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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

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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 {

  protected boolean skipValCalc = false;

  /**
   * 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 xScale)
   * 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
   */
  public abstract double valueAt(double[] x, double xScale, int[] batch);

  public double valueAt(double[] x, double xScale, int batchSize) {
    getBatch(batchSize);
    return valueAt(x, xScale, thisBatch);
  }

  /**
   * 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
   */
  public abstract 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);
  }

  /**
   * Performs stochastic gradient calculation based
   * on samples indexed by batch and does not apply regularization.
   * Does not update the parameter values.
   * Typically stores derivative information for later access.
   *
   * @param x Unscaled weights
   * @param batch Indices of which samples to compute function over
   */
  public abstract void calculateStochasticGradient(double[] x, int[] batch);

  /**
   * Performs stochastic gradient updates based
   * on samples indexed by batch and do not apply regularization.
   *
   * @param x unscaled weights
   * @param batchSize number of samples to pick next
   */
  public void calculateStochasticGradient(double[] x, int batchSize) {
    getBatch(batchSize);
    calculateStochasticGradient(x, thisBatch);
  }

}