/** * Creates a new BayesianNetworkSampler given an input {@link BayesianNetwork} object. * @param bn an input {@link BayesianNetwork} object. */ public BayesianNetworkSampler(BayesianNetwork bn){ this.network = bn; localSampler = new eu.amidst.core.utils.BayesianNetworkSampler(bn); }
/** * Sets a given {@link Variable} object as noisy. * @param var a given {@link Variable} object. * @param noiseProb a double that represents the noise probability. */ public void setMARVar(Variable var, double noiseProb){ this.localSampler.setMARVar(var, noiseProb);}
/** * Creates a new FactoredFrontierForDBN object. * @param inferenceAlgorithm an {@link InferenceAlgorithm} object. */ public FactoredFrontierForDBN(InferenceAlgorithm inferenceAlgorithm){ infAlgTime0 = inferenceAlgorithm; infAlgTimeT = Serialization.deepCopy(inferenceAlgorithm); timeID = -1; this.setSeed(0); }
/** * Sets a given {@link Variable} object as hidden. * @param var a given {@link Variable} object. */ public void setHiddenVar(Variable var) { this.localSampler.setHiddenVar(var); }
public static void main(String[] args) throws Exception { OptionParser.setArgsOptions(ExperimentsParallelML.class, args); ExperimentsParallelML.loadOptions(); compareNumberOfCores(); } }
@Override public byte[] map(T value) throws Exception { return Serialization.serializeObject(value); } });
/** * Sets a given {@link Variable} object as latent. A latent variable doesn't contain an attribute and therefore * doesn't generate a sampling value. * @param var a given {@link Variable} object. */ public void setLatentVar(Variable var){ this.localSampler.setLatentVar(var); }
public static void main(String[] args) throws IOException { DataStream<DataInstance> data = eu.amidst.core.utils.DataSetGenerator.generate(0,1000,0,10); DataStreamWriter.writeDataToFile(data, "./datasets/artificialDataset.arff"); // DataStream<DynamicDataInstance> dataDynamic = eu.amidst.dynamic.utils.DataSetGenerator.generate(0,100, 100,0,10); // DataStreamWriter.writeDataToFile(dataDynamic, "./datasets/artificialDatasetDynamic.arff"); } }
@Override public T map(byte[] value) throws Exception { return Serialization.deserializeObject(value); } });
/** * Returns a parallel {@link Stream} of {@link DataSequence}. * @param dataStream a DataStream object. * @return a Stream object. */ public static Stream<DataSequence> parallelStreamOfDataSequences(DataStream<DynamicDataInstance> dataStream){ return FixedBatchParallelSpliteratorWrapper.toFixedBatchStream(DataSequenceStream.streamOfDataSequences(dataStream), 1); }
/** * Creates a new BayesianNetworkSampler given an input {@link BayesianNetwork} object. * @param network1 an input {@link BayesianNetwork} object. */ public BayesianNetworkSampler(BayesianNetwork network1){ this.network = network1; localSampler = new eu.amidst.core.utils.BayesianNetworkSampler(network1); }
/** * Sets a given {@link Variable} object as noisy. * @param var a given {@link Variable} object. * @param noiseProb a double that represents the noise probability. */ public void setMARVar(Variable var, double noiseProb){ this.localSampler.setMARVar(var, noiseProb);}
/** * Sets a given {@link Variable} object as hidden. * @param var a given {@link Variable} object. */ public void setHiddenVar(Variable var) { this.localSampler.setHiddenVar(var); }
public static void main(String[] args) throws Exception { OptionParser.setArgsOptions(ExperimentsParallelML.class, args); ExperimentsParallelML.loadOptions(); if(isCoreComparison()) compareNumberOfCores(); else compareBatchSizes(); }