Added correlation package, Covariance class. JIRA: MATH-114
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@743688 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
01f82aed42
commit
42b68a69ce
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.stat.correlation;
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import org.apache.commons.math.MathRuntimeException;
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import org.apache.commons.math.linear.RealMatrix;
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import org.apache.commons.math.linear.DenseRealMatrix;
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import org.apache.commons.math.stat.descriptive.moment.Mean;
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import org.apache.commons.math.stat.descriptive.moment.Variance;
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/**
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* Computes covariances for pairs of arrays or columns of a matrix.
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*
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* <p>The constructors that take <code>RealMatrix</code> or
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* <code>double[][]</code> arguments generate correlation matrices. The
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* columns of the input matrices are assumed to represent variable values.</p>
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*
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* <p>The constructor argument <code>biasCorrected</code> determines whether or
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* not computed covariances are bias-corrected.</p>
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*
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* <p>Unbiased covariances are given by the formula</p>
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* <code>cov(X, Y) = Σ[(x<sub>i</sub> - E(X))(y<sub>i</sub> - E(Y))] / (n - 1)</code>
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* where <code>E(x)</code> is the mean of <code>X</code> and <code>E(Y)</code>
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* is the mean of the <code>Y</code> values.
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*
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* <p>Non-bias-corrected estimates use <code>n</code> in place of <code>n - 1</code>
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*
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* @version $Revision$ $Date$
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* @since 2.0
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*/
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public class Covariance {
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/** covariance matrix */
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private final RealMatrix covarianceMatrix;
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public Covariance() {
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super();
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covarianceMatrix = null;
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}
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/**
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* Create a Covariance matrix from a rectangular array
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* whose columns represent covariates.
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*
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* <p>The <code>biasCorrected</code> parameter determines whether or not
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* covariance estimates are bias-corrected.</p>
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*
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* <p>The input array must be rectangular with at least two columns
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* and two rows.</p>
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*
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* @param data rectangular array with columns representing covariates
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* @param biasCorrected true means covariances are bias-corrected
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* @throws IllegalArgumentException if the input data array is not
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* rectangular with at least two rows and two columns.
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*/
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public Covariance(double[][] data, boolean biasCorrected) {
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this(new DenseRealMatrix(data), biasCorrected);
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}
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/**
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* Create a Covariance matrix from a rectangular array
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* whose columns represent covariates.
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*
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* <p>The input array must be rectangular with at least two columns
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* and two rows</p>
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*
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* @param data rectangular array with columns representing covariates
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* @throws IllegalArgumentException if the input data array is not
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* rectangular with at least two rows and two columns.
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*/
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public Covariance(double[][] data) {
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this(data, true);
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}
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/**
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* Create a covariance matrix from a matrix whose columns
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* represent covariates.
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*
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* <p>The <code>biasCorrected</code> parameter determines whether or not
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* covariance estimates are bias-corrected.</p>
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*
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* <p>The matrix must have at least two columns and two rows</p>
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*
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* @param matrix matrix with columns representing covariates
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* @param biasCorrected true means covariances are bias-corrected
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* @throws IllegalArgumentException if the input matrix does not have
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* at least two rows and two columns
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*/
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public Covariance(RealMatrix matrix, boolean biasCorrected) {
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checkSufficientData(matrix);
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covarianceMatrix = computeCovariance(matrix, biasCorrected);
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}
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/**
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* Create a covariance matrix from a matrix whose columns
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* represent covariates.
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*
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* <p>The matrix must have at least two columns and two rows</p>
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*
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* @param matrix matrix with columns representing covariates
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* @throws IllegalArgumentException if the input matrix does not have
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* at least two rows and two columns
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*/
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public Covariance(RealMatrix matrix) {
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this(matrix, true);
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}
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/**
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* Returns the covariance matrix
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*
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* @return covariance matrix
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*/
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public RealMatrix getCovarianceMatrix() {
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return covarianceMatrix;
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}
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/**
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* Create a covariance matrix from a matrix whose columns represent
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* covariates.
