On Wed, Dec 18, 2013 at 3:38 PM, Charles R Harris wrote:
>
>
>
> On Wed, Dec 18, 2013 at 3:23 PM, Alan G Isaac wrote:
>
>> For teaching it is certainly nice to have
>> numpy.polynomial.polynomial.polyfit
>> providing modern (vs. traditional) parameter order, but
>>
>> - it is rather buried
>> - n
On Wed, Dec 18, 2013 at 3:23 PM, Alan G Isaac wrote:
> For teaching it is certainly nice to have
> numpy.polynomial.polynomial.polyfit
> providing modern (vs. traditional) parameter order, but
>
> - it is rather buried
> - np.polyfit uses traditional order and has the same name
>
> I recall there
For teaching it is certainly nice to have numpy.polynomial.polynomial.polyfit
providing modern (vs. traditional) parameter order, but
- it is rather buried
- np.polyfit uses traditional order and has the same name
I recall there was some controversy (?) over all of this,
but might it not be appro
On Thu, Mar 7, 2013 at 9:22 AM, eat wrote:
> Hi,
>
> On Thu, Mar 7, 2013 at 1:52 AM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> On Tue, Mar 5, 2013 at 5:23 AM, Charles R Harris <
>> charlesr.har...@gmail.com> wrote:
>>
>>>
>>>
>>> On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernánde
On Wed, Mar 6, 2013 at 4:52 PM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Tue, Mar 5, 2013 at 5:23 AM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>>
>>
>> On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernández del Río <
>> jaime.f...@gmail.com> wrote:
>>
>>> On Mon, Mar 4
On Tue, Mar 5, 2013 at 5:23 AM, Charles R Harris
wrote:
>
>
> On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris <
>> charlesr.har...@gmail.com> wrote:
>>
>>>
>>> There are actually seven versions of polyn
Jaime,
If you are going to work on this, you should also take a look at the recent
thread
http://mail.scipy.org/pipermail/numpy-discussion/2013-February/065649.html,
which is about the weighting function, which is in a confused state in the
current version of polyfit. By the way, Numerical Recipe
On Tue, Mar 5, 2013 at 6:23 AM, Charles R Harris
wrote:
>
>
> On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris <
>> charlesr.har...@gmail.com> wrote:
>>
>>>
>>> There are actually seven versions of polyn
On Tue, Mar 5, 2013 at 12:41 AM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>>
>> There are actually seven versions of polynomial fit, two for the usual
>> polynomial basis, and one each for Lege
On Mon, Mar 4, 2013 at 8:37 PM, Charles R Harris
wrote:
>
> There are actually seven versions of polynomial fit, two for the usual
> polynomial basis, and one each for Legendre, Chebyshev, Hermite, Hermite_e,
> and Laguerre ;)
>
Correct me if I am wrong, but the fitted function is the same regard
On Mon, Mar 4, 2013 at 5:23 PM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> A couple of days back, answering a question in StackExchange (
> http://stackoverflow.com/a/15196628/110026), I found myself using
> Lagrange multipliers to fit a polynomial with least squares to data, making
On Mon, Mar 4, 2013 at 5:53 PM, Aron Ahmadia wrote:
> Interesting, that question would probably have gotten a different response
> on scicomp, it is a pity we are not attracting more questions there!
>
> I know there are two polyfit modules in numpy, one in numpy.polyfit, the
> other in numpy.pol
On Mon, Mar 4, 2013 at 4:53 PM, Aron Ahmadia wrote:
> Interesting, that question would probably have gotten a different response
> on scicomp, it is a pity we are not attracting more questions there!
>
> I know there are two polyfit modules in numpy, one in numpy.polyfit, the
> other in numpy.pol
Interesting, that question would probably have gotten a different response
on scicomp, it is a pity we are not attracting more questions there!
I know there are two polyfit modules in numpy, one in numpy.polyfit, the
other in numpy.polynomial, the functionality you are suggesting seems to
fit in e
A couple of days back, answering a question in StackExchange (
http://stackoverflow.com/a/15196628/110026), I found myself using Lagrange
multipliers to fit a polynomial with least squares to data, making sure it
went through some fixed points. This time it was relatively easy, because
some 5 years
On Wed, Feb 27, 2013 at 3:01 PM, David Pine wrote:
> Pauli, Josef, Chuck,
>
> I read over the discussion on curve_fit. I believe I now understand what
> people are trying to do when they write about scaling the weighting and/or
> covariance matrix. And I agree that what polyfit does in its curre
Pauli, Josef, Chuck,
I read over the discussion on curve_fit. I believe I now understand what
people are trying to do when they write about scaling the weighting and/or
covariance matrix. And I agree that what polyfit does in its current form
is estimate the absolute errors in the data from the
27.02.2013 16:40, David Pine kirjoitti:
[clip]
> 2. I am sorry but I don't understand your response. The matrix Vbase
> in the code is already the covariance matrix, _before_ it is scaled by
> fac. Scaling it by fac and returning Vbase*fac as the covariance
> matrix is wrong, at least according
Please post inline so we have the context.
