scripts/builtin/msvmPredict.dml

#-------------------------------------------------------------
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#-------------------------------------------------------------

# This Scripts helps in applying an trained MSVM
#
# INPUT:
#------------------------------------------------------------------------------
# X     matrix X of feature vectors to classify
# W     matrix of the trained variables
#------------------------------------------------------------------------------
#
# OUTPUT:
#-------------------------------------------------------------------------------
# YRaw  Classification Labels Raw, meaning not modified to clean
#       Labeles of 1's and -1's
# Y     Classification Labels Maxed to ones and zeros.
#-------------------------------------------------------------------------------

m_msvmPredict = function(Matrix[Double] X, Matrix[Double] W)
  return(Matrix[Double] YRaw, Matrix[Double] Y)
{
  # Robustness for datasets with missing values 
  numNaNs = sum(isNaN(X))
  if( numNaNs > 0 ) {
    print("msvm: matrix X contains "+numNaNs+" missing values, replacing with 0.")
    X = replace(target=X, pattern=NaN, replacement=0);
  }
  if(ncol(X) != nrow(W)){
    if(ncol(X) + 1 != nrow(W)){
      stop("MSVM Predict: Invalid shape of W ["+ncol(W)+","+nrow(W)+"] or X ["+ncol(X)+","+nrow(X)+"]")
    }
    YRaw = X %*% W[1:ncol(X),] + W[ncol(X)+1,]
    Y = rowIndexMax(YRaw)
  }
  else{
    YRaw = X %*% W
    Y = rowIndexMax(YRaw)
  }
}