Java Code Examples for org.apache.commons.math.stat.descriptive.moment.Mean#evaluate()

The following examples show how to use org.apache.commons.math.stat.descriptive.moment.Mean#evaluate() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 2
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if (length != yArray.length) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
    } else if (length < 2) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.INSUFFICIENT_DIMENSION, length, 2);
    } else {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 3
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if (length != yArray.length) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
    } else if (length < 2) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.INSUFFICIENT_DIMENSION, length, 2);
    } else {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 4
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if (length != yArray.length) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
    } else if (length < 2) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.INSUFFICIENT_DIMENSION, length, 2);
    } else {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 5
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 6
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 7
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 8
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 9
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 10
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 11
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if (length != yArray.length) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length);
    } else if (length < 2) {
        throw MathRuntimeException.createIllegalArgumentException(
              LocalizedFormats.INSUFFICIENT_DIMENSION, length, 2);
    } else {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 12
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 * 
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected 
 * @return returns the covariance for the two arrays 
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) 
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 13
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 * 
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected 
 * @return returns the covariance for the two arrays 
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) 
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 14
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 * 
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected 
 * @return returns the covariance for the two arrays 
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) 
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 15
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}
Example 16
Source File: Covariance.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Computes the covariance between the two arrays.
 *
 * <p>Array lengths must match and the common length must be at least 2.</p>
 *
 * @param xArray first data array
 * @param yArray second data array
 * @param biasCorrected if true, returned value will be bias-corrected
 * @return returns the covariance for the two arrays
 * @throws  IllegalArgumentException if the arrays lengths do not match or
 * there is insufficient data
 */
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
    throws IllegalArgumentException {
    Mean mean = new Mean();
    double result = 0d;
    int length = xArray.length;
    if(length == yArray.length && length > 1) {
        double xMean = mean.evaluate(xArray);
        double yMean = mean.evaluate(yArray);
        for (int i = 0; i < length; i++) {
            double xDev = xArray[i] - xMean;
            double yDev = yArray[i] - yMean;
            result += (xDev * yDev - result) / (i + 1);
        }
    }
    else {
        throw MathRuntimeException.createIllegalArgumentException(
           "arrays must have the same length and both must have at " +
           "least two elements. xArray has size {0}, yArray has {1} elements",
                length, yArray.length);
    }
    return biasCorrected ? result * ((double) length / (double)(length - 1)) : result;
}