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  1. doc/modules/manifold.rst

    Barnes-Hut t-SNE
    ----------------
    
    The Barnes-Hut t-SNE that has been implemented here is usually much slower than
    other manifold learning algorithms. The optimization is quite difficult
    and the computation of the gradient is :math:`O[d N log(N)]`, where :math:`d`
    is the number of output dimensions and :math:`N` is the number of samples. The
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  2. examples/neighbors/approximate_nearest_neighbors.py

                TSNE(metric='precomputed', perplexity=perplexity,
                     method="barnes_hut", random_state=42, n_iter=n_iter), )),
            ('TSNE with NMSlibTransformer', make_pipeline(
                NMSlibTransformer(n_neighbors=n_neighbors, metric='sqeuclidean'),
                TSNE(metric='precomputed', perplexity=perplexity,
                     method="barnes_hut", random_state=42, n_iter=n_iter), )),
    Python
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  3. asv_benchmarks/benchmarks/manifold.py

    from .datasets import _digits_dataset
    
    
    class TSNEBenchmark(Estimator, Benchmark):
        """
        Benchmarks for t-SNE.
        """
    
        param_names = ['method']
        params = (['exact', 'barnes_hut'],)
    
        def setup_cache(self):
            super().setup_cache()
    
        def make_data(self, params):
            method, = params
    
            n_samples = 500 if method == 'exact' else None
    
    Python
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  4. benchmarks/md/LICENSE

    MIT License
    
    Copyright (c) 2020 Tyler Barnes
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
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  5. benchmarks/mdx/LICENSE

    MIT License
    
    Copyright (c) 2020 Tyler Barnes
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
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  6. doc/whats_new/v0.19.rst

    - :class:`decomposition.NMF` now faster when ``beta_loss=0``.
      :issue:`9277` by :user:`hongkahjun`.
    
    - Memory improvements for method ``barnes_hut`` in :class:`manifold.TSNE`
      :issue:`7089` by :user:`Thomas Moreau <tomMoral>` and `Olivier Grisel`_.
    
    - Optimization schedule improvements for Barnes-Hut :class:`manifold.TSNE`
      so the results are closer to the one from the reference implementation
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  7. TODO/new-in-python-3.7.md

    * 启动器可以通过命令 “py -0” 运行,生成已安装 Python 的版本列表,*标有星号的是为默认*,运行 “py -0p” 将包含安装路径。如果 py 使用无法匹配的版本说明符运行,也会打印*缩略形式*的可用说明符列表。(由 Steve Barnes 参与贡献的 [bpo-30362](https://bugs.python.org/issue30362)。)
    
    ## 移除的内容
    
    ### 移除的API及特性
    
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  8. Discovery/Web-Content/raft-large-extensions-lowercase.txt

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  9. Discovery/Web-Content/raft-large-extensions.txt

    .aspx.designer.cs
    .aspx_files
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  10. doc/whats_new/v0.17.rst

      shuffling step in the ``cd`` solver.
      By `Tom Dupre la Tour`_ and `Mathieu Blondel`_.
    
    Enhancements
    ............
    - :class:`manifold.TSNE` now supports approximate optimization via the
      Barnes-Hut method, leading to much faster fitting. By Christopher Erick Moody.
      (:issue:`4025`)
    
    - :class:`cluster.mean_shift_.MeanShift` now supports parallel execution,
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