List of usage examples for org.apache.commons.math3.stat.descriptive.moment Variance Variance
public Variance()
isBiasCorrected property. From source file:eu.crisis_economics.abm.model.configuration.LogNormalDistributionModelParameterConfiguration.java
/** * A lightweight test for this {@link ConfigurationComponent}. This snippet * creates a {@link Parameter}{@code <Double>} using an instance of * {@link LogNormalDistributionModelParameterConfiguration} and then tests * whether the logarithm of {@link Double} values drawn from this {@link Parameter} * have the expected mean./*from ww w.jav a 2 s.co m*/ */ public static void main(String[] args) { { final LogNormalDistributionModelParameterConfiguration configuration = new LogNormalDistributionModelParameterConfiguration(); configuration.setMu(5.0); configuration.setSigma(0.1); final ModelParameter<Double> distribution = configuration.createInjector() .getInstance(Key.get(new TypeLiteral<ModelParameter<Double>>() { })); double mean = 0.; final int numSamples = 10000000; for (int i = 0; i < numSamples; ++i) { final double value = Math.log(distribution.get()); mean += value; } mean /= numSamples; Assert.assertTrue(Math.abs(mean - 5.) < 1.e-3); } { final LogNormalDistributionModelParameterConfiguration configuration = LogNormalDistributionModelParameterConfiguration .create(5., .1); final ModelParameter<Double> distribution = configuration.createInjector() .getInstance(Key.get(new TypeLiteral<ModelParameter<Double>>() { })); final Variance variance = new Variance(); double mean = 0.; final int numSamples = 10000000; for (int i = 0; i < numSamples; ++i) { final double value = distribution.get(); mean += value; variance.increment(value); } mean /= numSamples; final double observedSigma = Math.sqrt(variance.getResult()); Assert.assertTrue(Math.abs(mean - 5.) < 1.e-3); Assert.assertTrue(Math.abs(observedSigma - .1) < 1.e-3); } }
From source file:cz.cuni.mff.d3s.spl.utils.StatisticsUtils.java
/** Compute variance of given data with bias correction. * //www . ja v a2 s . c om * @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: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]);/*w ww . jav a 2s . co 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: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; }/*w w w . j av a 2 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:GeMSE.GS.Analysis.Stats.TwoSampleTTestPanel.java
public TwoSampleTTestPanel() { initComponents();//from w ww . j ava 2 s . c om _sample1 = new double[0]; _sample2 = new double[0]; _decFor = new DecimalFormat("#.#########"); _decFor.setRoundingMode(RoundingMode.CEILING); DecimalFormatSymbols decFors = _decFor.getDecimalFormatSymbols(); decFors.setNaN("NaN"); decFors.setInfinity(""); _decFor.setDecimalFormatSymbols(decFors); _variance = new Variance(); _studentTest = new TTest(); }
From source file:cz.cuni.mff.d3s.spl.data.BenchmarkRunSummary.java
/** Compute variance of the samples. * /*ww w. j ava2 s .co 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:com.github.pitzcarraldo.dissimilar.Dissimilar.java
/** * Calculate the SSIM value on a window of pixels * @param pLumaOne luma values for first image * @param pLumaTwo luma values for second image * @param pWidth width of the two images * @param pHeight height of the two images * @param pWindowSize window size// w w w . j a v a 2s . com * @param pStartX start x coordinate for the window * @param pStartY start y coordinate for the window * @return SSIM for the window */ private static double calcSSIMOnWindow(final double[] pLumaOne, final double[] pLumaTwo, final int pWidth, final int pHeight, final int pWindowSize, final int pStartX, final int pStartY) { final double k1 = 0.01; final double k2 = 0.03; final double L = Math.pow(2, 8) - 1;//255 (at least for the moment all pixel