#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
#
#-------------------------------------------------------------


#
#
# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# X               Double  ---         matrix X of feature vectors to classify
# W               Double  ---         matrix of the trained variables
# verbose         Boolean FALSE       Set to true if one wants print statements.
# ---------------------------------------------------------------------------------------------
# OUTPUT:
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# Y^              Double  ---         Classification Labels Raw, meaning not modified to clean
#                                     Labeles of 1's and -1's
# Y               Double  ---         Classification Labels Maxed to ones and zeros.


m_l2svmPredict = function(Matrix[Double] X, Matrix[Double] W, Boolean verbose = FALSE)
  return(Matrix[Double] YRaw, Matrix[Double] Y)
{
  if(ncol(X) != nrow(W)){
    if(ncol(X) + 1 != nrow(W)){
      stop("l2svm 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)
  }
}