List of usage examples for org.apache.commons.math3.stat.descriptive.moment Variance Variance
public Variance()
isBiasCorrected property. From source file:com.analog.lyric.dimple.test.model.TestJunctionTreeTransform.java
/** * Assert that source and target graphs in {@code transformMap} represent the same * joint distribution down to some level of precision. * /* w w w . j a v a 2s . c om*/ * @param transformMap */ @SuppressWarnings("null") private void assertModelsEquivalent(JunctionTreeTransformMap transformMap) { if (transformMap.isIdentity()) { return; } final FactorGraph source = transformMap.source(); final FactorGraph target = transformMap.target(); GibbsSolver gibbs = new GibbsSolver(); GibbsSolverGraph sourceGibbs = source.setSolverFactory(gibbs); GibbsSolverGraph targetGibbs = target.setSolverFactory(gibbs); targetGibbs.initialize(); final int nSamples = 100; final double[] differences = new double[nSamples]; for (int n = 0; n < nSamples; ++n) { // Generate a sample on the source graph source.solve(); // Copy sample values to new graph for (Variable sourceVar : source.getVariables()) { Variable targetVar = transformMap.sourceToTargetVariable(sourceVar); ISolverVariableGibbs sourceSVar = sourceGibbs.getSolverVariable(sourceVar); ISolverVariableGibbs targetSVar = targetGibbs.getSolverVariable(targetVar); targetSVar.setCurrentSample(sourceSVar.getCurrentSampleValue()); } // Update values of added variables for (AddedJointVariable<?> added : transformMap.addedJointVariables()) { final ISolverVariableGibbs addedSVar = targetGibbs.getSolverVariable(added.getVariable()); final Value value = addedSVar.getCurrentSampleValue(); final Value[] inputs = new Value[added.getInputCount()]; for (int i = inputs.length; --i >= 0;) { final Variable inputVar = added.getInput(i); final ISolverVariableGibbs inputSVar = targetGibbs.getSolverVariable(inputVar); inputs[i] = inputSVar.getCurrentSampleValue(); } added.updateValue(value, inputs); } // Compare the joint likelihoods final double sourceEnergy = sourceGibbs.getSampleScore(); final double targetEnergy = targetGibbs.getSampleScore(); final double difference = sourceEnergy - targetEnergy; if (Math.abs(difference) > 1e-10) { Misc.breakpoint(); } differences[n] = difference; } double variance = new Variance().evaluate(differences); assertEquals(0.0, variance, 1e-10); }
From source file:feature.lowLevel.audio.FeatureExtractor.java
private double[] getStatisticalSpectrumDescriptor(double[] dataRow) { int N = dataRow.length; double ssd[] = new double[N_STATISTICAL_DESCRIPTORS]; ssd[0] = new Mean().evaluate(dataRow); ssd[1] = new Variance().evaluate(dataRow); ssd[2] = new Skewness().evaluate(dataRow); ssd[3] = new Kurtosis().evaluate(dataRow); // NOTE: be careful, sort changes data! // (as sonogram.getRow() above copies the data anyway, in this case there is no problem) // (otherwise, use dataRow.clone(); ) Arrays.sort(dataRow);//from w w w .j av a 2 s .c o m // median if (N % 2 == 0) { ssd[4] = (dataRow[(N / 2) - 1] + dataRow[(N / 2)]) / 2; } else { ssd[4] = dataRow[(N - 1) / 2]; } ssd[5] = dataRow[0]; // min value ssd[6] = dataRow[N - 1]; // max value return ssd; }
From source file:gamlss.utilities.WLSMultipleLinearRegression.java
/** * Calculates the variance of the y values. * * @return Y variance or Double.NaN if no applicable *///from w w w . j ava 2 s.c o m @Override protected double calculateYVariance() { if (copyOriginal) { return new Variance().evaluate(y.toArray()); } return Double.NaN; }
From source file:Clustering.technique.KMeansPlusPlusClusterer.java
