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
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
I would say prototype it and let's see what it implies on the code.
A
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Use one codebase in this
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
> 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.
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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
> 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
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
> 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
Hello,
I’m not sure if this is the correct place to send this. If it is not, could
you please direct me to the best place? Thank you.
I’d like to add a dynamic time warping metric to
sklearn.neighbors.DistanceMetric.
Dynamic time warping is one of the most used distance metrics for time
series, a
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