Your components should be in ut.T
On Thu, Jul 31, 2014 at 2:48 PM, Michael Eickenberg <
[email protected]> wrote:
> Forget what I said. I didn't read your test properly.
>
>
> On Thu, Jul 31, 2014 at 2:47 PM, Michael Eickenberg <
> [email protected]> wrote:
>
>> vt.T should be equal to the output of sklearn_pca(X.T).
>>
>> There is a big difference in centered across samples or across features.
>> Sklearn PCA centers across samples and that is the standard afaik.
>>
>> Hope that helps,
>> Michael
>>
>>
>> On Thu, Jul 31, 2014 at 2:41 PM, Deepak Pandian <
>> [email protected]> wrote:
>>
>>> Hello Everyone,
>>>
>>> I assumed that doing a PCA on X is equivalent to performing a SVD on
>>> a mean-centered X.
>>>
>>> For sklearn.pca the input matrix is of the shape, (n_samples,n_features).
>>>
>>> When I perform a SVD on a matrix X shaped (n_features,n_samples)
>>> ,some of the eigen vectors arent matching with the pca.components_
>>>
>>>
>>> def sklearn_pca(X):
>>> pca =PCA(n_components=4)
>>> pca.fit(X)
>>> return pca.components_
>>>
>>>
>>> def svd_pca(X):
>>> #every sample is a row
>>> X= X- np.mean(X,axis=0)
>>> u,s,v = np.linalg.svd(X,full_matrices=False)
>>> return u,s,v
>>>
>>> def svd_t(X):
>>> #x.shape = n_features,n_samples
>>> #every sample is a column
>>> mean= np.mean(X,axis=1)
>>> mean = mean[:,np.newaxis]
>>> X = X -mean
>>> u,s,v = np.linalg.svd(X,full_matrices=False)
>>> return u,s,v
>>>
>>> def test_svd_pca():
>>> rng = np.random.RandomState(1)
>>> X = rng.randn(4,10)
>>> XT = np.copy(X)
>>> PX= np.copy(X)
>>> pca_comp = sklearn_pca(PX)
>>> u,s,v = svd_pca(X)
>>> ut,st,vt = svd_t(X.T)
>>>
>>>
>>> I expected pca.components_ to be equivalent to v and vt.T. While its
>>> same as v , I get mismatches with vt.T
>>>
>>> Is it right to expect vt.T to be same as pca.components_? If not
>>> please help me clear my misunderstanding.
>>>
>>> --
>>> With Regards,
>>> Deepak
>>>
>>>
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>>
>>
>
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