/*
 * Copyright (C) 2008-2015 by Holger Arndt
 *
 * This file is part of the Universal Java Matrix Package (UJMP).
 * See the NOTICE file distributed with this work for additional
 * information regarding copyright ownership and licensing.
 *
 * UJMP is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.
 *
 * UJMP is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with UJMP; if not, write to the
 * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
 * Boston, MA  02110-1301  USA
 */

package org.ujmp.core.doublematrix.calculation.general.statistical;

import org.ujmp.core.Matrix;
import org.ujmp.core.doublematrix.DoubleMatrix2D;
import org.ujmp.core.doublematrix.calculation.AbstractDoubleCalculation;
import org.ujmp.core.util.MathUtil;
import org.ujmp.core.util.concurrent.PFor;

public class Cov extends AbstractDoubleCalculation {
	private static final long serialVersionUID = -2100820298353936855L;

	private Matrix mean = null;

	private boolean ignoreNaN = false;

	private boolean besselsCorrection = true;

	private final long[] size;

	public Cov(boolean ignoreNaN, Matrix matrix, boolean besselsCorrection) {
		super(matrix);
		this.size = new long[] { getSource().getColumnCount(), getSource().getColumnCount() };
		this.ignoreNaN = ignoreNaN;
		this.besselsCorrection = besselsCorrection;
	}

	public double getDouble(long... coordinates) {
		double sumProd = 0.0;
		final long rows = getSource().getRowCount();
		long N = 0;
		double deltaX = 0.0;
		double deltaY = 0.0;

		if (mean == null) {
			synchronized (this) {
				if (mean == null) {
					mean = new Mean(ROW, ignoreNaN, getSource()).calc(Ret.NEW);
				}
			}
		}

		if (ignoreNaN) {

			for (int i = 0; i < rows; i++) {
				deltaX = getSource().getAsDouble(i, coordinates[ROW])
						- mean.getAsDouble(0, coordinates[ROW]);
				deltaY = getSource().getAsDouble(i, coordinates[COLUMN])
						- mean.getAsDouble(0, coordinates[COLUMN]);

				if (!MathUtil.isNaNOrInfinite(deltaX) && !MathUtil.isNaNOrInfinite(deltaY)) {
					N++;
					sumProd += deltaX * deltaY;
				}
			}

		} else {

			N = rows;
			for (int i = 0; i < rows; i++) {
				deltaX = getSource().getAsDouble(i, coordinates[ROW])
						- mean.getAsDouble(0, coordinates[ROW]);
				deltaY = getSource().getAsDouble(i, coordinates[COLUMN])
						- mean.getAsDouble(0, coordinates[COLUMN]);
				sumProd += deltaX * deltaY;
			}

		}

		double cov = Double.NaN;
		if (N > 0) {
			if (besselsCorrection) {
				cov = sumProd / (N - 1);
			} else {
				cov = sumProd / N;
			}
		}
		return cov;
	}

	public long[] getSize() {
		return size;
	}

	public Matrix calcNew() {
		final int count = MathUtil.longToInt(getSize()[ROW]);

		final Matrix result = DoubleMatrix2D.Factory.zeros(count, count);

		new PFor(0, count - 1) {

			@Override
			public void step(int i) {
				for (int c = 0; c < count && c <= i; c++) {
					double value = getDouble(i, c);
					result.setAsDouble(value, i, c);
					result.setAsDouble(value, c, i);
				}
			}
		};

		if (getMetaData() != null) {
			result.setMetaData(getMetaData().clone());
		}
		return result;
	}

}