python - sklearn PCA producing numpy.linalg.linalg.LinAlgError -

i wanted run pca on matrix, got numpy.linalg.linalg.linalgerror. attached matrix , code.

get matrix here: http://workupload.com/file/yvsvhgja

import numpy np sklearn.decomposition import pca matrix = np.load("matrix.npy") transformed = pca(n_components=3).fit_transform(matrix) 

here full stack trace, think can reproduce it.

traceback (most recent call last):   file "<stdin>", line 1, in <module>   file "/home/user/anaconda/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 242, in fit_transform     u, s, v = self._fit(x)   file "/home/user/anaconda/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 275, in _fit     u, s, v = linalg.svd(x, full_matrices=false)   file "/home/user/anaconda/lib/python2.7/site-packages/scipy/linalg/decomp_svd.py", line 109, in svd     raise linalgerror("svd did not converge") numpy.linalg.linalg.linalgerror: svd did not converge 

any appreciated.

ps:

np.__version__ '1.9.2'  sklearn.__version__ '0.15.2' 

pps: running linux

it works on macs, guess doesn't much.
try x[:,:100] :1000 ?

there lapack tests svd; daunting.
"tell me lapack installation" command useful, don't see offhand.

from __future__ import division import platform import sys import numpy np numpy.distutils.system_info import get_info   np.set_printoptions( threshold=100, edgeitems=10, linewidth=80,         formatter = dict( float = lambda x: "%.2g" % x ))  # float arrays %.2g  def versions():     print "versions: numpy %s  python %s " % (         np.__version__, sys.version.split()[0] )     if platform.system() == "darwin":         print "mac %s" % platform.mac_ver()[0]     else:         print platform.platform( terse=1 )  # ?     info in "blas_opt lapack_opt " .split():         print "%s: %s" % (info, get_info( info, 0 ))     print ""  versions()  #............................................................................... x = np.load( "matrix.npy" ) print "x:", x.shape, np.percentile( x, q=[0,25,50,75,100] )  u, sing, vt = np.linalg.svd( x, full_matrices=false )  print "np.linalg.svd: x %s -> u %s  sing %s  vt %s" % (     x.shape, u.shape, sing.shape, vt.shape ) print "svd sing:", sing 

---

versions: numpy 1.9.2  python 2.7.6  mac 10.8.3 blas_opt: {'extra_link_args': ['-wl,-framework', '-wl,accelerate'], 'extra_compile_args': ['-msse3', '-dapple_accelerate_sgemv_patch', '-i/system/library/frameworks/veclib.framework/headers'], 'define_macros': [('no_atlas_info', 3)]} lapack_opt: {'extra_link_args': ['-wl,-framework', '-wl,accelerate'], 'extra_compile_args': ['-msse3', '-dapple_accelerate_sgemv_patch'], 'define_macros': [('no_atlas_info', 3)]}  x: (384, 5000) [-4.4e+02 -20 -0.27 17 4.5e+02] np.linalg.svd: x (384, 5000) -> u (384, 384)  sing (384,)  vt (384, 5000) svd sing: [5e+04 2.3e+04 2.1e+04 1.3e+04 1.2e+04 1.1e+04 1.1e+04 4.3e+03 3.3e+03 1.8e+03  ..., 0.00014 0.00014 0.00013 0.00013 0.00011 5.3e-12 5.3e-12 5.1e-16 1.3e-16  3.3e-17]