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+/tslearn-0.5.3.2.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-tslearn
+Version:	0.5.3.2
+Release:	1
+Summary:	A machine learning toolkit dedicated to time-series data
+License:	BSD-2-Clause
+URL:		http://tslearn.readthedocs.io/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/88/16/7cc705033e285af1468846c3e8e3ba70546e301c8fcb29c29ef22d66460f/tslearn-0.5.3.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-numba
+Requires:	python3-joblib
+Requires:	python3-pytest
+
+%description
+<!-- Our title -->
+<div align="center">
+  <h3>tslearn </h3>
+</div>
+
+<!-- Short description -->
+<p align="center">
+   The machine learning toolkit for time series analysis in Python
+</p>
+
+<!-- The badges -->
+<p align="center">
+    <a href="https://badge.fury.io/py/tslearn">
+        <img alt="PyPI" src="https://badge.fury.io/py/tslearn.svg">
+    </a>
+    <a href="http://tslearn.readthedocs.io/en/stable/?badge=stable">
+        <img alt="Documentation" src="https://readthedocs.org/projects/tslearn/badge/?version=stable">
+    </a>
+    <a href="https://dev.azure.com/romaintavenard/tslearn/_build">
+        <img alt="Build (Azure Pipelines)" src="https://dev.azure.com/romaintavenard/tslearn/_apis/build/status/tslearn-team.tslearn?branchName=main">
+    </a>
+    <a href="https://codecov.io/gh/tslearn-team/tslearn">
+        <img alt="Codecov" src="https://codecov.io/gh/tslearn-team/tslearn/branch/main/graph/badge.svg">
+    </a>
+    <a href="https://pepy.tech/project/tslearn">
+        <img alt="Downloads" src="https://pepy.tech/badge/tslearn">
+    </a>
+</p>
+
+<!-- Draw horizontal rule -->
+<hr>
+
+<!-- Table of content -->
+
+| Section | Description |
+|-|-|
+| [Installation](#installation) | Installing the dependencies and tslearn |
+| [Getting started](#getting-started) | A quick introduction on how to use tslearn |
+| [Available features](#available-features) | An extensive overview of tslearn's functionalities |
+| [Documentation](#documentation) | A link to our API reference and a gallery of examples |
+| [Contributing](#contributing) | A guide for heroes willing to contribute |
+| [Citation](#referencing-tslearn) | A citation for tslearn for scholarly articles |
+
+## Installation
+There are different alternatives to install tslearn:
+* PyPi: `python -m pip install tslearn`
+* Conda: `conda install -c conda-forge tslearn`
+* Git: `python -m pip install https://github.com/tslearn-team/tslearn/archive/main.zip`
+
+In order for the installation to be successful, the required dependencies must be installed. For a more detailed guide on how to install tslearn, please see the [Documentation](https://tslearn.readthedocs.io/en/stable/?badge=stable#installation).
+
+## Getting started
+
+### 1. Getting the data in the right format
+tslearn expects a time series dataset to be formatted as a 3D `numpy` array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (`n_ts, max_sz, d`). In order to get the data in the right format, different solutions exist:
+* [You can use the utility functions such as `to_time_series_dataset`.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils)
+* [You can convert from other popular time series toolkits in Python.](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)
+* [You can load any of the UCR datasets in the required format.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets)
+* [You can generate synthetic data using the `generators` module.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators)
+
+It should further be noted that tslearn [supports variable-length timeseries](https://tslearn.readthedocs.io/en/stable/variablelength.html).
+
+```python3
+>>> from tslearn.utils import to_time_series_dataset
+>>> my_first_time_series = [1, 3, 4, 2]
+>>> my_second_time_series = [1, 2, 4, 2]
+>>> my_third_time_series = [1, 2, 4, 2, 2]
+>>> X = to_time_series_dataset([my_first_time_series,
+                                my_second_time_series,
+                                my_third_time_series])
+>>> y = [0, 1, 1]
+```
+
+### 2. Data preprocessing and transformations
+Optionally, tslearn has several utilities to preprocess the data. In order to facilitate the convergence of different algorithms, you can [scale time series](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing). Alternatively, in order to speed up training times, one can [resample](https://tslearn.readthedocs.io/en/stable/gen_modules/preprocessing/tslearn.preprocessing.TimeSeriesResampler.html#tslearn.preprocessing.TimeSeriesResampler) the data or apply a [piece-wise transformation](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise).
