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m_naiveBayes = function(Matrix[Double] D, Matrix[Double] C, Double laplace = 1, Boolean verbose = TRUE)
  return (Matrix[Double] prior, Matrix[Double] classConditionals) 
{
  laplaceCorrection = laplace;
  numRows = nrow(D);
  numFeatures = ncol(D);
  numClasses = max(C);

  # Compute conditionals
  # Compute the feature counts for each class
  classFeatureCounts = aggregate(target=D, groups=C, fn="sum", ngroups=as.integer(numClasses));

  # Compute the total feature count for each class 
  # and add the number of features to this sum
  # for subsequent regularization (Laplace's rule)
  classSums = rowSums(classFeatureCounts) + numFeatures*laplaceCorrection;

  # Compute class conditional probabilities
  classConditionals = (classFeatureCounts + laplaceCorrection) / classSums;

  # Compute class priors
  classCounts = aggregate(target=C, groups=C, fn="count", ngroups=as.integer(numClasses));
  prior = classCounts / numRows;

  # Compute accuracy on training set
  if( verbose ) {
    logProbs = D %*% t(log(classConditionals)) + t(log(prior));
    acc = sum(rowIndexMax(logProbs) == C) / numRows * 100;
    print("Training Accuracy (%): " + acc);
  }
}
