Java Utililty Methods Array Covariance

List of utility methods to do Array Covariance

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Description

The list of methods to do Array Covariance are organized into topic(s).

Method

doublecovariance(double[] a, double amean, double[] b, double bmean)
Compute co-variance of the supplied array pair 
double t = 0;
for (int i = 0; i < a.length; i++) {
    t += (a[i] - amean) * (b[i] - bmean);
return t / a.length;
doublecovariance(double[] a, double[] b)
Calculates the covariance between the two double arrays a and b. 
if (a.length != b.length) {
    System.err.println("Arrays are not of the same size!");
    return Double.NaN;
double sumA = 0.0;
double sumB = 0.0;
double m_A = 0.0;
double m_B = 0.0;
...
doublecovariance(double[] x, double[] y)

Returns the covariance between the two arrays of data.

See - Mathworld 

double c = 0;
double meanX = mean(x);
double meanY = mean(y);
for (int t = 0; t < x.length; t++) {
    c += (x[t] - meanX) * (y[t] - meanY);
return c / x.length;
doublecovariance(double[] x, double[] y, int delay)

Returns the covariance between the two arrays of data, with a given lag between the first and second.

See - Mathworld 

double meanX = 0, meanY = 0;
for (int n = 0; n < x.length - delay; n++) {
    meanX += x[n];
    meanY += y[n + delay];
meanX /= (double) (x.length - delay);
meanY /= (double) (x.length - delay);
double c = 0;
...
doublecovariance(final double[] xArray, final double[] yArray)
Computes the covariance of the two input arrays. 
double result = 0d;
final int length = xArray.length;
final double xMean = mean(xArray);
final double yMean = mean(yArray);
for (int i = 0; i < length; i++) {
    final double xDev = xArray[i] - xMean;
    final double yDev = yArray[i] - yMean;
    result += (xDev * yDev - result) / (i + 1);
...
double[][]covariance(final double[][] data)
covariance 
final int len = data.length;
final double[] means = new double[len]; 
final double[][] ret = new double[len][len];
for (int i = 0; i < len; i++) {
    means[i] = mean(data[i]);
    for (int j = 0; j <= i; j++) {
        final double d = sum(multiply(shift(data[i], -means[i]), shift(data[j], -means[j]))) / (len);
        ret[i][j] = d;
...
doublecovariance(int[] v1, int[] v2)
Computes the covariance. 
if (v1.length != v2.length)
    throw new IllegalArgumentException(
            "Arrays must have the same length : " + v1.length + ", " + v2.length);
final double m1 = mean(v1);
final double m2 = mean(v2);
double ans = 0.0;
for (int i = 0; i < v1.length; i++)
    ans += (v1[i] - m1) * (v2[i] - m2);
...
doublecovariance(Number[] x, Number[] y)
Computes the covariance between the two data vectors and returns it. 
double result;
int i;
double xbar;
double ybar;
if (x.length != y.length)
    throw new IllegalArgumentException("Arrays differ in length: " + x.length + " != " + y.length);
if (x.length == 0)
    throw new IllegalArgumentException("0-length arrays provided!");
...
doublecovarianceOfDoubleArrays(double[] x, double[] y)
computes the covariance between two arrays 
double meanX = computeMean(x);
double meanY = computeMean(y);
double covariance = 0;
for (int i = 0; i < x.length; i++) {
    covariance += ((x[i] - meanX) * (y[i] - meanY)) / x.length;
return covariance / x.length;
doublecovarianceTwoColumns(double[][] data, int col1, int col2)

Returns the covariance between two columns of data in a multivariate array.

See - Mathworld 

double mean1 = mean(data, col1);
double mean2 = mean(data, col2);
return covarianceTwoColumns(data, col1, col2, mean1, mean2);