+
+```python3
+>>> from tslearn.preprocessing import TimeSeriesScalerMinMax
+>>> X_scaled = TimeSeriesScalerMinMax().fit_transform(X)
+>>> print(X_scaled)
+[[[0.] [0.667] [1.] [0.333] [nan]]
+ [[0.] [0.333] [1.] [0.333] [nan]]
+ [[0.] [0.333] [1.] [0.333] [0.333]]]
+```
+
+### 3. Training a model
+
+After getting the data in the right format, a model can be trained. Depending on the use case, tslearn supports different tasks: classification, clustering and regression. For an extensive overview of possibilities, check out our [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html).
+
+```python3
+>>> from tslearn.neighbors import KNeighborsTimeSeriesClassifier
+>>> knn = KNeighborsTimeSeriesClassifier(n_neighbors=1)
+>>> knn.fit(X_scaled, y)
+>>> print(knn.predict(X_scaled))
+[0 1 1]
+```
+
+As can be seen, the models in tslearn follow the same API as those of the well-known scikit-learn. Moreover, they are fully compatible with it, allowing to use different scikit-learn utilities such as [hyper-parameter tuning and pipelines](https://tslearn.readthedocs.io/en/stable/auto_examples/plot_knnts_sklearn.html#sphx-glr-auto-examples-plot-knnts-sklearn-py).
+
+### 4. More analyses
+
+tslearn further allows to perform all different types of analysis. Examples include [calculating barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters) of a group of time series or calculate the distances between time series using a [variety of distance metrics](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.metrics.html#module-tslearn.metrics).
+
+## Available features
+
+| data                                                                                                                                                                                         | processing                                                                                                              | clustering                                                                                                                                                       | classification                                                                                                                                                                          | regression                                                                                                                                                                           | metrics                                                                                                                              |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
+| [UCR Datasets](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets)                                                                           | [Scaling](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing) | [TimeSeriesKMeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.TimeSeriesKMeans.html#tslearn.clustering.TimeSeriesKMeans) | [KNN Classifier](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesClassifier.html#tslearn.neighbors.KNeighborsTimeSeriesClassifier) | [KNN Regressor](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesRegressor.html#tslearn.neighbors.KNeighborsTimeSeriesRegressor) | [Dynamic Time Warping](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.dtw.html#tslearn.metrics.dtw)    |
+| [Generators](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators)                                                                         | [Piecewise](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise)       | [KShape](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KShape.html#tslearn.clustering.KShape)                               | [TimeSeriesSVC](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVC.html#tslearn.svm.TimeSeriesSVC)                                                      | [TimeSeriesSVR](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVR.html#tslearn.svm.TimeSeriesSVR)                                                   | [Global Alignment Kernel](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.gak.html#tslearn.metrics.gak) |
+| Conversion([1](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils), [2](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)) |                                                                                                                         | [KernelKmeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KernelKMeans.html#tslearn.clustering.KernelKMeans)             | [LearningShapelets](https://tslearn.readthedocs.io/en/stable/gen_modules/shapelets/tslearn.shapelets.LearningShapelets.html)                                    | [MLP](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.neural_network.html#module-tslearn.neural_network)                                                                | [Barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters)              |
+|                                                                                                                                                                                              |                                                                                                                         |                                                                                                                                                                  | [Early Classification](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.early_classification.html#module-tslearn.early_classification)                                      |                                                                                                                                                                                      | [Matrix Profile](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.matrix_profile.html#module-tslearn.matrix_profile)     |
+
+
+## Documentation
+
+The documentation is hosted at [readthedocs](http://tslearn.readthedocs.io/en/stable/index.html). It includes an [API](https://tslearn.readthedocs.io/en/stable/reference.html), [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html) and a [user guide](https://tslearn.readthedocs.io/en/stable/user_guide/userguide.html).
