If you have a github account, you could create a gist: https://gist.github.com/ This results in a sort of temporary git repository you can push files to as with any git repository. If you push your data file and python code you use to process and plot it, I can take a look and see if I can figure out what the problem is. Alternatively, you could just push the plot there, but it would be harder for me to experiment with. Thanks! Jake
Timmy Wilson wrote: > This was rejected because attachments were > 40K > > What's the preferred way to pass random/tmp files? > > > ---------- Forwarded message ---------- > From: Timmy Wilson <[email protected]> > Date: Fri, Dec 9, 2011 at 8:57 AM > Subject: Re: [Scikit-learn-general] RuntimeError: Factor is exactly singular > To: [email protected] > > > the eigen_solver='dense' option eliminates the error, but the results > look different > > i attached the offending input (an adjacency matrix from twitter), and > the results of hessian w/ the dense option and standard w/ 30 > neighbors > > > On Thu, Dec 8, 2011 at 11:19 PM, Jacob VanderPlas > <[email protected]> wrote: > >> Timmy, >> Interesting... this means that none of the earlier ideas were on the >> right track, or else we'd see the same error for standard and modified. >> It turns out that Hessian and LTSA do very similar things. I still >> haven't wrapped my mind around it intuitively, but what you're seeing >> probably means that the hessian estimator is identical in two >> neighborhoods. I'm confused by the fact that adding noise and changing >> the number of neighbors is not affecting things... >> >> Can you try using eigen_solver='dense' (that should get around the >> problem, though it will be slow) and taking a look at the results to see >> if they're anywhere close to what standard and modified LLE give you? >> Jake >> >> Timmy Wilson wrote: >> >>> 'standard' + 'modified' both work fine >>> >>> 'hessian' + 'ltsa' both have issues >>> >>> ltsa is printing: >>> >>> RuntimeWarning: Diagonal number 2 is exactly zero. Singular matrix. >>> >>> and then setting everything to -nan >>> >>> i tried adding random noise and increasing n_neighbors -- but no dice >>> >>> Fabian's suggestion is still under review -- >>> https://github.com/scikit-learn/scikit-learn/pull/457 >>> >>> >>> >>> On Thu, Dec 8, 2011 at 11:39 AM, Fabian Pedregosa >>> <[email protected]> wrote: >>> >>> >>>> I reduced the problem to a difference of results with eigsh when >>>> working in shift-invert mode and when not. Notice that the following >>>> works with sigma=None but not with sigma=0. , thus seems to me that we >>>> should fall back to non invert-mode for singular matrices (see >>>> asociated pull request). >>>> >>>> ``` >>>> In [8]: M = sparse.csr_matrix([[1, 1, 1], [1, 1, 1], [1, 1, 1.]]) >>>> >>>> In [9]: linalg.eigsh(M, 1) >>>> Out[9]: >>>> (array([ 3.]), >>>> array([[-0.57735027], >>>> [-0.57735027], >>>> [-0.57735027]])) >>>> >>>> In [10]: linalg.eigsh(M, 1, sigma=0.) >>>> --------------------------------------------------------------------------- >>>> RuntimeError Traceback (most recent call last) >>>> /home/fabian/dev/sandbox/<ipython-input-10-f646f5be93ec> in <module>() >>>> ----> 1 linalg.eigsh(M, 1, sigma=0.) >>>> >>>> /home/fabian/envs/p26/lib/python2.6/site-packages/scipy/sparse/linalg/eigen/arpack/arpack.pyc >>>> in eigsh(A, k, M, sigma, which, v0, ncv, maxiter, tol, >>>> return_eigenvectors, Minv, OPinv, mode) >>>> 1484 if OPinv is None: >>>> 1485 Minv_matvec = get_OPinv_matvec(A, M, sigma, >>>> -> 1486 symmetric=True, >>>> tol=tol) >>>> 1487 else: >>>> 1488 OPinv = _aslinearoperator_with_dtype(OPinv) >>>> >>>> /home/fabian/envs/p26/lib/python2.6/site-packages/scipy/sparse/linalg/eigen/arpack/arpack.pyc >>>> in get_OPinv_matvec(A, M, sigma, symmetric, tol) >>>> 