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Java example source code file (StorelessBivariateCovariance.java)

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

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numberistoosmallexception, storelessbivariatecovariance

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/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math3.stat.correlation;

import org.apache.commons.math3.exception.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/access-control.cgi/2008/086212.pdf">
 * Formulas for Robust, One-Pass Parallel Computation of Covariances and
 * Arbitrary-Order Statistical Moments</a>, 2008, Technical Report SAND2008-6212,
 * 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. n-1 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;
        }
    }
}

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