+
+## Contributing
+
+If you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md). A list of interesting TODO's can be found [here](https://github.com/tslearn-team/tslearn/issues?utf8=✓&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20). **If you want other ML methods for time series to be added to this TODO list, do not hesitate to [open an issue](https://github.com/tslearn-team/tslearn/issues/new/choose)!**
+
+## Referencing tslearn
+
+If you use `tslearn` in a scientific publication, we would appreciate citations:
+
+```bibtex
+@article{JMLR:v21:20-091,
+  author  = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and 
+             Felix Divo and Guillaume Androz and Chester Holtz and 
+             Marie Payne and Roman Yurchak and Marc Ru{\ss}wurm and 
+             Kushal Kolar and Eli Woods},
+  title   = {Tslearn, A Machine Learning Toolkit for Time Series Data},
+  journal = {Journal of Machine Learning Research},
+  year    = {2020},
+  volume  = {21},
+  number  = {118},
+  pages   = {1-6},
+  url     = {http://jmlr.org/papers/v21/20-091.html}
+}
+```
+
+#### Acknowledgments
+Authors would like to thank Mathieu Blondel for providing code for [Kernel k-means](https://gist.github.com/mblondel/6230787) and [Soft-DTW](https://github.com/mblondel/soft-dtw).
+
+
+%package -n python3-tslearn
+Summary:	A machine learning toolkit dedicated to time-series data
+Provides:	python-tslearn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tslearn
+<!-- Our title -->
+<div align="center">
+  <h3>tslearn </h3>
+</div>
+
+<!-- Short description -->
+<p align="center">
+   The machine learning toolkit for time series analysis in Python
+</p>
+
+<!-- The badges -->
+<p align="center">
+    <a href="https://badge.fury.io/py/tslearn">
+        <img alt="PyPI" src="https://badge.fury.io/py/tslearn.svg">
+    </a>
+    <a href="http://tslearn.readthedocs.io/en/stable/?badge=stable">
+        <img alt="Documentation" src="https://readthedocs.org/projects/tslearn/badge/?version=stable">
+    </a>
+    <a href="https://dev.azure.com/romaintavenard/tslearn/_build">
+        <img alt="Build (Azure Pipelines)" src="https://dev.azure.com/romaintavenard/tslearn/_apis/build/status/tslearn-team.tslearn?branchName=main">
+    </a>
+    <a href="https://codecov.io/gh/tslearn-team/tslearn">
+        <img alt="Codecov" src="https://codecov.io/gh/tslearn-team/tslearn/branch/main/graph/badge.svg">
+    </a>
+    <a href="https://pepy.tech/project/tslearn">
+        <img alt="Downloads" src="https://pepy.tech/badge/tslearn">
+    </a>
+</p>
+
+<!-- Draw horizontal rule -->
+<hr>
+
+<!-- Table of content -->
+
+| Section | Description |
+|-|-|
+| [Installation](#installation) | Installing the dependencies and tslearn |
+| [Getting started](#getting-started) | A quick introduction on how to use tslearn |
+| [Available features](#available-features) | An extensive overview of tslearn's functionalities |
+| [Documentation](#documentation) | A link to our API reference and a gallery of examples |
+| [Contributing](#contributing) | A guide for heroes willing to contribute |
+| [Citation](#referencing-tslearn) | A citation for tslearn for scholarly articles |
+
+## Installation
+There are different alternatives to install tslearn:
+* PyPi: `python -m pip install tslearn`
+* Conda: `conda install -c conda-forge tslearn`
+* Git: `python -m pip install https://github.com/tslearn-team/tslearn/archive/main.zip`
+
+In order for the installation to be successful, the required dependencies must be installed. For a more detailed guide on how to install tslearn, please see the [Documentation](https://tslearn.readthedocs.io/en/stable/?badge=stable#installation).