values are 8-bit) final double c1 = Math.pow(k1 * L, 2); final double c2 = Math.pow(k2 * L, 2); final int windowWidth = ((pWindowSize + pStartX) > pWidth) ? (pWidth - pStartX) : (pWindowSize); final int windowHeight = ((pWindowSize + pStartY) > pHeight) ? (pHeight - pStartY) : (pWindowSize); if (windowWidth <= 0 || windowHeight <= 0) { System.out.println(pWidth + " " + pStartX + " " + windowWidth + " " + windowHeight); System.out.println(pHeight + " " + pStartY + " " + windowWidth + " " + windowHeight); } //store a temporary copy of the pixels double[] pixelsOne = new double[windowWidth * windowHeight]; double[] pixelsTwo = new double[windowWidth * windowHeight]; for (int h = 0; h < windowHeight; h++) { for (int w = 0; w < windowWidth; w++) { pixelsOne[(h * windowWidth) + w] = pLumaOne[(pStartY + h) * pWidth + pStartX + w]; pixelsTwo[(h * windowWidth) + w] = pLumaTwo[(pStartY + h) * pWidth + pStartX + w]; } } final double ux = new Mean().evaluate(pixelsOne, 0, pixelsOne.length); final double uy = new Mean().evaluate(pixelsTwo, 0, pixelsTwo.length); final double o2x = new Variance().evaluate(pixelsOne); final double o2y = new Variance().evaluate(pixelsTwo); final double oxy = new Covariance().covariance(pixelsOne, pixelsTwo); final double num = (2 * ux * uy + c1) * (2 * oxy + c2); final double den = (ux * ux + uy * uy + c1) * (o2x + o2y + c2); final double ssim = num / den; return ssim; }
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 . ja 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:com.github.pitzcarraldo.dissimilar.Dissimilar.java
/** * Calculate the SSIM; see http://en.wikipedia.org/wiki/Structural_similarity * @param pOne array of integer pixel values for first image * @param pTwo array of integer pixel values for second image * @param pWidth width of the two images * @param pHeight height of the two images * @param pGreyscale if the images are greyscale * @param pHeatMapFilename filename for the ssim heatmap image to be saved to (png) * @param pMin list to hold return value for ssim-minimum * @param pVariance list to hold return value for ssim-variance * @return SSIM//from w ww . ja v a 2s.c om */ public static double calcSSIM(final int[] pOne, final int[] pTwo, final int pWidth, final int pHeight, final boolean pGreyscale, final String pHeatMapFilename, List<Double> pMin, List<Double> pVariance) { if (!checkPair(pOne, pTwo)) return -1; double[] lumaOne = null; double[] lumaTwo = null; //if the image is greyscale then don't extract the luma if (pGreyscale) { System.out.println("=> Greyscale"); lumaOne = new double[pOne.length]; lumaTwo = new double[pTwo.length]; for (int i = 0; i < lumaOne.length; i++) { //all rgb values are the same lumaOne[i] = (pOne[i] >> 0) & 0xFF; lumaTwo[i] = (pTwo[i] >> 0) & 0xFF; } } else { lumaOne = calcLuma(pOne); lumaTwo = calcLuma(pTwo); } final int windowSize = SSIMWINDOWSIZE; final int windowsH = (int) Math.ceil((double) pHeight / windowSize); final int windowsW = (int) Math.ceil((double) pWidth / windowSize); double[] mssim = new double[windowsH * windowsW]; double mean = 0; double min = 1; for (int height = 0; height < windowsH; height++) { for (int width = 0; width < windowsW; width++) { final int window = (height * windowsW) + width; mssim[window] = calcSSIMOnWindow(lumaOne, lumaTwo, pWidth, pHeight, windowSize, width * windowSize, height * windowSize); mean += mssim[window]; if (mssim[window] < min) { min = mssim[window]; } } } final double variance = new Variance().evaluate(mssim); mean /= (windowsH * windowsW); //if(variance>0.001) System.out.println("warning: high variance"); if (null != pHeatMapFilename) { dumpSSIMHeatMap(mssim, windowsH, windowsW, pHeatMapFilename); } if (null != pMin) { pMin.add(0, new Double(min)); } if (null != pVariance) { pVariance.add(0, new Double(variance)); } return mean; }