AbstractStochasticCachingDiffUpdateFunction.java

package edu.stanford.nlp.optimization;

/**
 * Function for stochastic calculations that does update in place
 * (instead of maintaining and returning the derivative)
 *
 * Weights are represented by an array of doubles and a scalar
 *  that indicates how much to scale all weights by
 * This allows all weights to be scaled by just modifying the scalar
 *
 * @author Angel Chang
 */
public abstract class AbstractStochasticCachingDiffUpdateFunction
        extends AbstractStochasticCachingDiffFunction {

  /**
   * Gets a random sample (this is sampling with replacement)
   * @param sampleSize number of samples to generate
   * @return array of indices for random sample of sampleSize
   */
  public int[] getSample(int sampleSize)
  {
    int[] sample = new int[sampleSize];
    for(int i = 0; i< sampleSize;i++){
      sample[i] = randGenerator.nextInt(this.dataDimension()); //Just generate a random index
    }
    return sample;
  }

  /**
   * Computes value of function for specified value of x (scaled by xcale)
   *  only over samples indexed by batch
   * @param x - unscaled weights
   * @param xscale - how much to scale x by when performing calculations
   * @param batch - indices of which samples to compute function over
   * @return value of function at specified x (scaled by xscale) for samples
   */
  abstract public double valueAt(double[] x, double xscale, int[] batch);

  /**
   * Performs stochastic update of weights x (scaled by xscale) based
   *  on samples indexed by batch
   * @param x - unscaled weights
   * @param xscale - how much to scale x by when performing calculations
   * @param batch - indices of which samples to compute function over
   * @param gain - how much to scale adjustments to x
   * @return value of function at specified x (scaled by xscale) for samples
   */
  abstract public double calculateStochasticUpdate(double[] x, double xscale, int[] batch, double gain);

  /**
   * Performs stochastic update of weights x (scaled by xscale) based
   *  on next batch of batchSize
   * @param x - unscaled weights
   * @param xscale - how much to scale x by when performing calculations
   * @param batchSize - number of samples to pick next
   * @param gain - how much to scale adjustments to x
   * @return value of function at specified x (scaled by xscale) for samples
   */
  public double calculateStochasticUpdate(double[] x, double xscale, int batchSize, double gain) {
    getBatch(batchSize);
    return calculateStochasticUpdate(x, xscale, thisBatch, gain);
  }

}