Python Sklearn矩阵中inf或NAN的谱聚类错误

我用的是相似矩阵，它的主要论点。我的矩阵看起来像：

[[  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]]

我的代码与文档示例类似：

cl = SpectralClustering(n_clusters=4,affinity='precomputed')
y = cl.fit_predict(matrix)

但出现以下错误：

/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

/usr/local/lib/python2.7/dist packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/utils/validation.py:629:UserWarning:数组不是对称的，将通过转置平均值转换为对称的。
warnings.warn（“数组不是对称的，将被转换”
/usr/local/lib/python2.7/dist软件包/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/utils/graph.py:172:运行时警告：在sqrt中遇到无效值
w=np.sqrt（w）
回溯（最近一次呼叫最后一次）：
文件“/home/mahmood/PycharmProjects/sentence2vec/graphClustering.py”，第23行，在
y=cl.fit_预测（矩阵）
文件“/usr/local/lib/python2.7/dist packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/base.py”，第371行，在fit_中
自我适应（X）
文件“/usr/local/lib/python2.7/dist packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/cluster/spectrum.py”，第454行
分配\u标签=自身。分配\u标签）
文件“/usr/local/lib/python2.7/dist packages/scikit_learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/cluster/spectrum.py”，第258行，spectrum_clustering
本征值=本征值，先下降值=假）
文件“/usr/local/lib/python2.7/dist packages/scikit_-learn-0.17.1-py2.7-linux-x86_64.egg/sklearn/manifold/spectrum_-embedding_u.py”，第254行，spectrum_-embedding中
tol=本征值（tol）
eigsh中的文件“/usr/lib/python2.7/dist packages/scipy/sparse/linalg/eigen/arpack/arpack.py”，第1545行
对称=真，tol=tol）
文件“/usr/lib/python2.7/dist packages/scipy/sparse/linalg/eigen/arpack/arpack.py”，第1033行，在get_OPinv_matvec中
返回LuInv（A）.matvec
文件“/usr/lib/python2.7/dist-packages/scipy/sparse/linalg/interface.py”，第142行，新__
对象初始化（*args，**kwargs）
文件“/usr/lib/python2.7/dist packages/scipy/sparse/linalg/eigen/arpack/arpack.py”，第922行，在__
self.M_lu=lu_系数（M）
文件“/usr/lib/python2.7/dist packages/scipy/linalg/decomp_lu.py”，第58行，以lu_系数表示
a1=asarray_chkfinite（a）
asarray\u chkfinite中的文件“/usr/lib/python2.7/dist packages/numpy/lib/function\u base.py”，第1022行
“数组不能包含INF或NAN”）
ValueError:数组不能包含INF或NAN

第一个警告是可以接受的，因为矩阵不是对称的，但矩阵中没有INF或NaN。

NaN值的出现是因为矩阵不是相似性矩阵：您的数据包含负相似性。！当取这些值的

sqrt

时，会得到

NaN

，因此会出现错误

这些警告不仅仅是为了好玩——矩阵分解技术有着严格的要求，允许它们工作并返回有意义的结果

---

首先修复负面相似性，然后重试。

如何检查矩阵是否包含NaN/Inf值？我对打印的矩阵提出了质疑，似乎没有NaN/Inf值。这只是矩阵的一部分，并不准确。您必须检查它的每个元素以确保。numpy.any（numpy.isnan（matrix））和numpy.any（numpy.isinf（matrix））都返回False。我想这已经足够了。我接受了你的建议，但是第一个警告就消失了。第二次警告和其他错误仍然强烈！也不能有负值<代码>sqrt（-1）=nan