scripts/builtin/msvm.dml

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# Implements builtin multiclass SVM with squared slack variables, 
# learns one-against-the-rest binary-class classifiers by making a function call to l2SVM

# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# X               Double  ---         matrix X of feature vectors
# Y               Double  ---         matrix Y of class labels
# intercept       Boolean False       No Intercept ( If set to TRUE then a constant bias column is added to X)
# num_classes     integer 10          Number of classes
# epsilon         Double  0.001       Procedure terminates early if the reduction in objective function 
#                                     value is less than epsilon (tolerance) times the initial objective function value.
# lambda          Double  1.0         Regularization parameter (lambda) for L2 regularization
# maxIterations   Int     100         Maximum number of conjugate gradient iterations
# verbose         Boolean False       Set to true to print while training.
# ---------------------------------------------------------------------------------------------
 
#Output(s)
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# model           Double   ---        model matrix

m_msvm = function(Matrix[Double] X, Matrix[Double] Y, Boolean intercept = FALSE,
    Double epsilon = 0.001, Double lambda = 1.0, Integer maxIterations = 100, Boolean verbose = FALSE)
  return(Matrix[Double] model)
{
  if(min(Y) < 0)
    stop("MSVM: Invalid Y input, containing negative values")

  if(verbose)
    print("Running Multiclass-SVM")

  num_rows_in_w = ncol(X)
  if(intercept) {
    num_rows_in_w = num_rows_in_w + 1
  }

  if(ncol(Y) > 1) 
    Y = rowMaxs(Y * t(seq(1,ncol(Y))))

  # Assuming number of classes to be max contained in Y
  w = matrix(0, rows=num_rows_in_w, cols=max(Y))

  parfor(class in 1:max(Y)) {
    Y_local = 2 * (Y == class) - 1
    w[,class] = l2svm(X=X, Y=Y_local, intercept=intercept,
        epsilon=epsilon, lambda=lambda, maxIterations=maxIterations, 
        verbose= verbose, columnId=class)
  }
  
  model = w
}