Example usage for weka.core Statistics normalInverse

Introduction

In this page you can find the example usage for weka.core Statistics normalInverse.

Prototype

public static double normalInverse(double y0) 

Source Link

Document

Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).

Usage

From source file:j48.Stats.java

License:Open Source License

/**
 * Computes estimated extra error for given total number of instances
 * and error using normal approximation to binomial distribution
 * (and continuity correction)./*from   w ww  .  j  a va2s  . co  m*/
 *
 * @param N number of instances
 * @param e observed error
 * @param CF confidence value
 */
public static double addErrs(double N, double e, float CF) {

    // Ignore stupid values for CF
    if (CF > 0.5) {
        System.err
                .println("WARNING: confidence value for pruning " + " too high. Error estimate not modified.");
        return 0;
    }

    // Check for extreme cases at the low end because the
    // normal approximation won't work
    if (e < 1) {

        // Base case (i.e. e == 0) from documenta Geigy Scientific
        // Tables, 6th edition, page 185
        double base = N * (1 - Math.pow(CF, 1 / N));
        if (e == 0) {
            return base;
        }

        // Use linear interpolation between 0 and 1 like C4.5 does
        return base + e * (addErrs(N, 1, CF) - base);
    }

    // Use linear interpolation at the high end (i.e. between N - 0.5
    // and N) because of the continuity correction
    if (e + 0.5 >= N) {

        // Make sure that we never return anything smaller than zero
        return Math.max(N - e, 0);
    }

    // Get z-score corresponding to CF
    double z = Statistics.normalInverse(1 - CF);

    // Compute upper limit of confidence interval
    double f = (e + 0.5) / N;
    double r = (f + (z * z) / (2 * N) + z * Math.sqrt((f / N) - (f * f / N) + (z * z / (4 * N * N))))
            / (1 + (z * z) / N);

    return (r * N) - e;
}

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