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Java example source code file (Covariance.java)

This example Java source code file (Covariance.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

blockrealmatrix, covariance, mathillegalargumentexception, mean, notstrictlypositiveexception, realmatrix

The Covariance.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math3.stat.correlation;

import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.BlockRealMatrix;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Variance;

/**
 * Computes covariances for pairs of arrays or columns of a matrix.
 *
 * <p>The constructors that take RealMatrix or
 * <code>double[][] arguments generate covariance matrices.  The
 * columns of the input matrices are assumed to represent variable values.</p>
 *
 * <p>The constructor argument biasCorrected determines whether or
 * not computed covariances are bias-corrected.</p>
 *
 * <p>Unbiased covariances are given by the formula

* <code>cov(X, Y) = Σ[(xi - E(X))(yi - E(Y))] / (n - 1) * where <code>E(X) is the mean of X and E(Y) * is the mean of the <code>Y values. * * <p>Non-bias-corrected estimates use n in place of n - 1 * * @since 2.0 */ public class Covariance { /** covariance matrix */ private final RealMatrix covarianceMatrix; /** * Create an empty covariance matrix. */ /** Number of observations (length of covariate vectors) */ private final int n; /** * Create a Covariance with no data */ public Covariance() { super(); covarianceMatrix = null; n = 0; } /** * Create a Covariance matrix from a rectangular array * whose columns represent covariates. * * <p>The biasCorrected parameter determines whether or not * covariance estimates are bias-corrected.</p> * * <p>The input array must be rectangular with at least one column * and two rows.</p> * * @param data rectangular array with columns representing covariates * @param biasCorrected true means covariances are bias-corrected * @throws MathIllegalArgumentException if the input data array is not * rectangular with at least two rows and one column. * @throws NotStrictlyPositiveException if the input data array is not * rectangular with at least one row and one column. */ public Covariance(double[][] data, boolean biasCorrected) throws MathIllegalArgumentException, NotStrictlyPositiveException { this(new BlockRealMatrix(data), biasCorrected); } /** * Create a Covariance matrix from a rectangular array * whose columns represent covariates. * * <p>The input array must be rectangular with at least one column * and two rows</p> * * @param data rectangular array with columns representing covariates * @throws MathIllegalArgumentException if the input data array is not * rectangular with at least two rows and one column. * @throws NotStrictlyPositiveException if the input data array is not * rectangular with at least one row and one column. */ public Covariance(double[][] data) throws MathIllegalArgumentException, NotStrictlyPositiveException { this(data, true); } /** * Create a covariance matrix from a matrix whose columns * represent covariates. * * <p>The biasCorrected parameter determines whether or not * covariance estimates are bias-corrected.</p> * * <p>The matrix must have at least one column and two rows

* * @param matrix matrix with columns representing covariates * @param biasCorrected true means covariances are bias-corrected * @throws MathIllegalArgumentException if the input matrix does not have * at least two rows and one column */ public Covariance(RealMatrix matrix, boolean biasCorrected) throws MathIllegalArgumentException { checkSufficientData(matrix); n = matrix.getRowDimension(); covarianceMatrix = computeCovarianceMatrix(matrix, biasCorrected); } /** * Create a covariance matrix from a matrix whose columns * represent covariates. * * <p>The matrix must have at least one column and two rows

