Comments (2)
It looks easy but I can't find implementations in python in nice libraries. There is one in R. See http://stackoverflow.com/a/5766202 for how this can be called from python. I don't know yet what this means in terms of added dependencies. I'll check.
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Well maybe it's so easy in Python that nobody even talks about it. It's as simple as:
import dtw
dtw.dtw(x,y,f)
where x and y are numpy arrays and f is a distance function like 'lambda x,y : abs(x-y)'
This of course adds the dtw package to our dependencies.
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Related Issues (20)
- fix metrics problem in to tutorial HOT 1
- add cron builds
- fix test_find_best_architecture_with_class_weights
- Update build workflow
- Prepare some regression dataset
- Write a tutorial on regression functionality
- Create a plan for regression implementation HOT 1
- Not all training data in memory at the same time
- advertise that we can do regression in all our docs
- create new release (christmas edition) HOT 1
- explain in the docs how we automatically detect regression/classification data
- implement regression functionality in code
- Adapt defaults for evaluation metrics HOT 1
- find_best_architecture fails with tf.keras.metrics objects
- create sphinx build action for documentation build testing
- update supported python versions in tutorial docs and CI workflow HOT 2
- throw sensible error when using a datagenerator and subset!=None
- Add Transformer architecture
- Early stopping a bit aggressive for low number of epochs
- We should shuffle the data by default
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