I get the following error when running Hessian-based LLE::
/home/timmyt/projects/smarttypes/smarttypes/scripts/reduce_twitter_graph.py
in <module>()
32 print "Passed our little test: following %s users!" %
len(tmp_followies)
33
---> 34 results = reduce_graph(adjacency_matrix, follower_ids,
out_dim=2, n_neighbors=30, method='hessian')
35
36
/home/timmyt/projects/graphreduce/graphreduce.py in
reduce_graph(input_graph, sample_ids, out_dim, n_neighbors, method)
7 def reduce_graph(input_graph, sample_ids, out_dim=2,
n_neighbors=30, method='modified'):
8 clf = manifold.LocallyLinearEmbedding(n_neighbors,
out_dim=out_dim, method=method)
----> 9 results = clf.fit_transform(input_graph)
10 print "Done. Reconstruction error: %g" % clf.reconstruction_error_
11 return results
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/manifold/locally_linear.pyc
in fit_transform(self, X, y)
573 X_new: array-like, shape (n_samples, out_dim)
574 """
--> 575 self._fit_transform(X)
576 return self.embedding_
577
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/manifold/locally_linear.pyc
in _fit_transform(self, X)
544 eigen_solver=self.eigen_solver, tol=self.tol,
545 max_iter=self.max_iter, method=self.method,
--> 546 hessian_tol=self.hessian_tol,
modified_tol=self.modified_tol)
547
548 def fit(self, X, y=None):
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/manifold/locally_linear.pyc
in locally_linear_embedding(X, n_neighbors, out_dim, reg,
eigen_solver, tol, max_iter, method, hessian_tol, modified_tol)
457
458 return null_space(M, out_dim, k_skip=1, eigen_solver=eigen_solver,
--> 459 tol=tol, max_iter=max_iter)
460
461
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/manifold/locally_linear.pyc
in null_space(M, k, k_skip, eigen_solver, tol, max_iter)
139 if eigen_solver == 'arpack':
140 eigen_values, eigen_vectors = eigsh(M, k + k_skip, sigma=0.0,
--> 141 tol=tol, maxiter=max_iter)
142
143 return eigen_vectors[:, k_skip:], np.sum(eigen_values[k_skip:])
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/utils/arpack.pyc
in eigsh(A, k, M, sigma, which, v0, ncv, maxiter, tol,
return_eigenvectors, Minv, OPinv, mode)
1486 if OPinv is None:
1487 Minv_matvec = get_OPinv_matvec(A, M, sigma,
-> 1488 symmetric=True, tol=tol)
1489 else:
1490 OPinv = _aslinearoperator_with_dtype(OPinv)
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/utils/arpack.pyc
in get_OPinv_matvec(A, M, sigma, symmetric, tol)
1008 def get_OPinv_matvec(A, M, sigma, symmetric=False, tol=0):
1009 if sigma == 0:
-> 1010 return get_inv_matvec(A, symmetric=symmetric, tol=tol)
1011
1012 if M is None:
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/utils/arpack.pyc
in get_inv_matvec(M, symmetric, tol)
1001 if isspmatrix_csr(M) and symmetric:
1002 M = M.T
-> 1003 return SpLuInv(M).matvec
1004 else:
1005 return IterInv(M, tol=tol).matvec
/home/timmyt/.virtualenvs/smarttypes/lib/python2.6/site-packages/sklearn/utils/arpack.pyc
in __init__(self, M)
894 """
895 def __init__(self, M):
--> 896 self.M_lu = splu(M)
897 LinearOperator.__init__(self, M.shape, self._matvec,
dtype=M.dtype)
898 self.isreal = not np.issubdtype(self.dtype, np.complexfloating)
/usr/lib/python2.6/dist-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
------------------------------------------------------------------------------
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