+
+## Getting started
+
+### 1. Getting the data in the right format
+tslearn expects a time series dataset to be formatted as a 3D `numpy` array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (`n_ts, max_sz, d`). In order to get the data in the right format, different solutions exist:
+* [You can use the utility functions such as `to_time_series_dataset`.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils)
+* [You can convert from other popular time series toolkits in Python.](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)
+* [You can load any of the UCR datasets in the required format.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets)
+* [You can generate synthetic data using the `generators` module.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators)
+
+It should further be noted that tslearn [supports variable-length timeseries](https://tslearn.readthedocs.io/en/stable/variablelength.html).
+
+```python3
+>>> from tslearn.utils import to_time_series_dataset
+>>> my_first_time_series = [1, 3, 4, 2]
+>>> my_second_time_series = [1, 2, 4, 2]
+>>> my_third_time_series = [1, 2, 4, 2, 2]
+>>> X = to_time_series_dataset([my_first_time_series,
+                                my_second_time_series,
+                                my_third_time_series])
+>>> y = [0, 1, 1]
+```
+
+### 2. Data preprocessing and transformations
+Optionally, tslearn has several utilities to preprocess the data. In order to facilitate the convergence of different algorithms, you can [scale time series](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing). Alternatively, in order to speed up training times, one can [resample](https://tslearn.readthedocs.io/en/stable/gen_modules/preprocessing/tslearn.preprocessing.TimeSeriesResampler.html#tslearn.preprocessing.TimeSeriesResampler) the data or apply a [piece-wise transformation](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise).
+
+```python3
+>>> from tslearn.preprocessing import TimeSeriesScalerMinMax
+>>> X_scaled = TimeSeriesScalerMinMax().fit_transform(X)
+>>> print(X_scaled)
+[[[0.] [0.667] [1.] [0.333] [nan]]
+ [[0.] [0.333] [1.] [0.333] [nan]]
+ [[0.] [0.333] [1.] [0.333] [0.333]]]
+```
+
+### 3. Training a model
+
+After getting the data in the right format, a model can be trained. Depending on the use case, tslearn supports different tasks: classification, clustering and regression. For an extensive overview of possibilities, check out our [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html).
+
+```python3
+>>> from tslearn.neighbors import KNeighborsTimeSeriesClassifier
+>>> knn = KNeighborsTimeSeriesClassifier(n_neighbors=1)
+>>> knn.fit(X_scaled, y)
+>>> print(knn.predict(X_scaled))
+[0 1 1]
+```
+
+As can be seen, the models in tslearn follow the same API as those of the well-known scikit-learn. Moreover, they are fully compatible with it, allowing to use different scikit-learn utilities such as [hyper-parameter tuning and pipelines](https://tslearn.readthedocs.io/en/stable/auto_examples/plot_knnts_sklearn.html#sphx-glr-auto-examples-plot-knnts-sklearn-py).
+
+### 4. More analyses
+
+tslearn further allows to perform all different types of analysis. Examples include [calculating barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters) of a group of time series or calculate the distances between time series using a [variety of distance metrics](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.metrics.html#module-tslearn.metrics).
