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
On Wed, Dec 18, 2013 at 3:23 PM, Alan G Isaac alan.is...@gmail.com 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
On Wed, Dec 18, 2013 at 3:38 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Wed, Dec 18, 2013 at 3:23 PM, Alan G Isaac alan.is...@gmail.comwrote:
For teaching it is certainly nice to have
numpy.polynomial.polynomial.polyfit
providing modern (vs. traditional) parameter order,
On Thu, Mar 7, 2013 at 9:22 AM, eat e.antero.ta...@gmail.com 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
On Tue, Mar 5, 2013 at 5:23 AM, Charles R Harris
charlesr.har...@gmail.comwrote:
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
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, 2013 at 8:37 PM,
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 Legendre,
On Tue, Mar 5, 2013 at 6:23 AM, Charles R Harris
charlesr.har...@gmail.comwrote:
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
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
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
On Mon, Mar 4, 2013 at 4:53 PM, Aron Ahmadia a...@ahmadia.net 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
On Mon, Mar 4, 2013 at 5:53 PM, Aron Ahmadia a...@ahmadia.net 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
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 8:37 PM, Charles R Harris
charlesr.har...@gmail.comwrote:
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
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 Wed, Feb 27, 2013 at 6:46 AM, David Pine djp...@gmail.com 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
Please post inline so we have the context.
On Wed, Feb 27, 2013 at 9:40 AM, David Pine djp...@gmail.com 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
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
On Wed, Feb 27, 2013 at 3:01 PM, David Pine djp...@gmail.com 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
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]
On Mon, Apr 13, 2009 at 5:59 PM, Mathew Yeates myea...@jpl.nasa.gov 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,],
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
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.
Any
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 1:47 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
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
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:
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.
Polyfit is
28 matches
Mail list logo