On Wed, Feb 27, 2013 at 9:40 AM, David Pine wrote:
> Chuck,
>
> Thanks for the quick reply.
>
> 1. I see your point about zero weights but the code in its current form
> doesn't take into account zero weights in counting the degrees of freedom,
> as you
Chuck,
Thanks for the quick reply.
1. I see your point about zero weights but the code in its current form
doesn't take into account zero weights in counting the degrees of freedom,
as you point out, so it seems to me like a moot point. More importantly,
the documentation doesn't explain what t
On Wed, Feb 27, 2013 at 6:46 AM, David Pine wrote:
> As of NumPy v1.7, numpy.polyfit includes an option for providing weighting
> to data to be fit. It's a welcome addition, but the implementation seems a
> bit non-standard, perhaps even wrong, and I wonder if someone can enlighten
> me.
>
> 1.
As of NumPy v1.7, numpy.polyfit includes an option for providing weighting
to data to be fit. It's a welcome addition, but the implementation seems a
bit non-standard, perhaps even wrong, and I wonder if someone can enlighten
me.
1. The documentation does not specify what the weighting array "w"
On Mon, Apr 13, 2009 at 5:59 PM, Mathew Yeates wrote:
> Hi,
> I understand how to fit the points (x1,y1) (x2,y2),(x3,y3) with a line
> using polyfit. But, what if I want to perform this task on every row of
> an array?
> For instance
>
> [[x1,x2,x3],
> [s1,s2,s3]]
>
> [[y1,y2,y3,],
> [r1,r2,r3]
Hi,
I understand how to fit the points (x1,y1) (x2,y2),(x3,y3) with a line
using polyfit. But, what if I want to perform this task on every row of
an array?
For instance
[[x1,x2,x3],
[s1,s2,s3]]
[[y1,y2,y3,],
[r1,r2,r3]]
and I want the results to be the coefficients [a,b,c] and [d,e,f] wh
2008/6/16 Chandler Latour <[EMAIL PROTECTED]>:
> I believe I'm bound to python.
> In terms of forcing the regression through the origin, the purpose is partly
> for visualization but it also should fit the data. It would not make sense
> to model the data with an initial value other than 0.
Polyf
I believe I'm bound to python.
In terms of forcing the regression through the origin, the purpose is
partly for visualization but it also should fit the data. It would
not make sense to model the data with an initial value other than 0.
On Jun 16, 2008, at 4:33 PM, Simon Palmer wrote:
At
At the risk of uttering a heresy, are you bound to Python for this? I bet
you could find a C library that will work well, plus it is not a hard
algorithm to code yourself. I am pretty sure I have used a numerical
recipes algorithm for regression in my distant past.
Also I can't help thinking the
On Mon, Jun 16, 2008 at 1:47 PM, Chandler Latour <[EMAIL PROTECTED]>
wrote:
> Yes, exactly what I meant.
>
Polyfit just fits polynomials, there is no way of fixing the constant to
zero. Your best bet is to use linalg.lstsq directly to fit the function you
want.
Chuck
Yes, exactly what I meant.
On Jun 16, 2008, at 2:39 PM, Charles R Harris wrote:
On Mon, Jun 16, 2008 at 1:30 PM, Chandler Latour
<[EMAIL PROTECTED]> wrote:
Hello,
I'm new to the whole numpy scene, but I've been wanting to run a
regression on some data. I belive that polyfit is the way to
On Mon, Jun 16, 2008 at 1:30 PM, Chandler Latour <[EMAIL PROTECTED]>
wrote:
> Hello,
>
> I'm new to the whole numpy scene, but I've been wanting to run a
> regression on some data. I belive that polyfit is the way to go, but
> I was wondering if there exists a way to force the intercept to be 0.
Hello,
I'm new to the whole numpy scene, but I've been wanting to run a
regression on some data. I belive that polyfit is the way to go, but
I was wondering if there exists a way to force the intercept to be 0.
Any help would be much appreciated.
Thanks
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