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#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------
# Scale and center individual features in the input matrix (column wise.) using z-score to scale the values.
# ---------------------------------------------------------------------------------------------
# NAME TYPE DEFAULT MEANING
# ---------------------------------------------------------------------------------------------
# X Matrix --- Input feature matrix
# Center Boolean TRUE Indicates whether or not to center the feature matrix
# Scale Boolean TRUE Indicates whether or not to scale the feature matrix
# ---------------------------------------------------------------------------------------------
# Y Matrix --- Output feature matrix with K columns
# ColMean Matrix --- The column means of the input, subtracted if Center was TRUE
# ScaleFactor Matrix --- The Scaling of the values, to make each dimension have similar value ranges
# ---------------------------------------------------------------------------------------------
m_scale = function(Matrix[Double] X, Boolean center, Boolean scale)
return (Matrix[Double] Y, Matrix[Double] ColMean, Matrix[Double] ScaleFactor)
{
if(center){
ColMean = colMeans(X)
X = X - ColMean
}
else {
# Allocate the ColMean as an empty matrix,
# to return something on the function call.
ColMean = matrix(0,rows=0,cols=0)
}
if (scale) {
N = nrow(X)
ScaleFactor = sqrt(colSums(X^2)/(N-1))
# Replace entries in the scale factor that are 0 with 1.
# To avoid division by 0, introducing NaN to the ouput.
ScaleFactor = replace(target=ScaleFactor,
pattern=0, replacement=1);
X = X/ScaleFactor
}
else{
# Allocate the Scale factor as an empty matrix,
# to return something on the function call.
ScaleFactor = matrix(0, rows= 0, cols=0)
}
Y = X
}