Java Array Covariance covariance(double[] x, double[] y)

Description

<p>Returns the covariance between the two arrays of data.</p> <p>See - <a href="http://mathworld.wolfram.com/Covariance.html">Mathworld</a> </p>

License

Apache License

Parameter

Parameter	Description
x	a parameter
y	a parameter

Return

the covariance

Declaration

public static double covariance(double[] x, double[] y) 

Method Source Code

//package com.java2s;
/*******************************************************************************
 * Copyright 2013 Karlsruhe Institute of Technology. This Work has been partially supported by the EIT ICT Labs funded research project Towards a Mobile Cloud (activity CLD 12206).
 * //from w ww.j  a  va 2 s  . co m
 * Licensed 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.
 ******************************************************************************/

public class Main {
    /**
     * <p>Returns the covariance between the two arrays of data.</p>
     * <p>See - <a href="http://mathworld.wolfram.com/Covariance.html">Mathworld</a>
     * </p>
     * 
     * @param x
     * @param y
     * @return the covariance
     */
    public static double covariance(double[] x, double[] y) {
        double c = 0;
        double meanX = mean(x);
        double meanY = mean(y);
        for (int t = 0; t < x.length; t++) {
            c += (x[t] - meanX) * (y[t] - meanY);
        }
        return c / x.length;
    }

    public static double mean(int[] input) {
        return sum(input) / (double) input.length;
    }

    public static double mean(double[] input) {
        return sum(input) / input.length;
    }

    public static double mean(double[] input, int startIndex, int length) {
        return sum(input, startIndex, length) / length;
    }

    public static double mean(double[][] input) {
        return sum(input) / (input.length * input[0].length);
    }

    /**
     * Compute the mean along the given column 
     * 
     * @param input
     * @param column
     * @return
     */
    public static double mean(double[][] input, int column) {
        return sum(input, column) / input.length;
    }

    public static double sum(double[] input) {
        double total = 0;
        for (int i = 0; i < input.length; i++) {
            total += input[i];
        }
        return total;
    }

    public static double sum(double[] input, int startIndex, int length) {
        double total = 0;
        for (int i = startIndex; i < startIndex + length; i++) {
            total += input[i];
        }
        return total;
    }

    public static double sum(double[][] input) {
        double total = 0;
        for (int i = 0; i < input.length; i++) {
            for (int j = 0; j < input[i].length; j++) {
                total += input[i][j];
            }
        }
        return total;
    }

    public static double sum(double[][] input, int column) {
        double total = 0;
        for (int i = 0; i < input.length; i++) {
            total += input[i][column];
        }
        return total;
    }

    public static int sum(int[] input) {
        int total = 0;
        for (int i = 0; i < input.length; i++) {
            total += input[i];
        }
        return total;
    }

    public static int sum(int[][] input) {
        int total = 0;
        for (int i = 0; i < input.length; i++) {
            for (int j = 0; j < input[i].length; j++) {
                total += input[i][j];
            }
        }
        return total;
    }
}

Related

1. covariance(double[] a, double amean, double[] b, double bmean)
2. covariance(double[] a, double[] b)
3. covariance(double[] x, double[] y, int delay)
4. covariance(final double[] xArray, final double[] yArray)
5. covariance(final double[][] data)
6. covariance(int[] v1, int[] v2)