/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/tests/ |
H A D | test_affinity_propagation.py | 17 from sklearn.metrics import euclidean_distances 34 S = -euclidean_distances(X, squared=True) 70 S = -euclidean_distances(X, squared=True) 138 S = -euclidean_distances(X, squared=True) 194 S = -euclidean_distances(X, squared=True) 202 S = -euclidean_distances(X, squared=True)
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/dports/biology/py-scikit-bio/scikit-bio-0.5.6/skbio/stats/ordination/tests/ |
H A D | test_correspondence_analysis.py | 177 euclidean_distances = pdist(transformed_sites, 'euclidean') 178 npt.assert_almost_equal(chi2_distances, euclidean_distances) 186 euclidean_distances = pdist(transformed_species, 'euclidean') 187 npt.assert_almost_equal(chi2_distances, euclidean_distances)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/miscellaneous/ |
H A D | plot_johnson_lindenstrauss_bound.py | 25 from sklearn.metrics.pairwise import euclidean_distances 141 dists = euclidean_distances(data, squared=True).ravel() 160 projected_dists = euclidean_distances(projected_data, squared=True).ravel()[nonzero]
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/dports/science/py-segregation/segregation-2.1.0/segregation/util/ |
H A D | util.py | 11 from sklearn.metrics.pairwise import euclidean_distances 28 w = euclidean_distances(
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/tests/ |
H A D | test_pairwise.py | 67 S2 = euclidean_distances(X) 73 S2 = euclidean_distances(X, Y) 680 D = euclidean_distances(X, Y) 701 D1 = euclidean_distances(X, Y) 710 wrong_D = euclidean_distances( 729 D1 = euclidean_distances( 732 D2 = euclidean_distances( 738 D3 = euclidean_distances( 774 distances = euclidean_distances(X, Y) 796 distances = euclidean_distances(X) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/tests/ |
H A D | test_graph.py | 3 from sklearn.metrics import euclidean_distances 18 radius = np.percentile(euclidean_distances(X), 10)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/manifold/ |
H A D | _mds.py | 14 from ..metrics import euclidean_distances 102 dis = euclidean_distances(X) 527 self.dissimilarity_matrix_ = euclidean_distances(X)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/manifold/ |
H A D | plot_mds.py | 22 from sklearn.metrics import euclidean_distances 33 similarities = euclidean_distances(X_true)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/ |
H A D | pairwise.py | 226 def euclidean_distances( function 471 distances = euclidean_distances(X, Y, squared=True) 1175 K = euclidean_distances(X, Y, squared=True) 1378 "euclidean": euclidean_distances, 1380 "l2": euclidean_distances, 1440 if (X is Y or Y is None) and func is euclidean_distances: 1720 ) is euclidean_distances:
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H A D | __init__.py | 59 from .pairwise import euclidean_distances
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/dports/science/py-esda/esda-2.4.1/esda/ |
H A D | silhouettes.py | 30 metric=skp.euclidean_distances, 200 def boundary_silhouette(data, labels, W, metric=skp.euclidean_distances): 311 metric=skp.euclidean_distances): 405 metric=skp.euclidean_distances,
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/dports/math/py-spopt/spopt-0.2.1/spopt/tests/ |
H A D | test_skater.py | 102 sfkws = dict(dissimilarity=skm.euclidean_distances) 116 sfkws = dict(dissimilarity=skm.euclidean_distances)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/ |
H A D | test_random_projection.py | 8 from sklearn.metrics import euclidean_distances 244 original_distances = euclidean_distances(data, squared=True) 255 projected_distances = euclidean_distances(projected, squared=True)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/ |
H A D | _birch.py | 13 from ..metrics.pairwise import euclidean_distances 78 dist = euclidean_distances( 689 return euclidean_distances(X, self.subcluster_centers_)
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H A D | _affinity_propagation.py | 19 from ..metrics import euclidean_distances 459 self.affinity_matrix_ = -euclidean_distances(X, squared=True)
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H A D | _kmeans.py | 20 from ..metrics.pairwise import euclidean_distances 452 center_half_distances = euclidean_distances(centers) / 2 491 center_half_distances = euclidean_distances(centers_new) / 2 1305 return euclidean_distances(X, self.cluster_centers_)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/gaussian_process/tests/ |
H A D | test_kernels.py | 14 euclidean_distances, 297 K_absexp = np.exp(-euclidean_distances(X, X, squared=False))
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/dports/science/py-segregation/segregation-2.1.0/segregation/spatial/ |
H A D | spatial_indexes.py | 16 from sklearn.metrics.pairwise import manhattan_distances, euclidean_distances, haversine_distances 1178 dist = euclidean_distances( 1415 dist = euclidean_distances( 1649 dist = euclidean_distances( 1880 dist = euclidean_distances( 2098 dist = euclidean_distances(
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ |
H A D | multiclass.py | 47 from .metrics.pairwise import euclidean_distances 1099 pred = euclidean_distances(Y, self.code_book_).argmin(axis=1)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/developers/ |
H A D | develop.rst | 303 >>> from sklearn.metrics import euclidean_distances 329 ... closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/tests/ |
H A D | test_search.py | 67 from sklearn.metrics.pairwise import euclidean_distances 2233 X_precomputed = euclidean_distances(X)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/whats_new/ |
H A D | v0.21.rst | 138 - |Fix| Fixed a bug in :func:`metrics.pairwise.euclidean_distances` where a 792 - |Fix| The function :func:`metrics.pairwise.euclidean_distances`, and
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H A D | v0.17.rst | 281 - Added the ``X_norm_squared`` parameter to :func:`metrics.pairwise.euclidean_distances`
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H A D | older_versions.rst | 1070 :func:`metrics.euclidean_distances` and to
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/ |
H A D | classes.rst | 1092 metrics.pairwise.euclidean_distances
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