Predicting Best Matching for new data about somoclu

In python prediction is relatively straight forward:

W = som.codebook.reshape((som.codebook.shape[0] * som.codebook.shape[1], som.codebook.shape[2]))
X = data

D = -2*np.dot(W, X.T) + (W**2).sum(1)[:, None] + (X**2).sum(1)[:, None].T
BMU = (D==D.min(0)[None,:]).astype("float32").T
NBMU =  BMU.reshape((X.shape[0], som.codebook.shape[0], som.codebook.shape[1]))
new_bmus = np.vstack(NBMU.nonzero()[1:][::-1]).T

X can be a single instance or as many as your RAM would allow.

[theSage21]

@xgdgsc Let me rephrase.
How do I use somoclu to classify new data given an already existing set of .wts and .ux?
Essentially what is the equivalent of predict given that fit is done via something like
somoclu -x 10 -y 10 input_file output_file?

[xgdgsc]

http://chem-eng.utoronto.ca/~datamining/Presentations/SOM.pdf

[theSage21]

@Dringite thank you.