#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------

# This function calls the multiLogReg-function in which solves Multinomial
# Logistic Regression using Trust Region method
#
# INPUT:
# -------------------------------------------------------------------------------------
# X            matrix of feature vectors
# Y            matrix with category labels
# threshold    threshold to clear otherwise return X and Y unmodified
# verbose      flag specifying if logging information should be printed
# -------------------------------------------------------------------------------------
#
# OUTPUT:
# -------------------------------------------------------------------------------------
# Xout     abstained output X
# Yout     abstained output Y
# -------------------------------------------------------------------------------------

m_abstain = function(Matrix[Double] X, Matrix[Double] Y, Double threshold, Boolean verbose = FALSE)
return (Matrix[Double] Xout, Matrix[Double] Yout)
{
  Xout = X
  Yout = Y
  if(min(Y) != max(Y) & max(Y) <= 2)
  {
    betas = multiLogReg(X=X, Y=Y, icpt=1, reg=1e-4, maxi=100, maxii=0, verbose=verbose)
    [prob, yhat, accuracy] = multiLogRegPredict(X, betas, Y, FALSE)
    
    inc = ((yhat != Y) & (rowMaxs(prob) > threshold))
    if(sum(inc) > 0)
    {
      Xout = removeEmpty(target = X, margin = "rows", select = (inc == 0) )
      Yout = removeEmpty(target = Y, margin = "rows", select = (inc == 0) )
    }
  }
}
