Python下的PPCA庫：pca-magic

jopen 11年前發布 | 19K 次閱讀 pca-magic 機器學習

Python下的PPCA庫，相比Scikit-Learn里的實現，該庫能更好的處理缺失數據，并基于另外的數據集進行插值。

Install via pip: 

pip install ppca

Load in the data which should be arranged asn_samplesbyfeatures. As usual, you should make sure your data is stationary (take first differences if possible) and standardized. 

from ppca import PPCA
ppca = PPCA(data)

Fit the model with parameterdspecifying the number of components and verbose printing convergence output if required. 

ppca.fit(d=100, verbose=True)

The model parameters and components will be attached to the ppca object. 

variance_explained = ppca.var_exp
components = ppca.X
model_params = ppca.C

If you want the principal components, calltransform. 

component_mat = ppca.transform()

Post fitting the model, save the model if you want. 

ppca.save('mypcamodel')

Load a model, post instantiating a PPCA object. This will make fitting/transforming much faster. 

ppca.load('mypcamodel.npy')

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