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Added storeless covariance implementation contributed by Patrick Meyer. JIRA: MATH-449.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1160026 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
78c368544d
commit
22e68e3efe
3
pom.xml
3
pom.xml
@ -189,6 +189,9 @@
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<contributor>
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<name>Benjamin McCann</name>
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</contributor>
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<contributor>
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<name>Patrick Meyer</name>
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</contributor>
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<contributor>
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<name>J. Lewis Muir</name>
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</contributor>
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@ -0,0 +1,76 @@
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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.exception.MathIllegalArgumentException;
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import org.apache.commons.math.exception.util.LocalizedFormats;
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/**
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* Bivariate Covariance implementation that does not require input data to be
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* stored in memory.
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*
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* @version $Id$
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* @since 3.0
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*/
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public class StorelessBivariateCovariance {
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private double deltaX = 0.0;
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private double deltaY = 0.0;
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private double meanX = 0.0;
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private double meanY = 0.0;
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private double n = 0;
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private double covarianceNumerator = 0.0;
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private boolean biasCorrected = true;
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public StorelessBivariateCovariance(){
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}
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public StorelessBivariateCovariance(boolean biasCorrected){
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this.biasCorrected = biasCorrected;
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}
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public void increment(double x, double y){
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n++;
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deltaX = x - meanX;
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deltaY = y - meanY;
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meanX += deltaX / n;
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meanY += deltaY / n;
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covarianceNumerator += ((n-1.0) / n) * deltaX * deltaY;
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}
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public double getN(){
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return n;
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}
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public double getResult()throws IllegalArgumentException{
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if (n < 2) throw new MathIllegalArgumentException(
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LocalizedFormats.INSUFFICIENT_DIMENSION, n, 2);
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if(biasCorrected){
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return covarianceNumerator / (n - 1d);
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}else{
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return covarianceNumerator / n;
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}
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}
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}
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@ -0,0 +1,118 @@
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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.exception.MathIllegalArgumentException;
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import org.apache.commons.math.exception.MathUnsupportedOperationException;
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import org.apache.commons.math.exception.util.LocalizedFormats;
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import org.apache.commons.math.linear.Array2DRowRealMatrix;
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import org.apache.commons.math.linear.RealMatrix;
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/**
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* Covariance implementation that does not require input data to be
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* stored in memory.
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*
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* @version $Id$
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* @since 3.0
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*/
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public class StorelessCovariance extends Covariance {
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private StorelessBivariateCovariance[][] covMatrix = null;
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private int rowDimension = 1;
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private int colDimension = 1;
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private boolean biasCorrected = true;
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public StorelessCovariance(int rowDimension, int colDimension){
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this(rowDimension, colDimension, true);
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}
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public StorelessCovariance(int rowDimension, int colDimension, boolean biasCorrected){
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this.rowDimension = rowDimension;
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this.colDimension = colDimension;
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this.biasCorrected = biasCorrected;
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covMatrix = new StorelessBivariateCovariance[rowDimension][colDimension];
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initializeMatrix();
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}
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private void initializeMatrix(){
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for(int i=0;i<rowDimension;i++){
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for(int j=0;j<colDimension;j++){
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covMatrix[i][j] = new StorelessBivariateCovariance(biasCorrected);
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}
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}
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}
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public StorelessBivariateCovariance getCovariance(int xIndex, int yIndex){
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return covMatrix[xIndex][yIndex];
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}
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public void setCovariance(int xIndex, int yIndex, StorelessBivariateCovariance cov){
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covMatrix[xIndex][yIndex] = cov;
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}
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public void incrementCovariance(int xIndex, int yIndex, double x, double y){
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covMatrix[xIndex][yIndex].increment(x, y);
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}
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public void incrementRow(double[] rowData)throws IllegalArgumentException{
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int length = rowData.length;
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if (length != colDimension) {
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throw new MathIllegalArgumentException(
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LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, colDimension);
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}
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for(int i=0;i<length;i++){
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for(int j=0;j<length;j++){
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covMatrix[i][j].increment(rowData[i], rowData[j]);
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}
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}
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}
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@Override
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public RealMatrix getCovarianceMatrix() throws IllegalArgumentException {
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RealMatrix matrix = new Array2DRowRealMatrix(rowDimension, colDimension);
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for(int i=0;i<rowDimension;i++){
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for(int j=0;j<colDimension;j++){
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matrix.setEntry(i, j, covMatrix[i][j].getResult());
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}
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}
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return matrix;
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}
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public double[][] getData() throws IllegalArgumentException {
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double[][] data = new double[rowDimension][rowDimension];
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for(int i=0;i<rowDimension;i++){
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for(int j=0;j<colDimension;j++){
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data[i][j] = covMatrix[i][j].getResult();
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}
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}
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return data;
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}
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/**
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* This {@link Covariance} method is not supported by StorelessCovariance, since
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* the number of bivariate observations does not have to be the same for different
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* pairs of covariates - i.e., N as defined in {@link Covariance#getN()} is undefined.
