/*
 * 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.math.stat.descriptive.moment;

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

import org.apache.commons.math.DimensionMismatchException;
import org.apache.commons.math.linear.MatrixUtils;
import org.apache.commons.math.linear.RealMatrix;

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

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

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

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

	/** Indicator for bias correction. */
	private 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
	 * @exception 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 == null)
			return false;
		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;
	}

}