Calculate the covariance matrix - Java java.lang

Java examples for java.lang:Math Matrix

  1. HOME
  2. Java
  3. java.lang
  4. Math Matrix

Description

Calculate the covariance matrix

Demo Code

/**/* ww w  .j  a  v a 2 s  . c  o  m*/
 * MatrixHelper.java
 * 
 * Revision History:<br>
 * Jan 14, 2009 bpaulson - File created
 * 
 * <p>
 * 
 * <pre>
 * This work is released under the BSD License:
 * (C) 2008 Sketch Recognition Lab, Texas A&amp;M University (hereafter SRL @ TAMU)
 * All rights reserved.
 * 
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright
 *       notice, this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *       notice, this list of conditions and the following disclaimer in the
 *       documentation and/or other materials provided with the distribution.
 *     * Neither the name of the Sketch Recognition Lab, Texas A&amp;M University 
 *       nor the names of its contributors may be used to endorse or promote 
 *       products derived from this software without specific prior written 
 *       permission.
 * 
 * THIS SOFTWARE IS PROVIDED BY SRL @ TAMU ``AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL SRL @ TAMU BE LIABLE FOR ANY
 * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 * </pre>
 */
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import Jama.Matrix;

public class Main{
    /**
     * Calculate the covariance matrix
     * 
     * @param m
     *            matrix to calculate the covariance of
     * @return covariance matrix
     */
    public static Matrix cov(Matrix m) {
        Matrix cov = new Matrix(m.getColumnDimension(),
                m.getColumnDimension());
        for (int e = 0; e < m.getRowDimension(); e++) {
            for (int i = 0; i < m.getColumnDimension(); i++) {
                double fcei;
                fcei = m.get(e, i);
                if (Double.isNaN(fcei)) {
                    fcei = getColMean(m, i);
                } else if (Double.isInfinite(fcei)
                        || fcei == Double.MAX_VALUE) {
                    fcei = getColMax(m, i);
                } else if (fcei == Double.MIN_VALUE) {
                    fcei = getColMin(m, i);
                }
                for (int j = 0; j < m.getColumnDimension(); j++) {
                    double fcej;
                    fcej = m.get(e, j);
                    if (Double.isNaN(fcej)) {
                        fcej = getColMean(m, j);
                    } else if (Double.isInfinite(fcej)
                            || fcei == Double.MAX_VALUE) {
                        fcej = getColMax(m, j);
                    } else if (fcei == Double.MIN_VALUE) {
                        fcej = getColMin(m, j);
                    }
                    cov.set(i, j, cov.get(i, j) + (fcei - getColMean(m, i))
                            * (fcej - getColMean(m, j)));
                }
            }
        }
        return cov;
    }
    /**
     * Get the mean of a given column of a given matrix
     * 
     * @param m
     *            matrix
     * @param col
     *            column number
     * @return mean of the given column
     */
    public static double getColMean(Matrix m, int col) {
        double sum = 0;
        double num = 0;
        for (int i = 0; i < m.getRowDimension(); i++) {
            if (!Double.isNaN(m.get(i, col))
                    && !Double.isInfinite(m.get(i, col))) {
                sum += m.get(i, col);
                num++;
            }
        }
        return sum / num;
    }
    /**
     * Get the maximum value within a column of a given matrix
     * 
     * @param m
     *            matrix
     * @param col
     *            column number
     * @return maximum value in the column
     */
    public static double getColMax(Matrix m, int col) {
        double max = Double.MIN_VALUE;
        for (int i = 0; i < m.getRowDimension(); i++) {
            if (!Double.isNaN(m.get(i, col))
                    && !Double.isInfinite(m.get(i, col))
                    && m.get(i, col) > max)
                max = m.get(i, col);
        }
        return max;
    }
    /**
     * Get the minimum value within a column of a given matrix
     * 
     * @param m
     *            matrix
     * @param col
     *            column number
     * @return minimum value in the column
     */
    public static double getColMin(Matrix m, int col) {
        double min = Double.MAX_VALUE;
        for (int i = 0; i < m.getRowDimension(); i++) {
            if (!Double.isNaN(m.get(i, col))
                    && !Double.isInfinite(m.get(i, col))
                    && m.get(i, col) < min)
                min = m.get(i, col);
        }
        return min;
    }
}

Related Tutorials