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*
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* <p>The input matrix must have at least two columns and two rows</p>
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*
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* <p>The <code>biasCorrected</code> parameter determines whether or not
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* covariance estimates are bias-corrected.</p>
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*
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* @return covariance matrix
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*/
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protected RealMatrix computeCovariance(RealMatrix matrix, boolean biasCorrected) {
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int dimension = matrix.getColumnDimension();
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Variance variance = new Variance(biasCorrected);
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RealMatrix outMatrix = new DenseRealMatrix(dimension, dimension);
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for (int i = 0; i < dimension; i++) {
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for (int j = 0; j < i; j++) {
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double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
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outMatrix.setEntry(i, j, cov);
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outMatrix.setEntry(j, i, cov);
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}
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outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
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}
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return outMatrix;
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}
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/**
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* Computes the covariance between the two arrays.
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*
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* <p>Array lengths must match and the common length must be at least 2.</p>
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*
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* @param xArray first data array
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* @param yArray second data array
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* @param biasCorrected if true, returned value will be bias-corrected
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* @return returns the covariance for the two arrays
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* @throws IllegalArgumentException if the arrays lengths do not match or
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* there is insufficient data
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*/
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public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
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throws IllegalArgumentException {
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Mean mean = new Mean();
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double result = 0d;
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long length = xArray.length;
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if(length == yArray.length && length > 1) {
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double xMean = mean.evaluate(xArray);
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double yMean = mean.evaluate(yArray);
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for (int i = 0; i < xArray.length; i++) {
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double xDev = xArray[i] - xMean;
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double yDev = yArray[i] - yMean;
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result += (xDev * yDev - result) / (i + 1);
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}
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}
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else {
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throw MathRuntimeException.createIllegalArgumentException(
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"Arrays must have the same length and both must have at " +
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"least two elements. xArray has size {0}, yArray has {1} elements",
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new Object[] {xArray.length, yArray.length});
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}
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return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
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}
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/**
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* Throws IllegalArgumentException of the matrix does not have at least
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* two columns and two rows
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*/
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private void checkSufficientData(final RealMatrix matrix) {
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int nRows = matrix.getRowDimension();
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int nCols = matrix.getColumnDimension();
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if (nRows < 2 || nCols < 2) {
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throw MathRuntimeException.createIllegalArgumentException(
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"Insufficient data: only {0} rows and {1} columns.",
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new Object[]{nRows, nCols});
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}
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}
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}
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@ -39,9 +39,16 @@ The <action> type attribute can be add,update,fix,remove.
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</properties>
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<body>
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<release version="2.0" date="TBD" description="TBD">
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<action dev="psteitz" type="add" issue="MATH-114">
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Added Covariance class to compute variance-covariance matrices in new
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correlation package.
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</action>
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<action dev="luc" type="fix" issue="MATH-216" due-to="Cyril Briquet">
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Improved fast Fourier transform efficiency.
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</action>
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<action dev="billbarker" type="add">
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Added a SparseRealVector class that implements a sparse vector for the RealVector interface.
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</action>
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<action dev="luc" type="add" >
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Added factory methods to create Chebyshev, Hermite, Laguerre and Legendre polynomials.
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</action>
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@ -303,9 +310,6 @@ The <action> type attribute can be add,update,fix,remove.
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<action dev="brentworden" type="fix" issue="MATH-204" due-to="Mick">
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Added root checks for the endpoints.
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</action>
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<action dev="billbarker" type="add">
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Added a SparseRealVector class that implements a sparse vector for the RealVector interface.