/** * Get a random point from the {@link Cluster} with the largest distance variance. * * @param clusters the {@link Cluster}s to search * @return a random point from the selected cluster * @throws ConvergenceException if clusters are all empty */// w w w . ja v a 2s .co m private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters) throws ConvergenceException { double maxVariance = Double.NEGATIVE_INFINITY; Cluster<T> selected = null; for (final CentroidCluster<T> cluster : clusters) { if (!cluster.getPoints().isEmpty()) { // compute the distance variance of the current cluster final Clusterable center = cluster.getCenter(); final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center)); } final double variance = stat.getResult(); // select the cluster with the largest variance if (variance > maxVariance) { maxVariance = variance; selected = cluster; } } } // did we find at least one non-empty cluster ? if (selected == null) { throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS); } // extract a random point from the cluster final List<T> selectedPoints = selected.getPoints(); return selectedPoints.remove(random.nextInt(selectedPoints.size())); }
From source file:nars.concept.util.BeliefClusterer.java
/** * Get a random point from the {@link Cluster} with the largest distance variance. * * @param clusters the {@link Cluster}s to search * @return a random point from the selected cluster * @throws ConvergenceException if clusters are all empty *//*from w w w. ja va2s. co m*/ @NotNull private ArrayRealVector getPointFromLargestVarianceCluster(@NotNull final Collection<Cluster> clusters) throws ConvergenceException { double maxVariance = Double.NEGATIVE_INFINITY; Cluster selected = null; for (final Cluster cluster : clusters) { if (!cluster.getPoints().isEmpty()) { // compute the distance variance of the current cluster final ArrayRealVector center = cluster.pos; final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center.getDataRef())); } final double variance = stat.getResult(); // select the cluster with the largest variance if (variance > maxVariance) { maxVariance = variance; selected = cluster; } } } // did we find at least one non-empty cluster ? if (selected == null) { throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS); } // extract a random point from the cluster final List<T> selectedPoints = selected.getPoints(); return p(selectedPoints.remove(random.nextInt(selectedPoints.size()))); }
From source file:nl.systemsgenetics.eqtlinteractionanalyser.eqtlinteractionanalyser.TestEQTLDatasetForInteractions.java
private void correctExpressionDataForInteractions(String[] covsToCorrect, ExpressionDataset datasetCovariates, ExpressionDataset datasetGenotypes, int nrSamples, ExpressionDataset datasetExpression, OLSMultipleLinearRegression regression, HashMultimap<String, String> qtlProbeSnpMultiMap) throws MathIllegalArgumentException, Exception { System.out.println("Correcting expression data for predefined gene environment interaction effects: " + Arrays.toString(covsToCorrect)); int[] covsToCorrectIndex = new int[covsToCorrect.length]; for (int c = 0; c < covsToCorrect.length; c++) { covsToCorrectIndex[c] = ((Integer) datasetCovariates.hashProbes.get(covsToCorrect[c])).intValue(); }/*from ww w . jav a 2 s . c o m*/ HashMap<String, Integer> snpMap = new HashMap<String, Integer>(datasetGenotypes.nrProbes); for (Map.Entry<String, Integer> snpEntry : datasetGenotypes.hashProbes.entrySet()) { try { snpMap.put(snpEntry.getKey().substring(0, snpEntry.getKey().indexOf('_')), snpEntry.getValue()); } catch (Exception e) { System.out.println(snpEntry.getKey()); throw e; } } Variance v = new Variance(); for (int p = 0; p < datasetExpression.nrProbes; p++) { String probe = datasetExpression.probeNames[p].substring(0, datasetExpression.probeNames[p].lastIndexOf('_')); Set<String> probeQtls = qtlProbeSnpMultiMap.get(probe); if (probeQtls.isEmpty()) { throw new Exception("No eQTLs found for: " + probe); } int snpsInData = 0; HashSet<String> excludedSnps = new HashSet<String>(); for (String snp : probeQtls) { Integer s = snpMap.get(snp); if (s != null) { if (v.evaluate(datasetGenotypes.rawData[s]) > 0) { ++snpsInData; } else { excludedSnps.add(snp); } } } //boolean foundPisS = false; double[][] valsX = new double[nrSamples][snpsInData + covsToCorrect.length * 2]; //store genotypes, covariates, interactions int k = 0; for (String snp : probeQtls) { if (excludedSnps.contains(snp)) { continue; } Integer s = snpMap.get(snp); if (s == null) { //throw new Exception("Snp " + snp + " not found"); continue; } // if(s.intValue() == p){ // foundPisS = true; // } double[] snpData = datasetGenotypes.rawData[s]; for (int i = 0; i < datasetGenotypes.nrSamples; ++i) { valsX[i][k] = snpData[i]; } k++; } // if(!foundPisS){ // // System.out.println("Expected snp: " + datasetGenotypes.probeNames[p] + " at index: " + p); // // for(String qtlSnp : probeQtls = qtlProbeSnpMultiMap.get(probe)){ // System.out.println("QTL snp: " + qtlSnp + " found at index: " + snpMap.get(qtlSnp)); // } // // throw new Exception("Error 2"); // } for (int c = 0; c < covsToCorrect.length; c++) { double[] covData = datasetCovariates.rawData[covsToCorrectIndex[c]]; double[] snpData = datasetGenotypes.rawData[p]; for (int s = 0; s < nrSamples; s++) { valsX[s][c * 2 + snpsInData] = covData[s]; //covariate valsX[s][c * 2 + snpsInData + 1] = snpData[s] * covData[s]; //interction } } double[] valsY = datasetExpression.rawData[p]; regression.newSampleData(valsY, valsX); try { datasetExpression.rawData[p] = regression.estimateResiduals(); } catch (Exception up) { System.err.println( "Error correcting for interactions: " + probe + " - " + datasetGenotypes.probeNames[p]); } } }
From source file:nl.systemsgenetics.genenetworkbackend.hpo.TestDiseaseGenePerformance.java
private static HashMap<String, MeanSd> calculatePathayMeansOfAnnotatedGenes( DoubleMatrixDataset<String, String> predictionMatrixSignificant, DoubleMatrixDataset<String, String> annotationMatrixSignificant) { HashMap<String, MeanSd> pathwayMeanSdMap = new HashMap<>(predictionMatrixSignificant.columns()); Mean meanCalculator = new Mean(); Variance varianceCalculator = new Variance(); for (String pathway : predictionMatrixSignificant.getColObjects()) { meanCalculator.clear();// w w w .j a va2s.com varianceCalculator.clear(); DoubleMatrix1D pathwayPredictions = predictionMatrixSignificant.getCol(pathway); DoubleMatrix1D pathwayAnnotations = annotationMatrixSignificant.getCol(pathway); for (int g = 0; g < pathwayPredictions.size(); ++g) { if (pathwayAnnotations.get(g) != 0) { meanCalculator.increment(pathwayPredictions.getQuick(g)); varianceCalculator.increment(pathwayPredictions.getQuick(g)); } } double v = varianceCalculator.getResult(); pathwayMeanSdMap.put(pathway, new MeanSd(meanCalculator.getResult(), v * v)); } return pathwayMeanSdMap; }
From source file:org.hawkular.datamining.forecast.stats.AutoCorrelationFunction.java
private static double[] evaluate(final double[] x, int maxLag, boolean correlation) { // max lag = length - 1 if (maxLag >= x.length) { throw new IllegalArgumentException("Lag is higher than "); }/*from www.j a v a2 s . c om*/ double mean = new Mean().evaluate(x); double var = correlation ? new Variance().evaluate(x, mean) : 1; int lengthForMean = correlation ? x.length - 1 : x.length; double[] acf = new double[maxLag + 1]; for (int lag = 0; lag < maxLag + 1; lag++) { double sum = 0; for (int i = 0; i + lag < x.length; i++) { sum += (x[i] - mean) * (x[i + lag] - mean); } sum /= lengthForMean; acf[lag] = sum / var; } return acf; }
From source file:pt.minha.calibration.AbstractBenchmark.java
protected Result stop(boolean interarrival) { double cpu = ((double) (mxbean.getCurrentThreadCpuTime() - cputime)) / idx; if (interarrival) { for (int i = idx - 1; i > 0; i--) times[i] -= times[i - 1];/*from w ww . ja v a2s . co m*/ begin = 1; } Mean mean = new Mean(); double m = mean.evaluate(times, begin, idx - begin); Variance var = new Variance(); double v = var.evaluate(times, m, begin, idx - begin); return new Result(m, v, cpu); }