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D = read($1)
C = read($2)

# reading input args
numClasses = $3
laplace_correction = 1

numRows = nrow(D)
numFeatures = ncol(D)

# Compute conditionals

# Compute the feature counts for each class
classFeatureCounts = matrix(0, rows=numClasses, cols=numFeatures)
parfor (i in 1:numFeatures) {
  Col = D[,i]
  classFeatureCounts[,i] = aggregate(target=Col, groups=C, fn="sum")
}

# 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*laplace_correction

# Compute class conditional probabilities
repClassSums = classSums %*% matrix(1,rows=1,cols=numFeatures);
class_conditionals = (classFeatureCounts + laplace_correction) / repClassSums;

# Compute class priors
class_counts = aggregate(target=C, groups=C, fn="count")
class_prior = class_counts / numRows;

# write out the model
write(class_prior, $4, format="text");
write(class_conditionals, $5, format="text");