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</action>
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</release>
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<release version="1.2" date="2008-02-24"
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description="This release combines bug fixes and new features. Most notable
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@ -0,0 +1,146 @@
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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#------------------------------------------------------------------------------
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# R source file to validate covariance tests in
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# org.apache.commons.math.stat.correlation.CovarianceTest
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#
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# To run the test, install R, put this file and testFunctions
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# into the same directory, launch R from this directory and then enter
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# source("<name-of-this-file>")
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#
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#------------------------------------------------------------------------------
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tol <- 1E-9 # error tolerance for tests
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#------------------------------------------------------------------------------
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# Function definitions
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source("testFunctions") # utility test functions
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options(digits=16) # override number of digits displayed
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# function to verify covariance computations
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verifyCovariance <- function(matrix, expectedCovariance, name) {
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covariance <- cov(matrix)
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output <- c("Covariance test dataset = ", name)
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if (assertEquals(expectedCovariance,covariance,tol,"Covariances")) {
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displayPadded(output, SUCCEEDED, WIDTH)
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} else {
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displayPadded(output, FAILED, WIDTH)
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}
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}
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#--------------------------------------------------------------------------
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cat("Covariance test cases\n")
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# Longley
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longley <- matrix(c(60323,83.0,234289,2356,1590,107608,1947,
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61122,88.5,259426,2325,1456,108632,1948,
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60171,88.2,258054,3682,1616,109773,1949,
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61187,89.5,284599,3351,1650,110929,1950,
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63221,96.2,328975,2099,3099,112075,1951,
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63639,98.1,346999,1932,3594,113270,1952,
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64989,99.0,365385,1870,3547,115094,1953,
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63761,100.0,363112,3578,3350,116219,1954,
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66019,101.2,397469,2904,3048,117388,1955,
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67857,104.6,419180,2822,2857,118734,1956,
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68169,108.4,442769,2936,2798,120445,1957,
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66513,110.8,444546,4681,2637,121950,1958,
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68655,112.6,482704,3813,2552,123366,1959,
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69564,114.2,502601,3931,2514,125368,1960,
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69331,115.7,518173,4806,2572,127852,1961,
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70551,116.9,554894,4007,2827,130081,1962),
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nrow = 16, ncol = 7, byrow = TRUE)
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expectedCovariance <- matrix(c(
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12333921.73333333246, 3.679666000000000e+04, 343330206.333333313,
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1649102.666666666744, 1117681.066666666651, 23461965.733333334, 16240.93333333333248,
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36796.66000000000, 1.164576250000000e+02, 1063604.115416667,
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6258.666250000000, 3490.253750000000, 73503.000000000, 50.92333333333334,
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343330206.33333331347, 1.063604115416667e+06, 9879353659.329166412,
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56124369.854166664183, 30880428.345833335072, 685240944.600000024, 470977.90000000002328,
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1649102.66666666674, 6.258666250000000e+03, 56124369.854166664,
