Example usage for edu.stanford.nlp.math ArrayMath multiplyInto

Introduction

In this page you can find the example usage for edu.stanford.nlp.math ArrayMath multiplyInto.

Prototype

public static void multiplyInto(double[] a, double[] b, double c) 

Source Link

Usage

From source file:cmu.arktweetnlp.impl.OWLQN.java

void makeSteepestDescDir() {
    if (l1weight == 0) {
        ArrayMath.multiplyInto(dir, grad, -1);
    } else {/*from ww w .j  a v a  2 s  . c om*/

        for (int i = 0; i < dim; i++) {
            //mheilman: I added this if-statement to avoid penalizing bias parameters.
            if (OWLQN.biasParameters.contains(i)) {
                dir[i] = -grad[i];
                continue;
            }
            if (x[i] < 0) {
                dir[i] = -grad[i] + l1weight;
            } else if (x[i] > 0) {
                dir[i] = -grad[i] - l1weight;
            } else {
                if (grad[i] < -l1weight) {
                    dir[i] = -grad[i] - l1weight;
                } else if (grad[i] > l1weight) {
                    dir[i] = -grad[i] + l1weight;
                } else {
                    dir[i] = 0;
                }
            }
        }
    }
    steepestDescDir = dir.clone(); // deep copy needed
}

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From source file:dz.pfe.storm.ressources.cmu.arktweetnlp.impl.OWLQN.java

synchronized void makeSteepestDescDir() {
    OWLQN owlqn = new OWLQN();
    if (l1weight == 0) {
        ArrayMath.multiplyInto(dir, grad, -1);
    } else {//from   ww  w  .  j  a  v  a 2  s  .c o m

        for (int i = 0; i < dim; i++) {
            //mheilman: I added this if-statement to avoid penalizing bias parameters.
            if (owlqn.biasParameters.contains(i)) {
                dir[i] = -grad[i];
                continue;
            }
            if (x[i] < 0) {
                dir[i] = -grad[i] + l1weight;
            } else if (x[i] > 0) {
                dir[i] = -grad[i] - l1weight;
            } else {
                if (grad[i] < -l1weight) {
                    dir[i] = -grad[i] - l1weight;
                } else if (grad[i] > l1weight) {
                    dir[i] = -grad[i] + l1weight;
                } else {
                    dir[i] = 0;
                }
            }
        }
    }
    steepestDescDir = dir.clone(); // deep copy needed
}

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