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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/tests/
H A Dtest_affinity_propagation.py17 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)
/dports/biology/py-scikit-bio/scikit-bio-0.5.6/skbio/stats/ordination/tests/
H A Dtest_correspondence_analysis.py177 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)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/miscellaneous/
H A Dplot_johnson_lindenstrauss_bound.py25 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]
/dports/science/py-segregation/segregation-2.1.0/segregation/util/
H A Dutil.py11 from sklearn.metrics.pairwise import euclidean_distances
28 w = euclidean_distances(
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/tests/
H A Dtest_pairwise.py67 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 …]
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/tests/
H A Dtest_graph.py3 from sklearn.metrics import euclidean_distances
18 radius = np.percentile(euclidean_distances(X), 10)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/manifold/
H A D_mds.py14 from ..metrics import euclidean_distances
102 dis = euclidean_distances(X)
527 self.dissimilarity_matrix_ = euclidean_distances(X)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/manifold/
H A Dplot_mds.py22 from sklearn.metrics import euclidean_distances
33 similarities = euclidean_distances(X_true)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/
H A Dpairwise.py226 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:
H A D__init__.py59 from .pairwise import euclidean_distances
/dports/science/py-esda/esda-2.4.1/esda/
H A Dsilhouettes.py30 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,
/dports/math/py-spopt/spopt-0.2.1/spopt/tests/
H A Dtest_skater.py102 sfkws = dict(dissimilarity=skm.euclidean_distances)
116 sfkws = dict(dissimilarity=skm.euclidean_distances)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/
H A Dtest_random_projection.py8 from sklearn.metrics import euclidean_distances
244 original_distances = euclidean_distances(data, squared=True)
255 projected_distances = euclidean_distances(projected, squared=True)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/
H A D_birch.py13 from ..metrics.pairwise import euclidean_distances
78 dist = euclidean_distances(
689 return euclidean_distances(X, self.subcluster_centers_)
H A D_affinity_propagation.py19 from ..metrics import euclidean_distances
459 self.affinity_matrix_ = -euclidean_distances(X, squared=True)
H A D_kmeans.py20 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_)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/gaussian_process/tests/
H A Dtest_kernels.py14 euclidean_distances,
297 K_absexp = np.exp(-euclidean_distances(X, X, squared=False))
/dports/science/py-segregation/segregation-2.1.0/segregation/spatial/
H A Dspatial_indexes.py16 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(
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/
H A Dmulticlass.py47 from .metrics.pairwise import euclidean_distances
1099 pred = euclidean_distances(Y, self.code_book_).argmin(axis=1)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/developers/
H A Ddevelop.rst303 >>> from sklearn.metrics import euclidean_distances
329 ... closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/tests/
H A Dtest_search.py67 from sklearn.metrics.pairwise import euclidean_distances
2233 X_precomputed = euclidean_distances(X)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/whats_new/
H A Dv0.21.rst138 - |Fix| Fixed a bug in :func:`metrics.pairwise.euclidean_distances` where a
792 - |Fix| The function :func:`metrics.pairwise.euclidean_distances`, and
H A Dv0.17.rst281 - Added the ``X_norm_squared`` parameter to :func:`metrics.pairwise.euclidean_distances`
H A Dolder_versions.rst1070 :func:`metrics.euclidean_distances` and to
/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/
H A Dclasses.rst1092 metrics.pairwise.euclidean_distances

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