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Compressive sensing: tomography reconstruction ...
l / 2.0 ) ** 2 + ( y - l / 2.0 ) ** 2 < ( l / 2.0 ) ** 2 mask...np . float64 ) center = l_x / 2.0 X += 0.5 - center Y += 0.5 -...scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html -
inplace_csr_row_normalize_l1 — scikit-learn 1.7...
2 , 3 , 4 ]) >>> indices = np . array ([ 0 , 1 , 2 , 3 ])...>>> data = np . array ([ 1.0 , 2.0 , 3.0 , 4.0 ]) >>> X = csr_matrix...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz... -
label_ranking_average_precision_score — scikit-...
2 , 0.1 ]]) >>> label_ranking_a...scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html -
t-SNE: The effect of various perplexity values ...
2 sec S-curve, perplexity=50 in...n_samples = 150 n_components = 2 ( fig , subplots ) = plt . subplots...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
RepeatedKFold — scikit-learn 1.7.2 documentation
2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...RepeatedKFold ( n_splits = 2 , n_repeats = 2 , random_state = 2652124...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
Illustration of Gaussian process classification...
2 ) Y = np . logical_xor ( X [:,...DotProduct ( sigma_0 = 1.0 ) ** 2 ] for i , kernel in enumerate...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html -
Fess インストールガイド
ステップ 1: OpenSearch のインストール ステップ 2: Fess のインストール ステップ 3: 起動 Windows...1: Docker Compose ファイルの取得 ステップ 2: Docker Compose ファイルの確認 ステップ 3:...fess.codelibs.org/ja/15.3/install/index.html -
prism-toolbar.css
2); box-shadow: 0 2px 0 0 rgba(0,0,0,0.2); border-radius:...fess.codelibs.org/zh-cn/_static/assets/vendor/prismjs/plugins/toolbar/prism-toolbar.css -
Fess - Enterprise Search Server
2.0 is released. 2025-07-20 Fess...released. 2025-05-24 Fess 14.19.2 is released. More news... Commercial...fess.codelibs.org/ -
RandomTreesEmbedding — scikit-learn 1.7.2 docum...
min_samples_split = 2 , min_samples_leaf = 1 , min_...nodes is at most n_estimators * 2 ** max_depth . For an example...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html