-1
Ich verwende Spectral Clustering Library und Ähnlichkeitsmatrix ist sein Hauptargument. Meine Matrix wie folgt aussieht:Sklearn Spectral Clustering Fehler für Inf oder NaNs in Matrix
[[ 1.00000000e+00 8.47085137e-01 8.49644498e-01 8.49746438e-01
2.96473454e-01 8.50540412e-01 8.49462072e-01 8.50839475e-01
8.45951343e-01 5.76448265e-01 8.48265736e-01 8.43378943e-01
3.75348067e-01 1.17626480e-01 2.50357519e-01 8.50495202e-01
9.97541755e-01 8.49835674e-01 8.48770171e-01 8.45869271e-01
-5.97205241e-02]
[ 8.47085137e-01 1.00000000e+00 9.98547894e-01 9.98803332e-01
2.22305018e-01 9.98755219e-01 9.98502380e-01 9.98402601e-01
9.98778885e-01 5.66416311e-01 9.98639207e-01 9.98452172e-01
-6.10479042e-02 2.46741344e-02 -4.14116930e-03 9.98357419e-01
8.48955204e-01 9.98525354e-01 9.98900440e-01 9.98426618e-01
-6.51839614e-02]
[ 8.49644498e-01 9.98547894e-01 1.00000000e+00 9.98764222e-01
1.59017501e-01 9.98777492e-01 9.98797005e-01 9.98756310e-01
9.98785822e-01 5.71955127e-01 9.98834038e-01 9.98652820e-01
-5.95467715e-02 1.98107829e-02 -3.88527970e-03 9.98810942e-01
8.51337460e-01 9.98882675e-01 9.98815975e-01 9.98789494e-01
-6.69662309e-02]
[ 8.49746438e-01 9.98803332e-01 9.98764222e-01 1.00000000e+00
4.73518047e-01 9.98684853e-01 9.98839959e-01 9.99029920e-01
9.98804479e-01 5.67855583e-01 9.98759386e-01 9.98796277e-01
-6.07517782e-02 1.71388383e-02 -3.20996100e-03 9.98669121e-01
8.51600753e-01 9.98681806e-01 9.99072484e-01 9.98702177e-01
-6.29855810e-02]
[ 3.52784328e-01 2.41076867e-01 2.01621082e-01 4.11538647e-01
9.92999574e-01 2.09351787e-01 2.12464918e-01 1.84566399e-01
2.82162287e-01 8.88835155e-01 1.90613041e-01 2.12150578e-01
2.92104260e-01 6.25221827e-02 8.70607365e-01 2.88645877e-01
3.09283827e-01 2.81253950e-01 1.80307149e-01 2.49082955e-01
5.46192492e-02]
...
[ -5.97205241e-02 -6.51839614e-02 -6.69662309e-02 -6.29855810e-02
7.86918277e-02 -6.49002943e-02 -6.12003747e-02 -6.34500592e-02
-6.75593439e-02 7.23869691e-02 -6.20686862e-02 -5.94039824e-02
-1.00101778e-01 -1.14667128e-01 5.57606897e-02 -6.32884559e-02
-5.33734526e-02 -5.90822523e-02 -6.17068052e-02 -5.76615359e-02
1.00000000e+00]]
Und meinen Code ähnlich wie die Dokumentation Proben:
cl = SpectralClustering(n_clusters=4,affinity='precomputed')
y = cl.fit_predict(matrix)
Aber der folgenden Fehler tritt auf:
/usr/local/lib/python2.7/dist-packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/utils/validation.py:629: UserWarning: Array is not symmetric, and will be converted to symmetric by average with its transpose.
warnings.warn("Array is not symmetric, and will be converted "
/usr/local/lib/python2.7/dist-packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/utils/graph.py:172: RuntimeWarning: invalid value encountered in sqrt
w = np.sqrt(w)
Traceback (most recent call last):
File "/home/mahmood/PycharmProjects/sentence2vec/graphClustering.py", line 23, in <module>
y = cl.fit_predict(matrix)
File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/base.py", line 371, in fit_predict
self.fit(X)
File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/cluster/spectral.py", line 454, in fit
assign_labels=self.assign_labels)
File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/cluster/spectral.py", line 258, in spectral_clustering
eigen_tol=eigen_tol, drop_first=False)
File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/manifold/spectral_embedding_.py", line 254, in spectral_embedding
tol=eigen_tol)
File "/usr/lib/python2.7/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py", line 1545, in eigsh
symmetric=True, tol=tol)
File "/usr/lib/python2.7/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py", line 1033, in get_OPinv_matvec
return LuInv(A).matvec
File "/usr/lib/python2.7/dist-packages/scipy/sparse/linalg/interface.py", line 142, in __new__
obj.__init__(*args, **kwargs)
File "/usr/lib/python2.7/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py", line 922, in __init__
self.M_lu = lu_factor(M)
File "/usr/lib/python2.7/dist-packages/scipy/linalg/decomp_lu.py", line 58, in lu_factor
a1 = asarray_chkfinite(a)
File "/usr/lib/python2.7/dist-packages/numpy/lib/function_base.py", line 1022, in asarray_chkfinite
"array must not contain infs or NaNs")
ValueError: array must not contain infs or NaNs
erste Warnung ist akzeptabel, weil Matrix nicht ist symmetrisch, aber es gibt keine infs oder NaNs in der Matrix.
Wie überprüfen Sie, ob Ihre Matrix NaN/Inf-Werte enthält? – ahajib
Ich stelle gedruckte Matrix in Frage, und es scheint, dass es keine NaN/Inf-Werte gibt. –
Das ist nur ein Teil Ihrer Matrix und nicht genau. Sie müssen jedes Element überprüfen, nur um sicherzugehen. – ahajib