Comments (2)
Hi @hyjocean, sorry for the slow reply.
In relation to labels, I didn't mean any 'special', only that you won't need labels for certain tasks (e.g., clustering, unsupervised training, etc.).
You don't need the labels to generate the features (i.e., X_training_transform = transform(X_training, parameters)
), but you do need the labels when training the classifier (e.g., classifier.fit(X_training_transform, Y_training)
). If you don't have labels then you can't train the classifier in this way.
I hope that helps.
from minirocket.
Ahh, I get it.
Thanks for your patience reply~
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Related Issues (20)
- Extending Documentation of minirocket multivariate HOT 1
- Minimum length time series HOT 1
- Feature Size HOT 2
- Can't set random_state when doing a gridsearchCV HOT 3
- Are minirocket.py and minirocket_dv.py the implementation for the same Mini-Rocket algorithm? HOT 1
- is is necessary to normalize data when use minirocket multivariable version? HOT 1
- datatype HOT 13
- some question about multivarible version. HOT 3
- if the kernel numbers is much less than dataset sample numbers
- is the code handle the padding right?
- Port to R language
- Datatype Error HOT 2
- Need example to use variable ROCKET HOT 1
- Feature Transformation HOT 13
- pls help to understand how your this repo code related to sktime.transformers.series_as_features.rocket HOT 1
- Can't reproduce the accuracy of 'EOGHorizontalSignal' dataset in UCR109 based on original 'MINIROCKET' exmples HOT 7
- Accuracy problem HOT 3
- padding problem
- Example of CSV file reading HOT 5
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