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

# This function impute the data by mode value
# Related to [SYSTEMDS-2902] dependency function for cleaning pipelines
#
# INPUT:
# ------------------------------------------------------------------------------------
# X     Data Matrix (Recoded Matrix for categorical features)
# ------------------------------------------------------------------------------------
#
# OUTPUT:
# --------------------------------------------------------------------------------------
# X     imputed dataset
# --------------------------------------------------------------------------------------

m_imputeByMode = function(Matrix[Double] X)
return(Matrix[Double] X, Matrix[Double] imputedVec)
{

  Xt = replace(target=X, pattern=NaN, replacement=0)
  colMode = matrix(0, 1, ncol(Xt))
  for(i in 1: ncol(Xt)) {
    X_c = removeEmpty(target=Xt[, i], margin = "rows", select=(X[, i] < 1)==0)
    if(sum(X_c) == 0)
      colMode[1, i] = 0
    else {
      cat_counts = table(X_c, 1, nrow(X_c), 1);  # counts for each category
      colMode[1,i] = as.scalar(rowIndexMax(t(cat_counts))) # mode
    }
  }
  imputedVec = colMode
  X = imputeByModeApply(X, imputedVec)
}