1004 def get_OPinv_matvec(A, M, sigma, symmetric=False, tol=0): >>>> 1005 if sigma == 0: >>>> -> 1006 return get_inv_matvec(A, symmetric=symmetric, tol=tol) >>>> 1007 >>>> 1008 if M is None: >>>> >>>> /home/fabian/envs/p26/lib/python2.6/site-packages/scipy/sparse/linalg/eigen/arpack/arpack.pyc >>>> in get_inv_matvec(M, symmetric, tol) >>>> 997 if isspmatrix_csr(M) and symmetric: >>>> 998 M = M.T >>>> --> 999 return SpLuInv(M).matvec >>>> 1000 else: >>>> 1001 return IterInv(M, tol=tol).matvec >>>> >>>> /home/fabian/envs/p26/lib/python2.6/site-packages/scipy/sparse/linalg/eigen/arpack/arpack.pyc >>>> in __init__(self, M) >>>> 890 """ >>>> 891 def __init__(self, M): >>>> --> 892 self.M_lu = splu(M) >>>> 893 LinearOperator.__init__(self, M.shape, self._matvec, >>>> dtype=M.dtype) >>>> 894 self.isreal = not np.issubdtype(self.dtype, >>>> np.complexfloating) >>>> >>>> /home/fabian/envs/p26/lib/python2.6/site-packages/scipy/sparse/linalg/dsolve/linsolve.pyc >>>> in splu(A, permc_spec, diag_pivot_thresh, drop_tol, relax, panel_size, >>>> options) >>>> 171 _options.update(options) >>>> 172 return _superlu.gstrf(N, A.nnz, A.data, A.indices, A.indptr, >>>> --> 173 ilu=False, options=_options) >>>> 174 >>>> 175 def spilu(A, drop_tol=None, fill_factor=None, drop_rule=None, >>>> permc_spec=None, >>>> >>>> RuntimeError: Factor is exactly singular >>>> >>>> In [11]: >>>> ``` >>>> >>>> ------------------------------------------------------------------------------ >>>> Cloud Services Checklist: Pricing and Packaging Optimization >>>> This white paper is intended to serve as a reference, checklist and point >>>> of >>>> discussion for anyone considering optimizing the pricing and packaging >>>> model >>>> of a cloud services business. Read Now! >>>> http://www.accelacomm.com/jaw/sfnl/114/51491232/ >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> [email protected] >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>>> >>> ------------------------------------------------------------------------------ >>> Cloud Services Checklist: Pricing and Packaging Optimization >>> This white paper is intended to serve as a reference, checklist and point of >>> discussion for anyone considering optimizing the pricing and packaging model >>> of a cloud services business. Read Now! >>> http://www.accelacomm.com/jaw/sfnl/114/51491232/ >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >> ------------------------------------------------------------------------------ >> Cloud Services Checklist: Pricing and Packaging Optimization >> This white paper is intended to serve as a reference, checklist and point of >> discussion for anyone considering optimizing the pricing and packaging model >> of a cloud services business. Read Now! >> http://www.accelacomm.com/jaw/sfnl/114/51491232/ >> _______________________________________________ >> Scikit-learn-general mailing list >> [email protected] >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> > > ------------------------------------------------------------------------------ > Cloud Services Checklist: Pricing and Packaging Optimization > This white paper is intended to serve as a reference, checklist and point of > discussion for anyone considering optimizing the pricing and packaging model > of a cloud services business. Read Now! > http://www.accelacomm.com/jaw/sfnl/114/51491232/ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Cloud Services Checklist: Pricing and Packaging Optimization This white paper is intended to serve as a reference, checklist and point of discussion for anyone considering optimizing the pricing and packaging model of a cloud services business. Read Now! http://www.accelacomm.com/jaw/sfnl/114/51491232/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
