blob: 0d55de57b32b83f275b04749681321f6fe634640 [file] [log] [blame]
#-------------------------------------------------------------
#
# 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
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# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
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#
#-------------------------------------------------------------
# Related to [SYSTEMDS-2902] dependency function for cleaning pipelines
# impute the data by mode value
# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME TYPE DEFAULT MEANING
# ---------------------------------------------------------------------------------------------
# X Double --- Data Matrix (Recoded Matrix for categorical features)
# ---------------------------------------------------------------------------------------------
#Output(s)
# ---------------------------------------------------------------------------------------------
# NAME TYPE DEFAULT MEANING
# ---------------------------------------------------------------------------------------------
# X Double --- imputed dataset
m_imputeByMode = function(Matrix[Double] X)
return(Matrix[Double] X)
{
Mask = is.na(X)
X = replace(target=X, pattern=NaN, replacement=0)
colMode = matrix(0, 1, ncol(X))
for(i in 1: ncol(X)) {
X_c = removeEmpty(target=X[, i], margin = "rows", select=(X[, i] < 1)==0)
if(sum(X_c) == 0)
colMode[1, i] = 1
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
}
}
Mask = Mask * colMode
X = X + Mask
}