001/*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements.  See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License.  You may obtain a copy of the License at
008 *
009 *      http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017package org.apache.commons.math4.legacy.stat.descriptive.moment;
018
019import java.util.Arrays;
020
021import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
022import org.apache.commons.math4.legacy.linear.MatrixUtils;
023import org.apache.commons.math4.legacy.linear.RealMatrix;
024
025/**
026 * Returns the covariance matrix of the available vectors.
027 * @since 1.2
028 */
029public class VectorialCovariance {
030    /** Sums for each component. */
031    private final double[] sums;
032
033    /** Sums of products for each component. */
034    private final double[] productsSums;
035
036    /** Indicator for bias correction. */
037    private final boolean isBiasCorrected;
038
039    /** Number of vectors in the sample. */
040    private long n;
041
042    /** Constructs a VectorialCovariance.
043     * @param dimension vectors dimension
044     * @param isBiasCorrected if true, computed the unbiased sample covariance,
045     * otherwise computes the biased population covariance
046     */
047    public VectorialCovariance(int dimension, boolean isBiasCorrected) {
048        sums         = new double[dimension];
049        productsSums = new double[dimension * (dimension + 1) / 2];
050        n            = 0;
051        this.isBiasCorrected = isBiasCorrected;
052    }
053
054    /**
055     * Add a new vector to the sample.
056     * @param v vector to add
057     * @throws DimensionMismatchException if the vector does not have the right dimension
058     */
059    public void increment(double[] v) throws DimensionMismatchException {
060        if (v.length != sums.length) {
061            throw new DimensionMismatchException(v.length, sums.length);
062        }
063        int k = 0;
064        for (int i = 0; i < v.length; ++i) {
065            sums[i] += v[i];
066            for (int j = 0; j <= i; ++j) {
067                productsSums[k++] += v[i] * v[j];
068            }
069        }
070        n++;
071    }
072
073    /**
074     * Get the covariance matrix.
075     * @return covariance matrix
076     */
077    public RealMatrix getResult() {
078
079        int dimension = sums.length;
080        RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);
081
082        if (n > 1) {
083            double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
084            int k = 0;
085            for (int i = 0; i < dimension; ++i) {
086                for (int j = 0; j <= i; ++j) {
087                    double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
088                    result.setEntry(i, j, e);
089                    result.setEntry(j, i, e);
090                }
091            }
092        }
093
094        return result;
095    }
096
097    /**
098     * Get the number of vectors in the sample.
099     * @return number of vectors in the sample
100     */
101    public long getN() {
102        return n;
103    }
104
105    /**
106     * Clears the internal state of the Statistic.
107     */
108    public void clear() {
109        n = 0;
110        Arrays.fill(sums, 0.0);
111        Arrays.fill(productsSums, 0.0);
112    }
113
114    /** {@inheritDoc} */
115    @Override
116    public int hashCode() {
117        final int prime = 31;
118        int result = 1;
119        result = prime * result + (isBiasCorrected ? 1231 : 1237);
120        result = prime * result + (int) (n ^ (n >>> 32));
121        result = prime * result + Arrays.hashCode(productsSums);
122        result = prime * result + Arrays.hashCode(sums);
123        return result;
124    }
125
126    /** {@inheritDoc} */
127    @Override
128    public boolean equals(Object obj) {
129        if (this == obj) {
130            return true;
131        }
132        if (!(obj instanceof VectorialCovariance)) {
133            return false;
134        }
135        VectorialCovariance other = (VectorialCovariance) obj;
136        if (isBiasCorrected != other.isBiasCorrected) {
137            return false;
138        }
139        if (n != other.n) {
140            return false;
141        }
142        if (!Arrays.equals(productsSums, other.productsSums)) {
143            return false;
144        }
145        return Arrays.equals(sums, other.sums);
146    }
147}