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873223.429166666698, -115378.762499999997, 4462741.533333333, 2973.03333333333330,
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1117681.06666666665, 3.490253750000000e+03, 30880428.345833335,
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-115378.762499999997, 484304.095833333326, 1764098.133333333, 1382.43333333333339,
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23461965.73333333433, 7.350300000000000e+04, 685240944.600000024,
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4462741.533333333209, 1764098.133333333302, 48387348.933333330, 32917.40000000000146,
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16240.93333333333, 5.092333333333334e+01, 470977.900000000,
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2973.033333333333, 1382.433333333333, 32917.40000000, 22.66666666666667),
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nrow = 7, ncol = 7, byrow = TRUE)
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verifyCovariance(longley, expectedCovariance, "longley")
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# Swiss Fertility
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fertility <- matrix(c(80.2,17.0,15,12,9.96,
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83.1,45.1,6,9,84.84,
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92.5,39.7,5,5,93.40,
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85.8,36.5,12,7,33.77,
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76.9,43.5,17,15,5.16,
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76.1,35.3,9,7,90.57,
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83.8,70.2,16,7,92.85,
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92.4,67.8,14,8,97.16,
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82.4,53.3,12,7,97.67,
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82.9,45.2,16,13,91.38,
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87.1,64.5,14,6,98.61,
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64.1,62.0,21,12,8.52,
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66.9,67.5,14,7,2.27,
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68.9,60.7,19,12,4.43,
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61.7,69.3,22,5,2.82,
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68.3,72.6,18,2,24.20,
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71.7,34.0,17,8,3.30,
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55.7,19.4,26,28,12.11,
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54.3,15.2,31,20,2.15,
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65.1,73.0,19,9,2.84,
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65.5,59.8,22,10,5.23,
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65.0,55.1,14,3,4.52,
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56.6,50.9,22,12,15.14,
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57.4,54.1,20,6,4.20,
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72.5,71.2,12,1,2.40,
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74.2,58.1,14,8,5.23,
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72.0,63.5,6,3,2.56,
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60.5,60.8,16,10,7.72,
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58.3,26.8,25,19,18.46,
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65.4,49.5,15,8,6.10,
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75.5,85.9,3,2,99.71,
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69.3,84.9,7,6,99.68,
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77.3,89.7,5,2,100.00,
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70.5,78.2,12,6,98.96,
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79.4,64.9,7,3,98.22,
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65.0,75.9,9,9,99.06,
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92.2,84.6,3,3,99.46,
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79.3,63.1,13,13,96.83,
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70.4,38.4,26,12,5.62,
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65.7,7.7,29,11,13.79,
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72.7,16.7,22,13,11.22,
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64.4,17.6,35,32,16.92,
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77.6,37.6,15,7,4.97,
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67.6,18.7,25,7,8.65,
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35.0,1.2,37,53,42.34,
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44.7,46.6,16,29,50.43,
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42.8,27.7,22,29,58.33),
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nrow = 47, ncol = 5, byrow = TRUE)
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expectedCovariance <- matrix(c(
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156.0424976873265, 100.1691489361702, -64.36692876965772, -79.7295097132285, 241.5632030527289,
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100.169148936170251, 515.7994172062905, -124.39283071230344, -139.6574005550416, 379.9043755781684,
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-64.3669287696577, -124.3928307123034, 63.64662349676226, 53.5758556891767, -190.5606105457909,
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-79.7295097132285, -139.6574005550416, 53.57585568917669, 92.4560592044403, -61.6988297872340,
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241.5632030527289, 379.9043755781684, -190.56061054579092, -61.6988297872340, 1739.2945371877890),
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nrow = 5, ncol = 5, byrow = TRUE)
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verifyCovariance(fertility, expectedCovariance, "swiss fertility")
