python - 掩码数组的特征值 (NumPy)

Question

如何计算掩码 NumPy 数组的特征值和特征向量（对于未掩码数组，这可以通过 来实现scipy.linalg.eig）。

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编辑：

事实证明，你毕竟可以做到这一点。

# a list of numpy matrices
import numpy as np
import numpy.ma as npma

import numpy.matlib as npm
import scipy as sp
import scipy.linalg as splin

import pandas as pd

# read in numpy data
import urllib
dataURL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/arrhythmia/arrhythmia.data'
dataFile = urllib.urlopen(dataURL)

# read in the data as a NumPy dataset
aArr = np.genfromtxt(dataFile, dtype = np.float,
                         delimiter = ',', missing_values = '?')

aArrMasked = npma.masked_array(aArr, np.isnan(aArr))
aArrMaskedCenter = aArrMasked - npma.mean(aArrMasked, axis=0)
print splin.eig(npma.cov(aArrMaskedCenter))