+
+## Available features
+
+| data                                                                                                                                                                                         | processing                                                                                                              | clustering                                                                                                                                                       | classification                                                                                                                                                                          | regression                                                                                                                                                                           | metrics                                                                                                                              |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
+| [UCR Datasets](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets)                                                                           | [Scaling](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing) | [TimeSeriesKMeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.TimeSeriesKMeans.html#tslearn.clustering.TimeSeriesKMeans) | [KNN Classifier](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesClassifier.html#tslearn.neighbors.KNeighborsTimeSeriesClassifier) | [KNN Regressor](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesRegressor.html#tslearn.neighbors.KNeighborsTimeSeriesRegressor) | [Dynamic Time Warping](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.dtw.html#tslearn.metrics.dtw)    |
+| [Generators](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators)                                                                         | [Piecewise](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise)       | [KShape](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KShape.html#tslearn.clustering.KShape)                               | [TimeSeriesSVC](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVC.html#tslearn.svm.TimeSeriesSVC)                                                      | [TimeSeriesSVR](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVR.html#tslearn.svm.TimeSeriesSVR)                                                   | [Global Alignment Kernel](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.gak.html#tslearn.metrics.gak) |
+| Conversion([1](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils), [2](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)) |                                                                                                                         | [KernelKmeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KernelKMeans.html#tslearn.clustering.KernelKMeans)             | [LearningShapelets](https://tslearn.readthedocs.io/en/stable/gen_modules/shapelets/tslearn.shapelets.LearningShapelets.html)                                    | [MLP](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.neural_network.html#module-tslearn.neural_network)                                                                | [Barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters)              |
+|                                                                                                                                                                                              |                                                                                                                         |                                                                                                                                                                  | [Early Classification](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.early_classification.html#module-tslearn.early_classification)                                      |                                                                                                                                                                                      | [Matrix Profile](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.matrix_profile.html#module-tslearn.matrix_profile)     |
+
+
+## Documentation
+
+The documentation is hosted at [readthedocs](http://tslearn.readthedocs.io/en/stable/index.html). It includes an [API](https://tslearn.readthedocs.io/en/stable/reference.html), [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html) and a [user guide](https://tslearn.readthedocs.io/en/stable/user_guide/userguide.html).
+
+## Contributing
+
+If you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md). A list of interesting TODO's can be found [here](https://github.com/tslearn-team/tslearn/issues?utf8=✓&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20). **If you want other ML methods for time series to be added to this TODO list, do not hesitate to [open an issue](https://github.com/tslearn-team/tslearn/issues/new/choose)!**
+
+## Referencing tslearn
+
+If you use `tslearn` in a scientific publication, we would appreciate citations:
+
+```bibtex
+@article{JMLR:v21:20-091,
+  author  = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and 
+             Felix Divo and Guillaume Androz and Chester Holtz and 
+             Marie Payne and Roman Yurchak and Marc Ru{\ss}wurm and 
+             Kushal Kolar and Eli Woods},
+  title   = {Tslearn, A Machine Learning Toolkit for Time Series Data},
+  journal = {Journal of Machine Learning Research},
+  year    = {2020},
+  volume  = {21},
+  number  = {118},
+  pages   = {1-6},
+  url     = {http://jmlr.org/papers/v21/20-091.html}
+}
+```
+
+#### Acknowledgments
+Authors would like to thank Mathieu Blondel for providing code for [Kernel k-means](https://gist.github.com/mblondel/6230787) and [Soft-DTW](https://github.com/mblondel/soft-dtw).
+
+
+%package help
+Summary:	Development documents and examples for tslearn
+Provides:	python3-tslearn-doc
+%description help
+<!-- Our title -->
+<div align="center">
+  <h3>tslearn </h3>
+</div>
+
+<!-- Short description -->
+<p align="center">
+   The machine learning toolkit for time series analysis in Python
+</p>
+
+<!-- The badges -->
+<p align="center">
+    <a href="https://badge.fury.io/py/tslearn">
+        <img alt="PyPI" src="https://badge.fury.io/py/tslearn.svg">
+    </a>
+    <a href="http://tslearn.readthedocs.io/en/stable/?badge=stable">
+        <img alt="Documentation" src="https://readthedocs.org/projects/tslearn/badge/?version=stable">
+    </a>
+    <a href="https://dev.azure.com/romaintavenard/tslearn/_build">
+        <img alt="Build (Azure Pipelines)" src="https://dev.azure.com/romaintavenard/tslearn/_apis/build/status/tslearn-team.tslearn?branchName=main">
+    </a>
+    <a href="https://codecov.io/gh/tslearn-team/tslearn">
+        <img alt="Codecov" src="https://codecov.io/gh/tslearn-team/tslearn/branch/main/graph/badge.svg">
+    </a>
+    <a href="https://pepy.tech/project/tslearn">
+        <img alt="Downloads" src="https://pepy.tech/badge/tslearn">
+    </a>
+</p>
+
+<!-- Draw horizontal rule -->
+<hr>
+
+<!-- Table of content -->
+
+| Section | Description |
+|-|-|
+| [Installation](#installation) | Installing the dependencies and tslearn |
+| [Getting started](#getting-started) | A quick introduction on how to use tslearn |
+| [Available features](#available-features) | An extensive overview of tslearn's functionalities |
+| [Documentation](#documentation) | A link to our API reference and a gallery of examples |
+| [Contributing](#contributing) | A guide for heroes willing to contribute |
+| [Citation](#referencing-tslearn) | A citation for tslearn for scholarly articles |
+
+## Installation
+There are different alternatives to install tslearn:
+* PyPi: `python -m pip install tslearn`
+* Conda: `conda install -c conda-forge tslearn`
+* Git: `python -m pip install https://github.com/tslearn-team/tslearn/archive/main.zip`
+
+In order for the installation to be successful, the required dependencies must be installed. For a more detailed guide on how to install tslearn, please see the [Documentation](https://tslearn.readthedocs.io/en/stable/?badge=stable#installation).
