Package PyML :: Package preproc :: Module preproc
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Module preproc

source code

Classes
  Correlator
  Standardizer
class for performing feature normalization
Functions
 
pca(X, numcomp=None)
returns the matrix X as represented in the numcomp leading principal components if numcomp is None, all principal components are returned
source code
 
centerColumns(X)
returns X - mean(X), where the mean is taken over the columns of X
source code
 
centerRows(X) source code
 
standardizeColumns(X)
returns (X - mean(X)) / std(X)
source code
 
standardizeRows(X)
returns (X - mean(X)) / std(X)
source code
 
maxvar(X, numVariables)
returns the numVariables variables with the highest variance
source code
 
dmat(X)
returns the Euclidean distance-squared matrix
source code
 
norm2(x)
return the 2-norm of a vector given as a list or numpy array
source code
 
normalizeNorm(X)
normalize each row of X to unit vectors
source code
 
corrcoef2(X)
compute the correlation between the rows of the matrix X more space efficient than numpy version
source code
 
std(m, axis=0)
std(m,axis=0) returns the standard deviation along the given dimension of m.
source code
 
corrcoef(X) source code
 
corrcoefij(X, i, j) source code
Function Details

std(m, axis=0)

source code 

std(m,axis=0) returns the standard deviation along the given dimension of m. The result is unbiased with division by N-1. If m is of integer type returns a floating point answer.