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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.classify;

import edu.stanford.nlp.math.ArrayMath;
import edu.stanford.nlp.optimization.AbstractCachingDiffFunction;

import java.util.Arrays;


/**
 * Maximizes the conditional likelihood with a given prior.
 *
 * @author Jenny Finkel
 */

public class BiasedLogConditionalObjectiveFunction extends AbstractCachingDiffFunction {

  public void setPrior(LogPrior prior) {
    this.prior = prior;
  }

  protected LogPrior prior;

  protected int numFeatures = 0;
  protected int numClasses = 0;

  protected int[][] data = null;
  protected int[] labels = null;

  private double[][] confusionMatrix;
  
  @Override
  public int domainDimension() {
    return numFeatures * numClasses;
  }

  int classOf(int index) {
    return index % numClasses;
  }

  int featureOf(int index) {
    return index / numClasses;
  }

  protected int indexOf(int f, int c) {
    return f * numClasses + c;
  }

  public double[][] to2D(double[] x) {
    double[][] x2 = new double[numFeatures][numClasses];
    for (int i = 0; i < numFeatures; i++) {
      for (int j = 0; j < numClasses; j++) {
        x2[i][j] = x[indexOf(i, j)];
      }
    }
    return x2;
  }

  @Override
  protected void calculate(double[] x) {
    
    if (derivative == null) {
      derivative = new double[x.length];
    } else {
      Arrays.fill(derivative, 0.0);
    }

    value = 0.0;

    double[] sums = new double[numClasses];
    double[] probs = new double[numClasses];
    double[] weightedProbs = new double[numClasses];

    for (int d = 0; d < data.length; d++) {
      int[] features = data[d];
      int observedLabel = labels[d];
      // activation
      Arrays.fill(sums, 0.0);

      for (int c = 0; c < numClasses; c++) {
        for (int feature : features) {
          int i = indexOf(feature, c);
          sums[c] += x[i];
        }
      }

      double total = ArrayMath.logSum(sums);

      double[] weightedSums = new double[numClasses];
      for (int trueLabel = 0; trueLabel < numClasses; trueLabel++) {
        weightedSums[trueLabel] = Math.log(confusionMatrix[observedLabel][trueLabel]) + sums[trueLabel];
      }

      double weightedTotal = ArrayMath.logSum(weightedSums);
      
      for (int c = 0; c < numClasses; c++) {
        probs[c] = Math.exp(sums[c] - total);
        weightedProbs[c] = Math.exp(weightedSums[c] - weightedTotal);
        for (int feature : features) {
          int i = indexOf(feature, c);
          derivative[i] += probs[c] - weightedProbs[c];
        }
      }

      double tmpValue = 0.0;
      for (int c = 0; c < numClasses; c++) {
        tmpValue += confusionMatrix[observedLabel][c] * Math.exp(sums[c] - total);
      }
      value -= Math.log(tmpValue);
    }
    
    value += prior.compute(x, derivative);
    
  }



  public BiasedLogConditionalObjectiveFunction(GeneralDataset dataset, double[][] confusionMatrix) {
    this(dataset, confusionMatrix, new LogPrior(LogPrior.LogPriorType.QUADRATIC));
  }

  public BiasedLogConditionalObjectiveFunction(GeneralDataset dataset, double[][] confusionMatrix, LogPrior prior) {
    this(dataset.numFeatures(), dataset.numClasses(), dataset.getDataArray(), dataset.getLabelsArray(), confusionMatrix, prior);
  }

  public BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix) {
    this(numFeatures, numClasses, data, labels, confusionMatrix, new LogPrior(LogPrior.LogPriorType.QUADRATIC));
  }

  public BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix, LogPrior prior) {
    this.numFeatures = numFeatures;
    this.numClasses = numClasses;
    this.data = data;
    this.labels = labels;
    this.prior = prior;
    this.confusionMatrix = confusionMatrix;
  }
}