+
+## Getting started
+
+### 1. Getting the data in the right format
+tslearn expects a time series dataset to be formatted as a 3D `numpy` array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (`n_ts, max_sz, d`). In order to get the data in the right format, different solutions exist:
+* [You can use the utility functions such as `to_time_series_dataset`.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils)
+* [You can convert from other popular time series toolkits in Python.](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)
+* [You can load any of the UCR datasets in the required format.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets)
+* [You can generate synthetic data using the `generators` module.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators)
+
+It should further be noted that tslearn [supports variable-length timeseries](https://tslearn.readthedocs.io/en/stable/variablelength.html).
+
+```python3
+>>> from tslearn.utils import to_time_series_dataset
+>>> my_first_time_series = [1, 3, 4, 2]
+>>> my_second_time_series = [1, 2, 4, 2]
+>>> my_third_time_series = [1, 2, 4, 2, 2]
+>>> X = to_time_series_dataset([my_first_time_series,
+                                my_second_time_series,
+                                my_third_time_series])
+>>> y = [0, 1, 1]
+```
+
+### 2. Data preprocessing and transformations
+Optionally, tslearn has several utilities to preprocess the data. In order to facilitate the convergence of different algorithms, you can [scale time series](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing). Alternatively, in order to speed up training times, one can [resample](https://tslearn.readthedocs.io/en/stable/gen_modules/preprocessing/tslearn.preprocessing.TimeSeriesResampler.html#tslearn.preprocessing.TimeSeriesResampler) the data or apply a [piece-wise transformation](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise).
+
+```python3
+>>> from tslearn.preprocessing import TimeSeriesScalerMinMax
+>>> X_scaled = TimeSeriesScalerMinMax().fit_transform(X)
+>>> print(X_scaled)
+[[[0.] [0.667] [1.] [0.333] [nan]]
+ [[0.] [0.333] [1.] [0.333] [nan]]
+ [[0.] [0.333] [1.] [0.333] [0.333]]]
+```
+
+### 3. Training a model
+
+After getting the data in the right format, a model can be trained. Depending on the use case, tslearn supports different tasks: classification, clustering and regression. For an extensive overview of possibilities, check out our [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html).
+
+```python3
+>>> from tslearn.neighbors import KNeighborsTimeSeriesClassifier
+>>> knn = KNeighborsTimeSeriesClassifier(n_neighbors=1)
+>>> knn.fit(X_scaled, y)
+>>> print(knn.predict(X_scaled))
+[0 1 1]
+```
+
+As can be seen, the models in tslearn follow the same API as those of the well-known scikit-learn. Moreover, they are fully compatible with it, allowing to use different scikit-learn utilities such as [hyper-parameter tuning and pipelines](https://tslearn.readthedocs.io/en/stable/auto_examples/plot_knnts_sklearn.html#sphx-glr-auto-examples-plot-knnts-sklearn-py).
