List of usage examples for org.apache.commons.math3.stat.descriptive.moment Variance evaluate
@Override public double evaluate(final double[] values) throws MathIllegalArgumentException
Double.NaN if the array is empty. From source file:cz.cuni.mff.d3s.spl.utils.StatisticsUtils.java
/** Compute variance of given data with bias correction. * //from w w w . j av a2 s.c o m * @param values Array of values to compute the variance from. * @return Varince of the provided values. */ public static double variance(double... values) { Variance var = new Variance(); return var.evaluate(values); }
From source file:cz.cuni.mff.d3s.spl.utils.StatisticsUtils.java
/** Compute variance of given data without bias correction. * // w w w . j av a 2s .c o m * @param values Array of values to compute the variance from. * @return Varince of the provided values. */ public static double varianceN(double... values) { Variance var = new Variance(false); return var.evaluate(values); }
From source file:hyperheuristics.algorithm.moeadfrrmab.UCBSelectorVariance.java
protected double calcVariance(double[] values) { Variance variancia = new Variance(); return variancia.evaluate(values); }
From source file:com.facebook.presto.operator.aggregation.TestDoubleVarianceAggregation.java
@Override public Number getExpectedValue(int start, int length) { if (length < 2) { return null; }//from w w w. j a va2 s. c o m double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Variance variance = new Variance(); return variance.evaluate(values); }
From source file:com.facebook.presto.operator.aggregation.TestDoubleVariancePopAggregation.java
@Override public Number getExpectedValue(int start, int length) { if (length == 0) { return null; }/* w w w. j a v a 2s .c o m*/ double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Variance variance = new Variance(false); return variance.evaluate(values); }
From source file:cz.cuni.mff.d3s.spl.data.BenchmarkRunSummary.java
/** Compute variance of the samples. * /* w w w . j av a2 s . c o m*/ * @return Variance of the data in the original benchmark run. */ public synchronized double getVariance() { if (cacheVariance == null) { Variance mean = new Variance(); cacheVariance = mean.evaluate(data); } return cacheVariance; }
From source file:aml.filter.InteractiveSelector.java
private boolean disagreement(int sourceId, int targetId) { double[] signatureVector = new double[aligns.size()]; int index = 0; for (Alignment a : aligns) signatureVector[index++] = a.getSimilarity(sourceId, targetId); Variance v = new Variance(); double variance = v.evaluate(signatureVector); if (variance < 0.041) { previousAgreement = false;/*from w w w. j a v a 2s. c om*/ previousFeedback = 0; previousSignatureVector = signatureVector; return false; } else if (previousAgreement == true && previousFeedback == -1 && previousSignatureVector == signatureVector) //vectorsMatch(previousSignatureVector,signatureVector)) { previousAgreement = true; previousFeedback = -1; previousSignatureVector = signatureVector; return false; } else { previousAgreement = true; previousSignatureVector = signatureVector; return true; } }
From source file:br.unicamp.ic.recod.gpsi.measures.gpsiNormalBhattacharyyaDistanceScore.java
@Override public double score(double[][][] input) { Mean mean = new Mean(); Variance var = new Variance(); double mu0, sigs0, mu1, sigs1; double dist[][] = new double[2][]; dist[0] = MatrixUtils.createRealMatrix(input[0]).getColumn(0); dist[1] = MatrixUtils.createRealMatrix(input[1]).getColumn(0); mu0 = mean.evaluate(dist[0]);/*from ww w . j a v a2s . c o m*/ sigs0 = var.evaluate(dist[0]) + Double.MIN_VALUE; mu1 = mean.evaluate(dist[1]); sigs1 = var.evaluate(dist[1]) + Double.MIN_VALUE; double distance = (Math.log((sigs0 / sigs1 + sigs1 / sigs0 + 2) / 4) + (Math.pow(mu1 - mu0, 2.0) / (sigs0 + sigs1))) / 4; return distance == Double.POSITIVE_INFINITY ? 0 : distance; }
From source file:com.std.Index.java
@Override public void calculate_beta(YStockQuote sp500, int timeFrame) { double[] sp500Col = Arrays.copyOfRange(sp500.get_historical_rate_of_return(), 0, timeFrame); calculate_historical_rate_of_return(timeFrame); sp500Col = Arrays.copyOfRange(sp500.get_historical_rate_of_return(), 0, this.historical_rate_of_return.length); Covariance covarianceObj = new Covariance(); Variance varianceObj = new Variance(false); double covariance = covarianceObj.covariance(historical_rate_of_return, sp500Col); double variance = varianceObj.evaluate(sp500Col); this.Beta = String.valueOf(Math.round((covariance / variance) * 100.0) / 100.0); }
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.j a va 2 s .c om 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]); } } }