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displayDashes(WIDTH)
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@ -47,6 +47,9 @@ source("descriptiveTestCases")
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# multiple regression
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source("multipleOLSRegressionTestCases")
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# covariance
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source("covarianceTestCases")
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#------------------------------------------------------------------------------
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# if output has been diverted, change it back
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if (sink.number()) {
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|
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@ -0,0 +1,225 @@
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/*
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* 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.TestUtils;
|
||||
import org.apache.commons.math.linear.RealMatrix;
|
||||
import org.apache.commons.math.linear.RealMatrixImpl;
|
||||
import org.apache.commons.math.stat.descriptive.moment.Variance;
|
||||
|
||||
import junit.framework.TestCase;
|
||||
|
||||
public class CovarianceTest extends TestCase {
|
||||
|
||||
protected final double[] longleyData = new double[] {
|
||||
60323,83.0,234289,2356,1590,107608,1947,
|
||||
61122,88.5,259426,2325,1456,108632,1948,
|
||||
60171,88.2,258054,3682,1616,109773,1949,
|
||||
61187,89.5,284599,3351,1650,110929,1950,
|
||||
63221,96.2,328975,2099,3099,112075,1951,
|
||||
63639,98.1,346999,1932,3594,113270,1952,
|
||||
64989,99.0,365385,1870,3547,115094,1953,
|
||||
63761,100.0,363112,3578,3350,116219,1954,
|
||||
66019,101.2,397469,2904,3048,117388,1955,
|
||||
67857,104.6,419180,2822,2857,118734,1956,
|
||||
68169,108.4,442769,2936,2798,120445,1957,
|
||||
66513,110.8,444546,4681,2637,121950,1958,
|
||||
68655,112.6,482704,3813,2552,123366,1959,
|
||||
69564,114.2,502601,3931,2514,125368,1960,
|
||||
69331,115.7,518173,4806,2572,127852,1961,
|
||||
70551,116.9,554894,4007,2827,130081,1962
|
||||
};
|
||||
|
||||
protected final double[] swissData = new double[] {
|
||||
80.2,17.0,15,12,9.96,
|
||||
83.1,45.1,6,9,84.84,
|
||||
92.5,39.7,5,5,93.40,
|
||||
85.8,36.5,12,7,33.77,
|
||||
76.9,43.5,17,15,5.16,
|
||||
76.1,35.3,9,7,90.57,
|
||||
83.8,70.2,16,7,92.85,
|
||||
92.4,67.8,14,8,97.16,
|
||||
82.4,53.3,12,7,97.67,
|
||||
82.9,45.2,16,13,91.38,
|
||||
87.1,64.5,14,6,98.61,
|
||||
64.1,62.0,21,12,8.52,
|
||||
66.9,67.5,14,7,2.27,
|
||||
68.9,60.7,19,12,4.43,
|
||||
61.7,69.3,22,5,2.82,
|
||||
68.3,72.6,18,2,24.20,
|
||||
71.7,34.0,17,8,3.30,
|
||||
55.7,19.4,26,28,12.11,
|
||||
54.3,15.2,31,20,2.15,
|
||||
65.1,73.0,19,9,2.84,
|
||||
65.5,59.8,22,10,5.23,
|
||||
65.0,55.1,14,3,4.52,
|
||||
56.6,50.9,22,12,15.14,
|
||||
57.4,54.1,20,6,4.20,
|
||||
72.5,71.2,12,1,2.40,
|
||||
74.2,58.1,14,8,5.23,
|
||||
72.0,63.5,6,3,2.56,
|
||||
60.5,60.8,16,10,7.72,
|
||||
58.3,26.8,25,19,18.46,
|
||||
65.4,49.5,15,8,6.10,
|
||||
75.5,85.9,3,2,99.71,
|
||||
69.3,84.9,7,6,99.68,
|
||||
77.3,89.7,5,2,100.00,
|
||||
70.5,78.2,12,6,98.96,
|
||||
79.4,64.9,7,3,98.22,
|
||||
65.0,75.9,9,9,99.06,
|
||||
92.2,84.6,3,3,99.46,
|
||||
79.3,63.1,13,13,96.83,
|
||||
70.4,38.4,26,12,5.62,
|
||||
65.7,7.7,29,11,13.79,
|
||||
72.7,16.7,22,13,11.22,
|
||||
64.4,17.6,35,32,16.92,
|
||||
77.6,37.6,15,7,4.97,
|
||||
67.6,18.7,25,7,8.65,
|
||||
35.0,1.2,37,53,42.34,
|
||||
44.7,46.6,16,29,50.43,
|
||||
42.8,27.7,22,29,58.33
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* Test Longley dataset against R.
|
||||
* Data Source: J. Longley (1967) "An Appraisal of Least Squares
|
||||
* Programs for the Electronic Computer from the Point of View of the User"
|
||||
* Journal of the American Statistical Association, vol. 62. September,
|
||||
* pp. 819-841.
|
||||
*
|
||||
* Data are from NIST:
|
||||
* http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
|
||||
*/
|
||||
public void testLongly() {
|
||||
RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
|
||||
RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
|
||||
double[] rData = new double[] {
|
||||
12333921.73333333246, 3.679666000000000e+04, 343330206.333333313,
|
||||
1649102.666666666744, 1117681.066666666651, 23461965.733333334, 16240.93333333333248,
|
||||
36796.66000000000, 1.164576250000000e+02, 1063604.115416667,
|
||||
6258.666250000000, 3490.253750000000, 73503.000000000, 50.92333333333334,
|
||||
343330206.33333331347, 1.063604115416667e+06, 9879353659.329166412,
|
||||
56124369.854166664183, 30880428.345833335072, 685240944.600000024, 470977.90000000002328,
|
||||
1649102.66666666674, 6.258666250000000e+03, 56124369.854166664,
|
||||
873223.429166666698, -115378.762499999997, 4462741.533333333, 2973.03333333333330,
|
||||
1117681.06666666665, 3.490253750000000e+03, 30880428.345833335,
|
||||
-115378.762499999997, 484304.095833333326, 1764098.133333333, 1382.43333333333339,
|
||||
23461965.73333333433, 7.350300000000000e+04, 685240944.600000024,
|
||||
4462741.533333333209, 1764098.133333333302, 48387348.933333330, 32917.40000000000146,
|
||||
16240.93333333333, 5.092333333333334e+01, 470977.900000000,
|
||||
2973.033333333333, 1382.433333333333, 32917.40000000, 22.66666666666667
|
||||
};
|
||||
|
||||
TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 7, 7), covarianceMatrix, 10E-9);
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Test R Swiss fertility dataset against R.
