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

Introduction

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

Prototype

public static void multiplyInPlace(float[] a, double b) 

Source Link

Document

Scales the values in this array by b.

Usage

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

void mapDirByInverseHessian() {
    int count = sList.size();

    if (count != 0) {
        for (int i = count - 1; i >= 0; i--) {
            //mheilman: The program will try to divide by zero here unless there is a check 
            //that the parameters change at each iteration.  See comments in the minimize() method.
            //A roList value is the inner product of the change in the gradient 
            //and the change in parameters between the current and last iterations.  
            //See the discussion of L-BFGS in Nocedal and Wright's Numerical Optimization book 
            //(though I think that defines rho as the multiplicative inverse of what is here).
            alphas[i] = -ArrayMath.innerProduct(sList.get(i), dir) / roList.get(i);
            ArrayMath.addMultInPlace(dir, yList.get(i), alphas[i]);
        }/*from  www .j  a v  a2s.co m*/

        double[] lastY = yList.get(count - 1);
        double yDotY = ArrayMath.innerProduct(lastY, lastY);
        double scalar = roList.get(count - 1) / yDotY;
        ArrayMath.multiplyInPlace(dir, scalar);

        for (int i = 0; i < count; i++) {
            double beta = ArrayMath.innerProduct(yList.get(i), dir) / roList.get(i);
            ArrayMath.addMultInPlace(dir, sList.get(i), -alphas[i] - beta);
        }
    }
}

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

synchronized void mapDirByInverseHessian() {
    int count = sList.size();

    if (count != 0) {
        for (int i = count - 1; i >= 0; i--) {
            //mheilman: The program will try to divide by zero here unless there is a check
            //that the parameters change at each iteration.  See comments in the minimize() method.
            //A roList value is the inner product of the change in the gradient
            //and the change in parameters between the current and last iterations.
            //See the discussion of L-BFGS in Nocedal and Wright's Numerical Optimization book
            //(though I think that defines rho as the multiplicative inverse of what is here).
            alphas[i] = -ArrayMath.innerProduct(sList.get(i), dir) / roList.get(i);
            ArrayMath.addMultInPlace(dir, yList.get(i), alphas[i]);
        }/*from   w  w w  .ja  v  a2  s .com*/

        double[] lastY = yList.get(count - 1);
        double yDotY = ArrayMath.innerProduct(lastY, lastY);
        double scalar = roList.get(count - 1) / yDotY;
        ArrayMath.multiplyInPlace(dir, scalar);

        for (int i = 0; i < count; i++) {
            double beta = ArrayMath.innerProduct(yList.get(i), dir) / roList.get(i);
            ArrayMath.addMultInPlace(dir, sList.get(i), -alphas[i] - beta);
        }
    }
}

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