blob: b4209365012d05d2951429b7ec5d87d5bc39dd54 [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
#
# 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 performs min-max normalization (rescaling to [0,1]).
#
# This function is deprecated, use normalize instead.
#
# INPUT:
# ---------------------------------------------
# X Input feature matrix
# ---------------------------------------------
#
# OUTPUT:
# --------------------------------------------
# Y Scaled output matrix
# --------------------------------------------
m_scaleMinMax = function(Matrix[Double] X)
return (Matrix[Double] Y)
{
print("Deprecated scaleMinMax use normalize instead")
# compute feature ranges for transformations
cmin = colMins(X);
cmax = colMaxs(X);
diff = (cmax - cmin)
# avoid division by zero and divide by 1 instead
diff = replace(target=diff, pattern=0, replacement=1);
# normalize features to given range ([0,1] if indeed min/max)
Y = (X - cmin) / diff;
}