lsa.py 文件源码

def decompose(doc_vecs, n_features=100, normalize=False, flip=False):
    svd = TruncatedSVD(n_features)  
    if normalize:   
        if flip:
            lsa = make_pipeline(svd, Normalizer(copy=False))
            doc_mat = lsa.fit_transform(doc_vecs.transpose())
            doc_mat = doc_mat.transpose()
        else:
            lsa = make_pipeline(svd, Normalizer(copy=False))        
            doc_mat = lsa.fit_transform(doc_vecs)
        return doc_mat
    else:
        if flip:
            doc_mat = svd.fit_transform(doc_vecs.transpose())
            doc_mat = doc_mat.transpose()
        else:
            doc_mat = svd.fit_transform(doc_vecs)
        return doc_mat