[Numpy-discussion] Efficient orthogonalisation with scipy/numpy
Robert Kern
robert.kern@gmail....
Tue Jan 19 14:58:32 CST 2010
On Tue, Jan 19, 2010 at 14:47, Gael Varoquaux
<gael.varoquaux@normalesup.org> wrote:
> On Tue, Jan 19, 2010 at 02:22:30PM -0600, Robert Kern wrote:
>> > y = y - np.dot(confounds.T, linalg.lstsq(confounds.T, y)[0])
>
>> > with np = numpy and linalg = scipy.linalg where scipy calls ATLAS.
>
>> For clarification, are you trying to find the components of the y
>> vectors that are perpendicular to the space spanned by the 10
>> orthonormal vectors in confounds?
>
> Yes. Actually, what I am doing is calculating partial correlation between
> x and y conditionally to confounds, with the following code:
>
> def cond_partial_cor(y, x, confounds=[]):
> """ Returns the partial correlation of y and x, conditionning on
> confounds.
> """
> # First orthogonalise y and x relative to confounds
> if len(confounds):
> y = y - np.dot(confounds.T, linalg.lstsq(confounds.T, y)[0])
> x = x - np.dot(confounds.T, linalg.lstsq(confounds.T, x)[0])
> return np.dot(x, y)/sqrt(np.dot(y, y)*np.dot(x, x))
>
> I am not sure that what I am doing is optimal.
If confounds is orthonormal, then there is no need to use lstsq().
y = y - np.dot(np.dot(confounds, y), confounds)
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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