Re: [Scikit-learn-general] Dynamic Time Warping Contribution

I don't have any DTW code written, but I could definitely prototype how a lower bound callable might get incorporated into sklearn On Mon, Dec 7, 2015 at 5:28 PM, Stéfan van der Walt wrote: > On Mon, Dec 7, 2015 at 6:04 AM, Gael Varoquaux > wrote: > >> hello pandora's box ;) > >> I thought we d

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

On Mon, Dec 7, 2015 at 6:04 AM, Gael Varoquaux wrote: >> hello pandora's box ;) >> I thought we don't want to have time-series specific code? > > I agree. We should strive to make something like this pluggeable into > scikit-learn, but not have in inside. If you are seeking a home for DTW code, w

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

I would say prototype it and let's see what it implies on the code. A -- Go from Idea to Many App Stores Faster with Intel(R) XDK Give your users amazing mobile app experiences with Intel(R) XDK. Use one codebase in this

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

What about adding the option for users to pass in a callable "lower bound" function to a nearest neighbor search? Then users could use things like the LB Keogh lower bound On Mon, Dec 7, 2015 at 9:04 AM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > > hello pandora's box ;) > > I thoug

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

> hello pandora's box ;) > I thought we don't want to have time-series specific code? I agree. We should strive to make something like this pluggeable into scikit-learn, but not have in inside. -- Go from Idea to Many App

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

On 12/07/2015 04:33 AM, Alexandre Gramfort wrote: >> How do you plan to represent variable-length time series? Lists of 1d numpy >> arrays work but would be slow I guess. The ideal representation needs to be >> compatible with grid search and fast. > good point. I was thinking of forcing all time s

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

> How do you plan to represent variable-length time series? Lists of 1d numpy > arrays work but would be slow I guess. The ideal representation needs to be > compatible with grid search and fast. good point. I was thinking of forcing all time series to have the same length. or use dtype = object l

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

How do you plan to represent variable-length time series? Lists of 1d numpy arrays work but would be slow I guess. The ideal representation needs to be compatible with grid search and fast. Mathieu On Mon, Dec 7, 2015 at 10:35 AM, Dan Shiebler wrote: > Hello, > > I’m not sure if this is the cor

Re: [Scikit-learn-general] Dynamic Time Warping Contribution

> In addition, users > cannot take advantage of the LB Keogh lower bound of dynamic time warping, > which can dramatically speed up the nearest neighbors search. can you give more details on how you would use this in our NN code? if passing a dtw callable is not good enough, that can justify an a

Re: [Scikit-learn-general] Dynamic Time Warping

On Wed, Jan 21, 2015 at 6:58 PM, Mark Regan wrote: > Hi Diego. Not sure if it'll be useful but I coded up a DTW classifier with > the same sklearn style fit/predict methods here. We have an ancient pull request lying around for scikit-image: https://github.com/scikit-image/scikit-image/pull/518

Re: [Scikit-learn-general] Dynamic Time Warping

Hi Diego. Not sure if it'll be useful but I coded up a DTW classifier with the same sklearn style fit/predict methods here . Mark On Tue Jan 20 2015 at 4:26:40 AM Andy wrote: > Hi Diego.

Re: [Scikit-learn-general] Dynamic Time Warping

Hi Diego. I think the conclusion of that discussion still holds. We don't do any time-series specific stuff in scikit-learn. I think it would be good to ask over at pandas what they think about it. Otherwise, just publish your code on github, I'm sure people will find it useful. We'd be happy t

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

Gael, Thanks for let me know. Didier -Original Message- From: Gael Varoquaux [mailto:gael.varoqu...@normalesup.org] Sent: 30 October 2012 10:31 To: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) On Tue, Oct 30, 2012

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

On Tue, Oct 30, 2012 at 10:08:19AM -, Didier Vila wrote: > So, I will be an active reader/tester/ user of the implementation of the DTW > once everything is in scikit learn. I am not sure that DTW is in the scope of scikit-learn right now: the scikit still has no good way of dealing with time

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

t: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) A very good and efficient generalization of DTW is the Global Alignment Kernel of Marco Cuturi: http://www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/GA.html I did a small cython wrapper if you want to try it out: http://www.iip.ist.i.

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

A very good and efficient generalization of DTW is the Global Alignment Kernel of Marco Cuturi: http://www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/GA.html I did a small cython wrapper if you want to try it out: http://www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/Code/TGA_python_wrapper_v1.0.tar.gz C

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

On Mon, Oct 29, 2012 at 11:10 AM, Alexandre Gramfort wrote: > DTW in O(n^2) is super easy to implement. A good exercise with cython ! > Some implementations exist in O(n) although they give only good > approximations. Also see: https://github.com/scikit-image/scikit-image/blob/master/skimage/gr

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

kit-learn-general@lists.sourceforge.net > Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) > > hi, > > DTW in O(n^2) is super easy to implement. A good exercise with cython ! > Some implementations exist in O(n) although they give only good > approximatio

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

From: Olivier Grisel [mailto:olivier.gri...@ensta.org] > Sent: 29 October 2012 14:08 > To: scikit-learn-general@lists.sourceforge.net > Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) > > 2012/10/29 Didier Vila : >> Good Afternoon all, >> >>

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

- From: Olivier Grisel [mailto:olivier.gri...@ensta.org] Sent: 29 October 2012 14:08 To: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) 2012/10/29 Didier Vila : > Good Afternoon all, > > Does Scikit learn have the dyn

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

<mailto:mbruna...@capquestco.com> From: Leon Palafox [mailto:leonoe...@gmail.com] Sent: 29 October 2012 16:22 To: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) Didier Try using the DTW library in R with a wrapper to python

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

n-general@lists.sourceforge.net > Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) > > 2012/10/29 Didier Vila : > > Good Afternoon all, > > > > Does Scikit learn have the dynamic time warping measure embedded in the > > framework ? > > No. >

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW) 2012/10/29 Didier Vila : > Good Afternoon all, > > Does Scikit learn have the dynamic time warping measure embedded in the > framework ? No. > If yes, can I use it

Re: [Scikit-learn-general] Dynamic Time Warping measure (DTW)

2012/10/29 Didier Vila : > Good Afternoon all, > > Does Scikit learn have the dynamic time warping measure embedded in the > framework ? No. > If yes, can I use it with clustering/ svm tools ? > > If not, do you have any other alternatives / suggestions ? According to the wikipedia article: http