提升LinAlgError("SVD没有收敛")LinAlgError:SVD没有收敛于matplotlib pca确定

Question

代码:

import numpy
from matplotlib.mlab import PCA
file_name = "store1_pca_matrix.txt"
ori_data = numpy.loadtxt(file_name,dtype='float', comments='#', delimiter=None,                 converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)
result = PCA(ori_data)

这是我的代码.虽然我的输入矩阵没有nan和inf,但我确实得到了下面所述的错误.

raise LinAlgError("SVD did not converge") LinAlgError: SVD did not converge

有什么问题？

Answer 1

当数据中存在inf或nan值时,可能会发生这种情况.

使用它来删除nan值:

ori_data.dropna(inplace=True)

Answer 2

I know this post is old, but in case someone else encounters the same problem. @jseabold was right when he said that the problem is nan or inf and the op was probably right when he said that the data did not have nan's or inf. However, if one of the columns in ori_data has always the same value, the data will get Nans, since the implementation of PCA in mlab normalizes the input data by doing

ori_data = (ori_data - mean(ori_data)) / std(ori_data).

The solution is to do:

result = PCA(ori_data, standardize=False)

In this way, only the mean will be subtracted without dividing by the standard deviation.

Answer 3

如果没有 inf 或 NaN 值，则可能是内存问题。请在具有更高 RAM 的机器上尝试。

Answer 4

我没有这个问题的答案,但我有没有nans和infs的复制场景.不幸的是,datataset相当大(96MB gzip).

import numpy as np
from StringIO import StringIO
from scipy import linalg
import urllib2
import gzip

url = 'http://physics.muni.cz/~vazny/gauss/X.gz'
X = np.loadtxt(gzip.GzipFile(fileobj=StringIO(urllib2.urlopen(url).read())), delimiter=',')
linalg.svd(X, full_matrices=False)

哪个上升:

LinAlgError: SVD did not converge

上:

>>> np.__version__
'1.8.1'
>>> import scipy
>>> scipy.__version__
'0.10.1'

但没有引起异常:

>>> np.__version__
'1.8.2'
>>> import scipy
>>> scipy.__version__
'0.14.0'