On 10/12/06, Charles R Harris <[EMAIL PROTECTED]> wrote:
Polyfit seems overly conservative in its choice of rcond.
In [101]: lin.lstsq(v,y,1e-10)[0]
Out[101]:
array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01,
3.05631361e-02])
In [107]: polyfit(x,y,3)
Out[108]:
array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01,
3.05631361e-02])
Compare
In [112]: lin.lstsq(v,y,1e-12)[0]
Out[112]:
array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00,
6.51889107e+03])
In [113]: dot(lin.inv(vtv),dot(v.T,y))
Out[113]:
array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00,
6.51889107e+03])
So the default needs to be changed somewhere. Probably polyfit shoud accept rcond as a keyword. Where the actual problem lies is a bit obscure as the normal rcond default for lin.lstsq
is 1e-12. Maybe some sort of import error somewhere down the line.
Chuck
On 10/12/06, Greg Willden < [EMAIL PROTECTED]> wrote:On 10/12/06, Charles R Harris <[EMAIL PROTECTED] > wrote:I'm guessing that the rcond number in the lstsq version (default 1e-10) is the difference. Generally the lstsq version should work better than the MPL version because at*a is not as well conditioned and vandermonde matrices are notoriously ill conditioned anyway for higher degree fits. It would help if you attached the data files in ascii form unless they happen to contain thousands of data items. Just the x will suffice, zip it up if you have to.
Here are the files.
Since the two algorithms behave differently and each has it place then can both be included in numpy?
i.e. numpy.polyfit(x,y,N, mode='alg1')
numpy.polyfit (x,y,N, mode='alg2')
replacing alg1 and alg2 with meaningful names.
The polyfit function looks seriously busted. If I do the fits by hand I get the same results using the (not so hot) MPL version or lstsq. I don't know what the problem is. The docstring is also incorrect for the method. Hmmm...
Polyfit seems overly conservative in its choice of rcond.
In [101]: lin.lstsq(v,y,1e-10)[0]
Out[101]:
array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01,
3.05631361e-02])
In [107]: polyfit(x,y,3)
Out[108]:
array([ 5.84304475e-07, -5.51513630e-03, 1.51465472e+01,
3.05631361e-02])
Compare
In [112]: lin.lstsq(v,y,1e-12)[0]
Out[112]:
array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00,
6.51889107e+03])
In [113]: dot(lin.inv(vtv),dot(v.T,y))
Out[113]:
array([ -5.42970700e-07, 1.61425067e-03, 1.99260667e+00,
6.51889107e+03])
Chuck
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