@Override public Double summarize(NumericColumn<?> column) { double[] values = removeMissing(column); return StatUtils.variance(values); } };
@Override public Double summarize(NumericColumn<?> column) { return Math.sqrt(StatUtils.variance(removeMissing(column))); } };
/** * Computes a <a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc22.htm#formula"> * t statistic </a> given observed values and a comparison constant. * <p> * This statistic can be used to perform a one sample t-test for the mean. * </p><p> * <strong>Preconditions</strong>: <ul> * <li>The observed array length must be at least 2. * </li></ul></p> * * @param mu comparison constant * @param observed array of values * @return t statistic * @throws NullArgumentException if <code>observed</code> is <code>null</code> * @throws NumberIsTooSmallException if the length of <code>observed</code> is < 2 */ public double t(final double mu, final double[] observed) throws NullArgumentException, NumberIsTooSmallException { checkSampleData(observed); // No try-catch or advertised exception because args have just been checked return t(StatUtils.mean(observed), mu, StatUtils.variance(observed), observed.length); }
return tTest(StatUtils.mean(sample), mu, StatUtils.variance(sample), sample.length);
private void computeStats(int len) { average = StatUtils.mean(window, 0, len); variance = StatUtils.variance(window, average, 0, len); stddev = FastMath.sqrt(variance); recomputeCounter = 0; }
public void kalmanFilter(Double opt) throws IOException, NullPointerException { ArrayList<double[]> noise = new ArrayList<>(); double[] innerNoiseArray = new double[NUMPLOTS]; ArrayList<Double> vals; double standardDeviation; if (opt == null) { //Replaced "OFF" with null. K = null; } for (int a = 0; a < 500; a++) { vals = getRaw(); for (int b = 0; b < NUMPLOTS; b++) { innerNoiseArray[b] = vals.get(b); noise.set(b, innerNoiseArray); } } for (int a = 0; a < NUMPLOTS; a++) { standardDeviation = FastMath.sqrt(StatUtils.variance(noise.get(a))); //Apachae Commons Maths used to calculate standard deviation K.set(a, new KalmanFilter(1. / opt, Math.pow(standardDeviation, 2))); } }
public void KalmanFilter(Double opt) throws IOException, NullPointerException { ArrayList<double[]> noise = new ArrayList<>(); double[] innerNoiseArray = new double[NUMPLOTS]; ArrayList<Double> vals; double standardDeviation; if (opt == 0) { //Replaced "OFF" with 0. kalman = null; } for (int a = 0; a < 500; a++) { vals = getRaw(); for (int b = 0; b < NUMPLOTS; b++) { innerNoiseArray[b] = vals.get(b); noise.set(b, innerNoiseArray); } } for (int a = 0; a < NUMPLOTS; a++) { standardDeviation = FastMath.sqrt(StatUtils.variance(noise.get(a))); kalman.set(a, new KalmanFilter(1. / opt, Math.pow(standardDeviation, 2))); } }
public static double variance(double[] population) { stddev = StatUtils.variance(population); return stddev; } // public double[] sampling (int dimSample) {
@Override public Double summarize(NumericColumn<?> column) { double[] values = removeMissing(column); return StatUtils.variance(values); } };
@Override public double reduce(double[] data) { return StatUtils.variance(data); }
@Test public void testSummarize() { IntColumn c = IntColumn.indexColumn("t", 99, 1); IntColumn c2 = c.copy(); c2.appendCell(""); double c2Variance = c2.variance(); double cVariance = StatUtils.variance(c.asDoubleArray()); assertEquals(cVariance, c2Variance, 0.00001); assertEquals(StatUtils.sumLog(c.asDoubleArray()), c2.sumOfLogs(), 0.00001); assertEquals(StatUtils.sumSq(c.asDoubleArray()), c2.sumOfSquares(), 0.00001); assertEquals(StatUtils.geometricMean(c.asDoubleArray()), c2.geometricMean(), 0.00001); assertEquals(StatUtils.product(c.asDoubleArray()), c2.product(), 0.00001); assertEquals(StatUtils.populationVariance(c.asDoubleArray()), c2.populationVariance(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getQuadraticMean(), c2.quadraticMean(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getStandardDeviation(), c2.standardDeviation(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getKurtosis(), c2.kurtosis(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getSkewness(), c2.skewness(), 0.00001); assertEquals(StatUtils.variance(c.asDoubleArray()), c.variance(), 0.00001); assertEquals(StatUtils.sumLog(c.asDoubleArray()), c.sumOfLogs(), 0.00001); assertEquals(StatUtils.sumSq(c.asDoubleArray()), c.sumOfSquares(), 0.00001); assertEquals(StatUtils.geometricMean(c.asDoubleArray()), c.geometricMean(), 0.00001); assertEquals(StatUtils.product(c.asDoubleArray()), c.product(), 0.00001); assertEquals(StatUtils.populationVariance(c.asDoubleArray()), c.populationVariance(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getQuadraticMean(), c.quadraticMean(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getStandardDeviation(), c.standardDeviation(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getKurtosis(), c.kurtosis(), 0.00001); assertEquals(new DescriptiveStatistics(c.asDoubleArray()).getSkewness(), c.skewness(), 0.00001); }
@Override public Double summarize(NumericColumn<?> column) { return Math.sqrt(StatUtils.variance(removeMissing(column))); } };
@Override public double reduce(double[] data) { return Math.sqrt(StatUtils.variance(data)); }
public static double stdDeviation(double[] population) { stddev = StatUtils.variance(population); return Math.sqrt(stddev); }
private void computeStats(int len) { average = StatUtils.mean(window, 0, len); variance = StatUtils.variance(window, average, 0, len); stddev = FastMath.sqrt(variance); recomputeCounter = 0; }
/** * 1-sample t-test confidence interval */ public double[] confidenceInterval(final double[] sample, final double alpha) { return confidenceInterval(StatUtils.mean(sample), StatUtils.variance(sample), sample.length, alpha); }