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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 |
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centerColumns(X)
returns X - mean(X), where the mean is taken over the columns of X |
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standardizeColumns(X)
returns (X - mean(X)) / std(X) |
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standardizeRows(X)
returns (X - mean(X)) / std(X) |
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maxvar(X,
numVariables)
returns the numVariables variables with the highest variance |
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dmat(X)
returns the Euclidean distance-squared matrix |
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norm2(x)
return the 2-norm of a vector given as a list or numpy array |
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normalizeNorm(X)
normalize each row of X to unit vectors |
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corrcoef2(X)
compute the correlation between the rows of the matrix X more space
efficient than numpy version |
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std(m,
axis=0)
std(m,axis=0) returns the standard deviation along the given
dimension of m. |
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