|
||||
* Data Source: R datasets package
|
||||
*/
|
||||
public void testSwissFertility() {
|
||||
RealMatrix matrix = createRealMatrix(swissData, 47, 5);
|
||||
RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
|
||||
double[] rData = new double[] {
|
||||
156.0424976873265, 100.1691489361702, -64.36692876965772, -79.7295097132285, 241.5632030527289,
|
||||
100.169148936170251, 515.7994172062905, -124.39283071230344, -139.6574005550416, 379.9043755781684,
|
||||
-64.3669287696577, -124.3928307123034, 63.64662349676226, 53.5758556891767, -190.5606105457909,
|
||||
-79.7295097132285, -139.6574005550416, 53.57585568917669, 92.4560592044403, -61.6988297872340,
|
||||
241.5632030527289, 379.9043755781684, -190.56061054579092, -61.6988297872340, 1739.2945371877890
|
||||
};
|
||||
|
||||
TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 5, 5), covarianceMatrix, 10E-13);
|
||||
}
|
||||
|
||||
/**
|
||||
* Constant column
|
||||
*/
|
||||
public void testConstant() {
|
||||
double[] noVariance = new double[] {1, 1, 1, 1};
|
||||
double[] values = new double[] {1, 2, 3, 4};
|
||||
assertEquals(0d, new Covariance().covariance(noVariance, values, true), Double.MIN_VALUE);
|
||||
assertEquals(0d, new Covariance().covariance(noVariance, noVariance, true), Double.MIN_VALUE);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Insufficient data
|
||||
*/
|
||||
public void testInsufficientData() {
|
||||
double[] one = new double[] {1};
|
||||
double[] two = new double[] {2};
|
||||
try {
|
||||
new Covariance().covariance(one, two, false);
|
||||
fail("Expecting IllegalArgumentException");
|
||||
} catch (IllegalArgumentException ex) {
|
||||
// Expected
|
||||
}
|
||||
RealMatrix matrix = new RealMatrixImpl(new double[][] {{0},{1}});
|
||||
try {
|
||||
new Covariance(matrix);
|
||||
fail("Expecting IllegalArgumentException");
|
||||
} catch (IllegalArgumentException ex) {
|
||||
// Expected
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Verify that diagonal entries are consistent with Variance computation and matrix matches
|
||||
* column-by-column covariances
|
||||
*/
|
||||
public void testConsistency() {
|
||||
RealMatrix matrix = createRealMatrix(swissData, 47, 5);
|
||||
RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
|
||||
|
||||
// Variances on the diagonal
|
||||
Variance variance = new Variance();
|
||||
for (int i = 0; i < 5; i++) {
|
||||
assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
|
||||
}
|
||||
|
||||
// Symmetry, column-consistency
|
||||
assertEquals(covarianceMatrix.getEntry(2, 3),
|
||||
new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
|
||||
assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);
|
||||
|
||||
// All columns same -> all entries = column variance
|
||||
RealMatrix repeatedColumns = new RealMatrixImpl(47, 3);
|
||||
for (int i = 0; i < 3; i++) {
|
||||
repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
|
||||
}
|
||||
covarianceMatrix = 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++) {
|
||||
assertEquals(columnVariance, covarianceMatrix.getEntry(i, j), 10E-14);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
protected RealMatrix createRealMatrix(double[] data, int nRows, int nCols) {
|
||||
double[][] matrixData = new double[nRows][nCols];
|
||||
int ptr = 0;
|
||||
for (int i = 0; i < nRows; i++) {
|
||||
System.arraycopy(data, ptr, matrixData[i], 0, nCols);
|
||||
ptr += nCols;
|
||||
}
|
||||
return new RealMatrixImpl(matrixData);
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue