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################################################################################
# Normalization Functions #
# File: Normalization.R #
# Author: Shengqiao Li #
# Date: Octber 8, 2009 (initial) #
# Dependency: None #
################################################################################
normalize.unit<- function(data)
{
####normalize to [0,1]
minvect<- apply(data, 2, min);
rangevect<- apply(data, 2, function(x)diff(range(x)))
scale(data, center=minvect, scale=rangevect)
}
normalize.sigmoidal<- function(data)
{
#nonlinearly transform data into [-1,1] using a sigmoid function
z<- scale(data);
tanh(z/2)
}
normalize.softmax<- function(data)
{
#more or less linear in the middle range, and has a nonlinearity at both ends
z<- scale(data);
plogis(z)
}
normalize.decscale<- function (data)
{
#decimal scaling to a matrix or dataframe. Decimal scaling transforms the
#data into [-1,1] by finding k such that the absolute value of the maximum
#value of each attribute divided by 10^k is less than or equal to 1.
maxvect <- apply(abs(data), 2, max)
kvector <- ceiling(log10(maxvect))
scalefactor <- 10^kvector
scale(data, center = FALSE, scale = scalefactor)
}
################################################################################
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