+
+### 4. More analyses
+
+tslearn further allows to perform all different types of analysis. Examples include [calculating barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters) of a group of time series or calculate the distances between time series using a [variety of distance metrics](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.metrics.html#module-tslearn.metrics).
+
+## Available features
+
+| data                                                                                                                                                                                         | processing                                                                                                              | clustering                                                                                                                                                       | classification                                                                                                                                                                          | regression                                                                                                                                                                           | metrics                                                                                                                              |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
+| [UCR Datasets](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets)                                                                           | [Scaling](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing) | [TimeSeriesKMeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.TimeSeriesKMeans.html#tslearn.clustering.TimeSeriesKMeans) | [KNN Classifier](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesClassifier.html#tslearn.neighbors.KNeighborsTimeSeriesClassifier) | [KNN Regressor](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesRegressor.html#tslearn.neighbors.KNeighborsTimeSeriesRegressor) | [Dynamic Time Warping](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.dtw.html#tslearn.metrics.dtw)    |
+| [Generators](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators)                                                                         | [Piecewise](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise)       | [KShape](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KShape.html#tslearn.clustering.KShape)                               | [TimeSeriesSVC](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVC.html#tslearn.svm.TimeSeriesSVC)                                                      | [TimeSeriesSVR](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVR.html#tslearn.svm.TimeSeriesSVR)                                                   | [Global Alignment Kernel](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.gak.html#tslearn.metrics.gak) |
+| Conversion([1](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils), [2](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)) |                                                                                                                         | [KernelKmeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KernelKMeans.html#tslearn.clustering.KernelKMeans)             | [LearningShapelets](https://tslearn.readthedocs.io/en/stable/gen_modules/shapelets/tslearn.shapelets.LearningShapelets.html)                                    | [MLP](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.neural_network.html#module-tslearn.neural_network)                                                                | [Barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters)              |
+|                                                                                                                                                                                              |                                                                                                                         |                                                                                                                                                                  | [Early Classification](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.early_classification.html#module-tslearn.early_classification)                                      |                                                                                                                                                                                      | [Matrix Profile](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.matrix_profile.html#module-tslearn.matrix_profile)     |
+
+
+## Documentation
+
+The documentation is hosted at [readthedocs](http://tslearn.readthedocs.io/en/stable/index.html). It includes an [API](https://tslearn.readthedocs.io/en/stable/reference.html), [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html) and a [user guide](https://tslearn.readthedocs.io/en/stable/user_guide/userguide.html).
+
+## Contributing
+
+If you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md). A list of interesting TODO's can be found [here](https://github.com/tslearn-team/tslearn/issues?utf8=✓&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20). **If you want other ML methods for time series to be added to this TODO list, do not hesitate to [open an issue](https://github.com/tslearn-team/tslearn/issues/new/choose)!**
+
+## Referencing tslearn
+
+If you use `tslearn` in a scientific publication, we would appreciate citations:
+
+```bibtex
+@article{JMLR:v21:20-091,
+  author  = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and 
+             Felix Divo and Guillaume Androz and Chester Holtz and 
+             Marie Payne and Roman Yurchak and Marc Ru{\ss}wurm and 
+             Kushal Kolar and Eli Woods},
+  title   = {Tslearn, A Machine Learning Toolkit for Time Series Data},
+  journal = {Journal of Machine Learning Research},
+  year    = {2020},
+  volume  = {21},
+  number  = {118},
+  pages   = {1-6},
+  url     = {http://jmlr.org/papers/v21/20-091.html}
+}
+```
+
+#### Acknowledgments
+Authors would like to thank Mathieu Blondel for providing code for [Kernel k-means](https://gist.github.com/mblondel/6230787) and [Soft-DTW](https://github.com/mblondel/soft-dtw).
+
+
+%prep
+%autosetup -n tslearn-0.5.3.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-tslearn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.3.2-1
+- Package Spec generated
diff --git a/sources b/sources
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+0e754b1b7290bc6701b02108fcb37a29  tslearn-0.5.3.2.tar.gz