It looks like the transpose may make the system under-determined. If you
try with
X = np.random.randn(*X.shape)
What happens?
On Thu, Jul 31, 2014 at 4:17 PM, Kyle Kastner <[email protected]> wrote:
> What is the shape of X
>
>
> On Thu, Jul 31, 2014 at 4:14 PM, Deepak Pandian <[email protected]
> > wrote:
>
>> On Thu, Jul 31, 2014 at 7:31 PM, Olivier Grisel
>> <[email protected]> wrote:
>> > The sign of the components is not deterministic. The absolute values
>> > should be the same.
>>
>> But the last component differs even in absolute values,
>> Pca_components:
>> [[-0.61098855 0.54326104 0.3089728 0.29598692 -0.01767037 0.25638981
>> -0.24499776 -0.02891609 -0.10832649 0.09922362]
>> [-0.04285247 0.58774877 -0.02102497 -0.18987243 -0.09547488 -0.37323125
>> 0.62203739 0.03558049 0.0780125 -0.27114941]
>> [ 0.3481033 0.32602361 -0.20594773 -0.26088651 -0.63592937 0.39839983
>> -0.20642102 -0.23287936 -0.0447964 0.00887286]]
>>
>> SVD on X (matches pca since it follows the same steps)
>> Below is V from svd of (X)
>> [[-0.61098855 0.54326104 0.3089728 0.29598692 -0.01767037 0.25638981
>> -0.24499776 -0.02891609 -0.10832649 0.09922362]
>> [-0.04285247 0.58774877 -0.02102497 -0.18987243 -0.09547488 -0.37323125
>> 0.62203739 0.03558049 0.0780125 -0.27114941]
>> [ 0.3481033 0.32602361 -0.20594773 -0.26088651 -0.63592937 0.39839983
>> -0.20642102 -0.23287936 -0.0447964 0.00887286]
>>
>>
>> SVD on X.T:
>>
>> Below is U.T of SVD(X.T)
>> [-0.61098855 0.54326104 0.3089728 0.29598692 -0.01767037 0.25638981
>> -0.24499776 -0.02891609 -0.10832649 0.09922362]
>> [-0.04285247 0.58774877 -0.02102497 -0.18987243 -0.09547488 -0.37323125
>> 0.62203739 0.03558049 0.0780125 -0.27114941]
>> [ 0.39253032 0.27426443 -0.09946156 -0.17669364 -0.55699776 0.60982874
>> 0.0461356 -0.18837449 -0.04298647 0.08936917]
>>
>> You can see that the final component doesn't match in terms of absolute
>> values.
>>
>>
>> I am pasting the code I used for svd(X.T) to check if I am making some
>> mistakes here.
>> 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=True)
>> return u,s,v
>>
>>
>> Thanks
>> Deepak
>>
>> --
>> With Regards,
>> Deepak Pandian
>> "Deconstructing world one piece at a time"
>>
>>
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>
>
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