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Pythonで特異値分解(SVD)、低ランク近似(LRA)する

Last updated at 2017-01-15Posted at 2017-01-14

高速化したいならLapackとかPythonから使うことないと思うので、そういう科学技術計算をcudaとかCとかC++でやる人の答え合わせ程度に使っていただけると

と思ってメモ

#ソースコード

low_rank_approximation.py

import numpy as np
from scipy import linalg

def low_rank_approximation(a,rank):
    u, s, v = linalg.svd(a)
    ur = u[:, :rank]
    sr = np.matrix(linalg.diagsvd(s[:rank], rank,rank))
    vr = v[:rank, :]
    return np.asarray(ur*sr*vr)

A = np.array([[1,2,3],[4,5,6],[7,8,9]])
print A
B = low_rank_approximation(A,1)
print B

# python low_rank_approximation.py
# [[1 2 3]
#  [4 5 6]
#  [7 8 9]]
# [[ 1.73621779  2.07174246  2.40726714]
#  [ 4.2071528   5.02018649  5.83322018]
#  [ 6.6780878   7.96863051  9.25917322]]

#参考

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