np.dot(X.T, X) is invertible.None of the features have a 0 variance and I don't
think they are correlated.
"Would you look at the eigenvalues of this thing please?"
I calculated the eigenvalues in the following manner. Sorry, I am not sure
if this is what you wanted me to do or what I should look for.
>>> print np.linalg.eig(np.dot(X.T,X))array([ 6.98841511e+03,
3.74800914e+02, 1.91861359e+02,
1.60115903e+02, 1.43342111e+02, 1.32635349e+02,
1.05821870e+02, 1.04876518e+02,
9.35168895e+01, 8.17388838e+01,
7.32905453e+01, 6.78274062e+01,
3.00527208e+01, 3.49964917e+01, 3.54243068e+01,
4.34113226e+01, 5.59046873e+01,
4.89676135e+01, 5.16760071e-14,
-4.21817181e-15])
From: Virgile Fritsch <[email protected]>
Sent: Thu, 28 Jun 2012 17:44:35
To: [email protected]
Subject: Re: [Scikit-learn-general] EllipticEnvelope giving error IndexError:
0-d arrays can't be indexed
It may come from the fact that np.dot(X, X.T) is not invertible.
I meant, of course, np.dot(X.T, X).
Would you look at the eigenvalues of this thing please?
On Thu, Jun 28, 2012 at 10:33 AM, Virgile Fritsch
<[email protected]> wrote:
Hi,
It may come from the fact that np.dot(X, X.T) is not invertible.
Is it possible that two (or more) features are correlated? Or that one (or
more) features have a 0 variance?
If yes, try to remove those features.
Let us know if you think this could be the reason why it fails.
Virgile
On Thu, Jun 28, 2012 at 1:08 AM, ert <[email protected]> wrote:
Hi,
I am trying to use EllipticEnvelope for outlier detection but it results in an
error when trying to use the m.fit(X) function (stacktrace below).
Here m = EllipticEnvelope(contamination=0.1)
X.shape gives output (731, 22)X is not a sparse matrix.
Initially, I thought there was some prob with my X, howeverm = OneClassSVM()
m.fit(X)runs fine without any errors. I am not sure if these are
related. Any idea what could be causing this?
StackTrace (note 'instances' is a list of lists):-
Traceback (most recent call last): File "create_classifier_new.py", line
274, in <module> m.fit(numpy.array(instances)) File
"/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-x86_64.egg/sklearn/covariance/outlier_detection.py",
line 176, in fit
MinCovDet.fit(self, X) File
"/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-x86_64.egg/sklearn/covariance/robust_covariance.py",
line 513, in fit random_state=self.random_state)
File
"/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-x86_64.egg/sklearn/covariance/robust_covariance.py",
line 360, in fast_mcd random_state=random_state)
File
"/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-x86_64.egg/sklearn/covariance/robust_covariance.py",
line 228, in select_candidates random_state=random_state))
File
"/usr/local/lib/python2.6/dist-packages/scikit_learn-0.12_git-py2.6-linux-x86_64.egg/sklearn/covariance/robust_covariance.py",
line 120, in c_step if np.allclose(det, previous_det):
File "/usr/lib/python2.6/dist-packages/numpy/core/numeric.py", line
1671, in allclose if not all(x[xinf] == y[xinf]):IndexError: 0-d
arrays can't be indexed
Thanks
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