Java Variance.evaluate方法代码示例

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
public static double[] estBetaDist(double[] betaValues) {
	Mean mean = new Mean();
	double mu = mean.evaluate(betaValues,0,betaValues.length);
	Variance variance = new Variance();
	double var = variance.evaluate(betaValues, mu);
	double alpha = -mu*(var+mu*mu-mu)/var;
	double beta = (mu-1)*(var+mu*mu-mu)/var;
	return new double[] {alpha, beta, mu, FastMath.sqrt(var)};
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
@Override
public Number getExpectedValue(int start, int length)
{
    if (length == 0) {
        return null;
    }

    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);
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
@Override
public Number getExpectedValue(int start, int length)
{
    if (length < 2) {
        return null;
    }

    double[] values = new double[length];
    for (int i = 0; i < length; i++) {
        values[i] = start + i;
    }

    Variance variance = new Variance();
    return variance.evaluate(values);
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/** Compute variance of the samples.
 * 
 * @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;
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
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];
		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);
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/**
 * Verify that diagonal entries are consistent with Variance computation and matrix matches
 * column-by-column covariances
 */
@Test
public void testConsistency() {
    final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
    final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();

    // Variances on the diagonal
    Variance variance = new Variance();
    for (int i = 0; i < 5; i++) {
        Assert.assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
    }

    // Symmetry, column-consistency
    Assert.assertEquals(covarianceMatrix.getEntry(2, 3),
            new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
    Assert.assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);

    // All columns same -> all entries = column variance
    RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
    for (int i = 0; i < 3; i++) {
        repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
    }
    RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
    double columnVariance = variance.evaluate(matrix.getColumn(0));
    for (int i = 0; i < 3; i++) {
        for (int j = 0; j < 3; j++) {
            Assert.assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
        }
    }

    // Check bias-correction defaults
    double[][] data = matrix.getData();
    TestUtils.assertEquals("Covariances",
            covarianceMatrix, new Covariance().computeCovarianceMatrix(data),Double.MIN_VALUE);
    TestUtils.assertEquals("Covariances",
            covarianceMatrix, new Covariance().computeCovarianceMatrix(data, true),Double.MIN_VALUE);

    double[] x = data[0];
    double[] y = data[1];
    Assert.assertEquals(new Covariance().covariance(x, y),
            new Covariance().covariance(x, y, true), Double.MIN_VALUE);
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/** Compute variance of given data with bias correction.
 * 
 * @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);
}
 

import org.apache.commons.math3.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/** Compute variance of given data without bias correction.
 * 
 * @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);
}