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

Introduction

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

Prototype

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

Source Link

Usage

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

private boolean getNextPoint(double alpha) {
    ArrayMath.addMultInto(newX, x, dir, alpha);
    /*if (OWLQN.isConstrained())
       newX = OWLQN.projectWeights(newX);*/ //TODO
    if (l1weight > 0) {
        for (int i = 0; i < dim; i++) {
            //mheilman: I added this if-statement to avoid penalizing bias parameters.
            if (OWLQN.biasParameters.contains(i)) {
                continue;
            }//from   w  ww. j a va2s  .c  o m
            if (x[i] * newX[i] < 0.0) {
                newX[i] = 0.0;
            }
        }
    }
    return true;
}

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

void shift() {
    double[] nextS = null, nextY = null;

    int listSize = sList.size();

    if (listSize < m) {
        try {//from  w ww. j a v a 2 s  . c  o  m
            nextS = new double[dim];
            nextY = new double[dim];
        } catch (OutOfMemoryError e) {
            m = listSize;
            nextS = null;
        }
    }

    if (nextS == null) {
        nextS = sList.poll();
        nextY = yList.poll();
        roList.poll();
    }

    ArrayMath.addMultInto(nextS, newX, x, -1);
    ArrayMath.addMultInto(nextY, newGrad, grad, -1);

    double ro = ArrayMath.innerProduct(nextS, nextY);
    assert (ro != 0.0);

    sList.offer(nextS);
    yList.offer(nextY);
    roList.offer(ro);

    double[] tmpX = newX;
    newX = x;
    x = tmpX;

    // TODO: added: nschneid
    /*if (OWLQN.isConstrained()) {
       newX = OWLQN.projectWeights(newX);
       x = OWLQN.projectWeights(x);
    }*/

    double[] tmpGrad = newGrad;
    newGrad = grad;
    grad = tmpGrad;

    ++iter;
}

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

private synchronized boolean getNextPoint(double alpha) {
    OWLQN owlqn = new OWLQN();
    ArrayMath.addMultInto(newX, x, dir, alpha);
    /*if (OWLQN.isConstrained())
       newX = OWLQN.projectWeights(newX);*/ //TODO
    if (l1weight > 0) {
        for (int i = 0; i < dim; i++) {
            //mheilman: I added this if-statement to avoid penalizing bias parameters.
            if (owlqn.biasParameters.contains(i)) {
                continue;
            }//from www  . j  a v a2s  .co m
            if (x[i] * newX[i] < 0.0) {
                newX[i] = 0.0;
            }
        }
    }
    return true;
}

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

synchronized void shift() {
    double[] nextS = null, nextY = null;

    int listSize = sList.size();

    if (listSize < m) {
        try {//from   w w  w .jav a2 s.  c om
            nextS = new double[dim];
            nextY = new double[dim];
        } catch (OutOfMemoryError e) {
            m = listSize;
            nextS = null;
        }
    }

    if (nextS == null) {
        nextS = sList.poll();
        nextY = yList.poll();
        roList.poll();
    }

    ArrayMath.addMultInto(nextS, newX, x, -1);
    ArrayMath.addMultInto(nextY, newGrad, grad, -1);

    double ro = ArrayMath.innerProduct(nextS, nextY);
    assert (ro != 0.0);

    sList.offer(nextS);
    yList.offer(nextY);
    roList.offer(ro);

    double[] tmpX = newX;
    newX = x;
    x = tmpX;

    // TODO: added: nschneid
    /*if (OWLQN.isConstrained()) {
       newX = OWLQN.projectWeights(newX);
       x = OWLQN.projectWeights(x);
    }*/

    double[] tmpGrad = newGrad;
    newGrad = grad;
    grad = tmpGrad;

    ++iter;
}

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From source file:impl.features.OWLQN.java

private boolean getNextPoint(double alpha) {
    ArrayMath.addMultInto(newX, x, dir, alpha);
    /*if (OWLQN.isConstrained())
       newX = OWLQN.projectWeights(newX);*/ //TODO OWLQN.projectWeights
    if (l1weight > 0) {
        for (int i = 0; i < dim; i++) {
            //mheilman: I added this if-statement to avoid penalizing bias parameters.
            if (OWLQN.biasParameters.contains(i)) {
                continue;
            }// w w  w . ja  va 2s  . c om
            if (x[i] * newX[i] < 0.0) {
                newX[i] = 0.0;
            }
        }
    }
    return true;
}

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