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Commons Math example source code file (Covariance.java)

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

Java - Commons Math tags/keywords

blockrealmatrix, blockrealmatrix, covariance, covariance, illegalargumentexception, mean, realmatrix, realmatrix, variance, variance

The Commons Math Covariance.java 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.math.stat.correlation;

import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.BlockRealMatrix;
import org.apache.commons.math.stat.descriptive.moment.Mean;
import org.apache.commons.math.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 * * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $ * @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 two columns * and two rows.</p> * * @param data rectangular array with columns representing covariates * @param biasCorrected true means covariances are bias-corrected * @throws IllegalArgumentException if the input data array is not * rectangular with at least two rows and two columns. */ public Covariance(double[][] data, boolean biasCorrected) { 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 two columns * and two rows</p> * * @param data rectangular array with columns representing covariates * @throws IllegalArgumentException if the input data array is not * rectangular with at least two rows and two columns. */ public Covariance(double[][] data) { 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 two columns and two rows

* * @param matrix matrix with columns representing covariates * @param biasCorrected true means covariances are bias-corrected * @throws IllegalArgumentException if the input matrix does not have * at least two rows and two columns */ public Covariance(RealMatrix matrix, boolean biasCorrected) { 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 two columns and two rows

* * @param matrix matrix with columns representing covariates * @throws IllegalArgumentException if the input matrix does not have * at least two rows and two columns */ public Covariance(RealMatrix matrix) { 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 two columns and two rows) * @param biasCorrected determines whether or not covariance estimates are bias-corrected * @return covariance matrix */ protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) { 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 two columns and two rows) * @return covariance matrix * @see #Covariance */ protected RealMatrix computeCovarianceMatrix(RealMatrix matrix) { return computeCovarianceMatrix(matrix, true); } /** * Compute a covariance matrix from a rectangular array whose columns represent * covariates. * @param data input array (must have at least two columns and two rows) * @param biasCorrected determines whether or not covariance estimates are bias-corrected * @return covariance matrix */ protected RealMatrix computeCovarianceMatrix(double[][] data, boolean biasCorrected) { return computeCovarianceMatrix(new BlockRealMatrix(data), biasCorrected); } /** * Create a covariance matrix from a rectangual array whose columns represent * covariates. Covariances are computed using the bias-corrected formula. * @param data input array (must have at least two columns and two rows) * @return covariance matrix * @see #Covariance */ protected RealMatrix computeCovarianceMatrix(double[][] data) { 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 IllegalArgumentException if the arrays lengths do not match or * there is insufficient data */ public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) throws IllegalArgumentException { Mean mean = new Mean(); double result = 0d; int length = xArray.length; if(length == yArray.length && length > 1) { 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); } } else { throw MathRuntimeException.createIllegalArgumentException( "arrays must have the same length and both must have at " + "least two elements. xArray has size {0}, yArray has {1} elements", length, yArray.length); } 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 IllegalArgumentException if the arrays lengths do not match or * there is insufficient data */ public double covariance(final double[] xArray, final double[] yArray) throws IllegalArgumentException { return covariance(xArray, yArray, true); } /** * Throws IllegalArgumentException of the matrix does not have at least * two columns and two rows * @param matrix matrix to check */ private void checkSufficientData(final RealMatrix matrix) { int nRows = matrix.getRowDimension(); int nCols = matrix.getColumnDimension(); if (nRows < 2 || nCols < 2) { throw MathRuntimeException.createIllegalArgumentException( "insufficient data: only {0} rows and {1} columns.", nRows, nCols); } } }

Other Commons Math examples (source code examples)

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