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Java example source code file (VectorialCovariance.java)

This example Java source code file (VectorialCovariance.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.

Learn more about this Java project at its project page.

Java - Java tags/keywords

dimensionmismatchexception, override, realmatrix, serializable, util, vectorialcovariance

The VectorialCovariance.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.descriptive.moment;

import java.io.Serializable;
import java.util.Arrays;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;

/**
 * Returns the covariance matrix of the available vectors.
 * @since 1.2
 */
public class VectorialCovariance implements Serializable {

    /** Serializable version identifier */
    private static final long serialVersionUID = 4118372414238930270L;

    /** Sums for each component. */
    private final double[] sums;

    /** Sums of products for each component. */
    private final double[] productsSums;

    /** Indicator for bias correction. */
    private final boolean isBiasCorrected;

    /** Number of vectors in the sample. */
    private long n;

    /** Constructs a VectorialCovariance.
     * @param dimension vectors dimension
     * @param isBiasCorrected if true, computed the unbiased sample covariance,
     * otherwise computes the biased population covariance
     */
    public VectorialCovariance(int dimension, boolean isBiasCorrected) {
        sums         = new double[dimension];
        productsSums = new double[dimension * (dimension + 1) / 2];
        n            = 0;
        this.isBiasCorrected = isBiasCorrected;
    }

    /**
     * Add a new vector to the sample.
     * @param v vector to add
     * @throws DimensionMismatchException if the vector does not have the right dimension
     */
    public void increment(double[] v) throws DimensionMismatchException {
        if (v.length != sums.length) {
            throw new DimensionMismatchException(v.length, sums.length);
        }
        int k = 0;
        for (int i = 0; i < v.length; ++i) {
            sums[i] += v[i];
            for (int j = 0; j <= i; ++j) {
                productsSums[k++] += v[i] * v[j];
            }
        }
        n++;
    }

    /**
     * Get the covariance matrix.
     * @return covariance matrix
     */
    public RealMatrix getResult() {

        int dimension = sums.length;
        RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);

        if (n > 1) {
            double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
            int k = 0;
            for (int i = 0; i < dimension; ++i) {
                for (int j = 0; j <= i; ++j) {
                    double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
                    result.setEntry(i, j, e);
                    result.setEntry(j, i, e);
                }
            }
        }

        return result;

    }

    /**
     * Get the number of vectors in the sample.
     * @return number of vectors in the sample
     */
    public long getN() {
        return n;
    }

    /**
     * Clears the internal state of the Statistic
     */
    public void clear() {
        n = 0;
        Arrays.fill(sums, 0.0);
        Arrays.fill(productsSums, 0.0);
    }

    /** {@inheritDoc} */
    @Override
    public int hashCode() {
        final int prime = 31;
        int result = 1;
        result = prime * result + (isBiasCorrected ? 1231 : 1237);
        result = prime * result + (int) (n ^ (n >>> 32));
        result = prime * result + Arrays.hashCode(productsSums);
        result = prime * result + Arrays.hashCode(sums);
        return result;
    }

    /** {@inheritDoc} */
    @Override
    public boolean equals(Object obj) {
        if (this == obj) {
            return true;
        }
        if (!(obj instanceof VectorialCovariance)) {
            return false;
        }
        VectorialCovariance other = (VectorialCovariance) obj;
        if (isBiasCorrected != other.isBiasCorrected) {
            return false;
        }
        if (n != other.n) {
            return false;
        }
        if (!Arrays.equals(productsSums, other.productsSums)) {
            return false;
        }
        if (!Arrays.equals(sums, other.sums)) {
            return false;
        }
        return true;
    }

}

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