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*/
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@Override
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public int getN() {
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throw new MathUnsupportedOperationException();
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}
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}
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@ -0,0 +1,305 @@
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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.TestUtils;
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import org.apache.commons.math.linear.Array2DRowRealMatrix;
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import org.apache.commons.math.linear.RealMatrix;
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import org.junit.Test;
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public class StorelessCovarianceTest {
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protected final double[] longleyData = new double[] {
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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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};
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protected final double[] swissData = new double[] {
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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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};
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protected final double[][] longleyDataSimple = {
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{60323, 83.0},
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{61122,88.5},
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{60171, 88.2},
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{61187, 89.5},
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{63221, 96.2},
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{63639, 98.1},
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{64989, 99.0},
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{63761, 100.0},
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{66019, 101.2},
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{67857, 104.6},
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{68169, 108.4},
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{66513, 110.8},
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{68655, 112.6},
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{69564, 114.2},
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{69331, 115.7},
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{70551, 116.9}
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};
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@Test
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public void testLonglySimpleVar(){
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double rCov = 12333921.73333333246;
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StorelessBivariateCovariance cov = new StorelessBivariateCovariance();
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for(int i=0;i<longleyDataSimple.length;i++){
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cov.increment(longleyDataSimple[i][0],longleyDataSimple[i][0]);
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}
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TestUtils.assertEquals("simple covariance test", rCov, cov.getResult(), 10E-7);
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}
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@Test
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public void testLonglySimpleCov(){
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double rCov = 36796.660000;
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StorelessBivariateCovariance cov = new StorelessBivariateCovariance();
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for(int i=0;i<longleyDataSimple.length;i++){
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cov.increment(longleyDataSimple[i][0], longleyDataSimple[i][1]);
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}
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TestUtils.assertEquals("simple covariance test", rCov, cov.getResult(), 10E-7);
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}
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/**
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* Test Longley dataset against R.
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* Data Source: J. Longley (1967) "An Appraisal of Least Squares
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* Programs for the Electronic Computer from the Point of View of the User"
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* Journal of the American Statistical Association, vol. 62. September,
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* pp. 819-841.
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*
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* Data are from NIST:
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* http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
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*/
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@Test
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public void testLonglyByRow() {
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RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
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double[] rData = new double[] {
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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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};
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StorelessCovariance covMatrix = new StorelessCovariance(7, 7);
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for(int i=0;i<matrix.getRowDimension();i++){
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covMatrix.incrementRow(matrix.getRow(i));
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}
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RealMatrix covarianceMatrix = covMatrix.getCovarianceMatrix();
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TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 7, 7), covarianceMatrix, 10E-7);
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}
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/**
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* Test R Swiss fertility dataset against R.
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* Data Source: R datasets package
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*/
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@Test
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public void testSwissFertilityByRow() {
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RealMatrix matrix = createRealMatrix(swissData, 47, 5);
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double[] rData = new double[] {
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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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};
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StorelessCovariance covMatrix = new StorelessCovariance(5, 5);
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for(int i=0;i<matrix.getRowDimension();i++){
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covMatrix.incrementRow(matrix.getRow(i));
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}
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RealMatrix covarianceMatrix = covMatrix.getCovarianceMatrix();
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TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 5, 5), covarianceMatrix, 10E-13);
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}
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/**
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* Test Longley dataset against R.
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* 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
|
||||
*/
|
||||
@Test
|
||||
public void testLonglyByEntry() {
|
||||
RealMatrix matrix = createRealMatrix(longleyData, 16, 7);
|
||||
|
||||
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
|
||||
};
|
||||
|
||||
int row = matrix.getRowDimension();
|
||||
int col = matrix.getColumnDimension();
|
||||
double x = 0.0;
|
||||
double y = 0.0;
|
||||
StorelessCovariance covMatrix = new StorelessCovariance(7, 7);
|
||||
for(int i=0;i<row;i++){
|
||||
for(int j=0;j<col;j++){
|
||||
x = matrix.getEntry(i, j);
|
||||
for(int k=0;k<col;k++){
|
||||
y = matrix.getEntry(i, k);
|
||||
covMatrix.incrementCovariance(j, k, x, y);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
RealMatrix covarianceMatrix = covMatrix.getCovarianceMatrix();
|
||||
|
||||
TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 7, 7), covarianceMatrix, 10E-7);
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Test R Swiss fertility dataset against R.
|
||||
* Data Source: R datasets package
|
||||
*/
|
||||
@Test
|
||||
public void testSwissFertilityByEntry() {
|
||||
RealMatrix matrix = createRealMatrix(swissData, 47, 5);
|
||||
|
||||
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
|
||||
};
|
||||
|
||||
int row = matrix.getRowDimension();
|
||||
int col = matrix.getColumnDimension();
|
||||
double x = 0.0;
|
||||
double y = 0.0;
|
||||
StorelessCovariance covMatrix = new StorelessCovariance(5, 5);
|
||||
for(int i=0;i<row;i++){
|
||||
for(int j=0;j<col;j++){
|
||||
x = matrix.getEntry(i, j);
|
||||
for(int k=0;k<col;k++){
|
||||
y = matrix.getEntry(i, k);
|
||||
covMatrix.incrementCovariance(j, k, x, y);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
RealMatrix covarianceMatrix = covMatrix.getCovarianceMatrix();
|
||||
|
||||
TestUtils.assertEquals("covariance matrix", createRealMatrix(rData, 5, 5), covarianceMatrix, 10E-13);
|
||||
}
|
||||
|
||||
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 Array2DRowRealMatrix(matrixData);
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user