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/*
* 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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