOfEstimate(); bestMpeEstimateMethod = 2; } /*********************************************** * 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; if (mpeInference.getLogProbabilityOfEstimate() > bestMpeEstimateLogProb) { bestMpeEstimate = mpeInference.getEstimate(); bestMpeEstimateLogProb = mpeInference.getLogProbabilityOfEstimate(); bestMpeEstimateMethod = -1; } } 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; // // } // 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("SA_All RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Math.sqrt(Arrays.stream(SA_All_prob) .map(value -> Math.pow(value - bestProbability, 2)).average().getAsDouble()))); if (Main.VERBOSE) System.out.println("SA_Some RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Math.sqrt(Arrays.stream(SA_Some_prob) .map(value -> Math.pow(value - bestProbability, 2)).average().getAsDouble()))); if (Main.VERBOSE) System.out.println("HC_All RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Math.sqrt(Arrays.stream(HC_All_prob) .map(value -> Math.pow(value - bestProbability, 2)).average().getAsDouble()))); if (Main.VERBOSE) System.out.println("HC_Some RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Math.sqrt(Arrays.stream(HC_Some_prob) .map(value -> Math.pow(value - bestProbability, 2)).average().getAsDouble()))); if (Main.VERBOSE) System.out.println("Sampling RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Math.sqrt(Arrays.stream(sampling_prob) .map(value -> Math.pow(value - bestProbability, 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 (Main.VERBOSE) System.out.println(); // 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("BEST MPE ESTIMATE FOUND:"); if (Main.VERBOSE) System.out.println(bestMpeEstimate.outputString(Utils.getTopologicalOrder(bn.getDAG()))); if (Main.VERBOSE) System.out.println("with method:" + bestMpeEstimateMethod); if (Main.VERBOSE) System.out.println("and log probability: " + bestMpeEstimateLogProb); } }