* * @param matrix matrix with columns representing covariates * @throws MathIllegalArgumentException if the input matrix does not have * at least two rows and one column */ public Covariance(RealMatrix matrix) throws MathIllegalArgumentException { this(matrix, true); } /** * Returns the covariance matrix * * @return covariance matrix */ public RealMatrix getCovarianceMatrix() { return covarianceMatrix; } /** * Returns the number of observations (length of covariate vectors) * * @return number of observations */ public int getN() { return n; } /** * Compute a covariance matrix from a matrix whose columns represent * covariates. * @param matrix input matrix (must have at least one column and two rows) * @param biasCorrected determines whether or not covariance estimates are bias-corrected * @return covariance matrix * @throws MathIllegalArgumentException if the matrix does not contain sufficient data */ protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) throws MathIllegalArgumentException { int dimension = matrix.getColumnDimension(); Variance variance = new Variance(biasCorrected); RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension); for (int i = 0; i < dimension; i++) { for (int j = 0; j < i; j++) { double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected); outMatrix.setEntry(i, j, cov); outMatrix.setEntry(j, i, cov); } outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i))); } return outMatrix; } /** * Create a covariance matrix from a matrix whose columns represent * covariates. Covariances are computed using the bias-corrected formula. * @param matrix input matrix (must have at least one column and two rows) * @return covariance matrix * @throws MathIllegalArgumentException if matrix does not contain sufficient data * @see #Covariance */ protected RealMatrix computeCovarianceMatrix(RealMatrix matrix) throws MathIllegalArgumentException { return computeCovarianceMatrix(matrix, true); } /** * Compute a covariance matrix from a rectangular array whose columns represent * covariates. * @param data input array (must have at least one column and two rows) * @param biasCorrected determines whether or not covariance estimates are bias-corrected * @return covariance matrix * @throws MathIllegalArgumentException if the data array does not contain sufficient * data * @throws NotStrictlyPositiveException if the input data array is not * rectangular with at least one row and one column. */ protected RealMatrix computeCovarianceMatrix(double[][] data, boolean biasCorrected) throws MathIllegalArgumentException, NotStrictlyPositiveException { return computeCovarianceMatrix(new BlockRealMatrix(data), biasCorrected); } /** * Create a covariance matrix from a rectangular array whose columns represent * covariates. Covariances are computed using the bias-corrected formula. * @param data input array (must have at least one column and two rows) * @return covariance matrix * @throws MathIllegalArgumentException if the data array does not contain sufficient data * @throws NotStrictlyPositiveException if the input data array is not * rectangular with at least one row and one column. * @see #Covariance */ protected RealMatrix computeCovarianceMatrix(double[][] data) throws MathIllegalArgumentException, NotStrictlyPositiveException { return computeCovarianceMatrix(data, true); } /** * Computes the covariance between the two arrays. * * <p>Array lengths must match and the common length must be at least 2.

* * @param xArray first data array * @param yArray second data array * @param biasCorrected if true, returned value will be bias-corrected * @return returns the covariance for the two arrays * @throws MathIllegalArgumentException if the arrays lengths do not match or * there is insufficient data */ public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) throws MathIllegalArgumentException { Mean mean = new Mean(); double result = 0d; int length = xArray.length; if (length != yArray.length) { throw new MathIllegalArgumentException( LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length); } else if (length < 2) { throw new MathIllegalArgumentException( LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2); } else { double xMean = mean.evaluate(xArray); double yMean = mean.evaluate(yArray); for (int i = 0; i < length; i++) { double xDev = xArray[i] - xMean; double yDev = yArray[i] - yMean; result += (xDev * yDev - result) / (i + 1); } } return biasCorrected ? result * ((double) length / (double)(length - 1)) : result; } /** * Computes the covariance between the two arrays, using the bias-corrected * formula. * * <p>Array lengths must match and the common length must be at least 2.

* * @param xArray first data array * @param yArray second data array * @return returns the covariance for the two arrays * @throws MathIllegalArgumentException if the arrays lengths do not match or * there is insufficient data */ public double covariance(final double[] xArray, final double[] yArray) throws MathIllegalArgumentException { return covariance(xArray, yArray, true); } /** * Throws MathIllegalArgumentException if the matrix does not have at least * one column and two rows. * @param matrix matrix to check * @throws MathIllegalArgumentException if the matrix does not contain sufficient data * to compute covariance */ private void checkSufficientData(final RealMatrix matrix) throws MathIllegalArgumentException { int nRows = matrix.getRowDimension(); int nCols = matrix.getColumnDimension(); if (nRows < 2 || nCols < 1) { throw new MathIllegalArgumentException( LocalizedFormats.INSUFFICIENT_ROWS_AND_COLUMNS, nRows, nCols); } } }

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