On Thu, Nov 12, 2009 at 17:38, Alexey Tigarev <alexey.tiga...@gmail.com> wrote:
> Hi All!
>
> I have implemented multiple regression in a following way:
>
> def multipleRegression(x, y):
> """ Perform linear regression using least squares method.
>
> X - matrix containing inputs for observations,
> y - vector containing one of outputs for every observation """
> mulregLogger.debug("multipleRegression(x=%s, y=%s)" % (x, y))
> xt = transpose(x)
> a = dot(xt, x) # A = xt * x
> b = dot(xt, y) # B = xt * y
> try:
> return linalg.solve(a, b)
Never, ever use the normal equations. :-)
Use linalg.lstsq(x, y) instead.
> except linalg.LinAlgError, lae:
> mulregLogger.warn("Singular matrix:\n%s" % (a))
> mulregLogger.warn(lae)
> mulregLogger.warn("Determinant: %f" % (linalg.det(a)))
> raise lae
>
> Can you suggest me something to optimize it?
>
> I am using it on large number of observations so it is common to have
> "x" matrix of about 5000x20 and "y" vector of length 5000, and more.
> I also have to run that multiple times for different "y" vectors and
> same "x" matrix.
Just make a matrix "y" such that each column vector is a different
output vector (e.g. y.shape == (5000, number_of_different_y_vectors))
--
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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