[SciPy-User] bug in svd ?
Sun Sep 25 11:56:48 CDT 2011
Please ignore this mail...the implementation is working correctly, it
was a silly error on my part.
On Sunday 25 September 2011 11:15 AM, pratik wrote:
> Hi Scipy users,
> I was using the scikits.learn package for pca analysis, but couldn't get
> the desired principal components (which were apparant from the structure
> of the data). So i just took the svd of the covariance matrix of the
> input data (after making the mean 0, of course). But on examining the
> singular values, i found that they are *NOT sorted in decreasing order*
> (as they should be; although i can of course sort it myself now, the
> scikits.learn package depends upon this fact) This is also mentioned in
> the code; that the singular values should be sorted:
> u : ndarray
> Unitary matrix. The shape of `u` is (`M`, `M`) or (`M`, `K`)
> depending on value of ``full_matrices``.
> s : ndarray
> The singular values, sorted so that ``s[i] >= s[i+1]``. `s` is
> a 1-d array of length min(`M`, `N`).
> v : ndarray
> Unitary matrix of shape (`N`, `N`) or (`K`, `N`), depending on
> I am attaching the code and the data for you to examine. Just print out
> the values of the s array in ipython to see what i mean...
More information about the SciPy-User