

Java example source code file (StorelessBivariateCovariance.java)
The StorelessBivariateCovariance.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/LICENSE2.0 * * 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.NumberIsTooSmallException; import org.apache.commons.math3.exception.util.LocalizedFormats; /** * Bivariate Covariance implementation that does not require input data to be * stored in memory. * * <p>This class is based on a paper written by Philippe Pébay: * <a href="http://prod.sandia.gov/techlib/accesscontrol.cgi/2008/086212.pdf"> * Formulas for Robust, OnePass Parallel Computation of Covariances and * ArbitraryOrder Statistical Moments</a>, 2008, Technical Report SAND20086212, * Sandia National Laboratories. It computes the covariance for a pair of variables. * Use {@link StorelessCovariance} to estimate an entire covariance matrix.</p> * * <p>Note: This class is package private as it is only used internally in * the {@link StorelessCovariance} class.</p> * * @since 3.0 */ class StorelessBivariateCovariance { /** the mean of variable x */ private double meanX; /** the mean of variable y */ private double meanY; /** number of observations */ private double n; /** the running covariance estimate */ private double covarianceNumerator; /** flag for bias correction */ private boolean biasCorrected; /** * Create an empty {@link StorelessBivariateCovariance} instance with * bias correction. */ StorelessBivariateCovariance() { this(true); } /** * Create an empty {@link StorelessBivariateCovariance} instance. * * @param biasCorrection if <code>true the covariance estimate is corrected * for bias, i.e. n1 in the denominator, otherwise there is no bias correction, * i.e. n in the denominator. */ StorelessBivariateCovariance(final boolean biasCorrection) { meanX = meanY = 0.0; n = 0; covarianceNumerator = 0.0; biasCorrected = biasCorrection; } /** * Update the covariance estimation with a pair of variables (x, y). * * @param x the x value * @param y the y value */ public void increment(final double x, final double y) { n++; final double deltaX = x  meanX; final double deltaY = y  meanY; meanX += deltaX / n; meanY += deltaY / n; covarianceNumerator += ((n  1.0) / n) * deltaX * deltaY; } /** * Appends another bivariate covariance calculation to this. * After this operation, statistics returned should be close to what would * have been obtained by by performing all of the {@link #increment(double, double)} * operations in {@code cov} directly on this. * * @param cov StorelessBivariateCovariance instance to append. */ public void append(StorelessBivariateCovariance cov) { double oldN = n; n += cov.n; final double deltaX = cov.meanX  meanX; final double deltaY = cov.meanY  meanY; meanX += deltaX * cov.n / n; meanY += deltaY * cov.n / n; covarianceNumerator += cov.covarianceNumerator + oldN * cov.n / n * deltaX * deltaY; } /** * Returns the number of observations. * * @return number of observations */ public double getN() { return n; } /** * Return the current covariance estimate. * * @return the current covariance * @throws NumberIsTooSmallException if the number of observations * is < 2 */ public double getResult() throws NumberIsTooSmallException { if (n < 2) { throw new NumberIsTooSmallException(LocalizedFormats.INSUFFICIENT_DIMENSION, n, 2, true); } if (biasCorrected) { return covarianceNumerator / (n  1d); } else { return covarianceNumerator / n; } } } Other Java examples (source code examples)Here is a short list of links related to this Java StorelessBivariateCovariance.java source code file: 
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