Python下的PPCA库：pca-magic
jopen
10年前
Python下的PPCA库，相比Scikit-Learn里的实现，该库能更好的处理缺失数据，并基于另外的数据集进行插值。
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')