| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/tests/ |
| H A D | test_neighbors_pipeline.py | 32 n_neighbors = 5
52 n_neighbors = 5
103 n_neighbors = 10
113 Isomap(n_neighbors=n_neighbors, metric="precomputed"),
169 n_neighbors = 4
179 n_neighbors=n_neighbors,
185 n_neighbors=n_neighbors, novelty=False, contamination="auto"
206 n_neighbors=n_neighbors,
212 n_neighbors=n_neighbors, novelty=True, contamination="auto"
235 n_neighbors=int(n_neighbors * factor), mode="distance"
[all …]
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| H A D | test_graph.py | 10 n_neighbors = 5
23 nnt = KNeighborsTransformer(n_neighbors=n_neighbors, mode=mode)
26 assert Xt.data.shape == (n_samples_fit * (n_neighbors + add_one),)
32 assert X2t.data.shape == (n_queries * (n_neighbors + add_one),)
42 assert not Xt.data.shape == (n_samples_fit * (n_neighbors + add_one),)
48 assert not X2t.data.shape == (n_queries * (n_neighbors + add_one),)
62 n_neighbors = 5
68 nnt = KNeighborsTransformer(n_neighbors=n_neighbors)
71 assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
75 assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
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| H A D | test_neighbors.py | 87 n_neighbors=n_neighbors, algorithm=algorithm, p=p
403 n_neighbors=n_neighbors, weights=weights, algorithm=algorithm
423 knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors)
919 n_neighbors=n_neighbors, weights=weights, algorithm=algorithm
967 n_neighbors=n_neighbors, weights=weights, algorithm=algorithm
1078 knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors, algorithm="auto")
1082 n_neighbors=n_neighbors, metric="precomputed"
1330 n_neighbors=n_neighbors,
1766 n_neighbors = 12
1774 n_neighbors=int(n_neighbors * factor), mode="distance"
[all …]
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| H A D | test_lof.py | 38 clf = neighbors.LocalOutlierFactor(n_neighbors=5)
46 clf = neighbors.LocalOutlierFactor(contamination=0.25, n_neighbors=5).fit(X)
76 n_neighbors=2, contamination=0.1, novelty=True
78 clf2 = neighbors.LocalOutlierFactor(n_neighbors=2, novelty=True).fit(X_train)
101 lof_X = neighbors.LocalOutlierFactor(n_neighbors=3, novelty=True)
108 n_neighbors=3, algorithm="brute", metric="precomputed", novelty=True
120 clf = neighbors.LocalOutlierFactor(n_neighbors=500).fit(X)
123 clf = neighbors.LocalOutlierFactor(n_neighbors=500)
133 n_neighbors=2, contamination=0.1, novelty=True
135 clf2 = neighbors.LocalOutlierFactor(n_neighbors=2, novelty=True).fit(X_train)
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/neighbors/ |
| H A D | approximate_nearest_neighbors.py | 76 self.n_neighbors = n_neighbors
103 n_neighbors = self.n_neighbors + 1
109 indptr = np.arange(0, n_samples_transform * n_neighbors + 1, n_neighbors)
122 self.n_neighbors = n_neighbors
148 n_neighbors = self.n_neighbors + 1
166 indptr = np.arange(0, n_samples_transform * n_neighbors + 1, n_neighbors)
225 NMSlibTransformer(n_neighbors=n_neighbors, metric=metric),
230 n_neighbors=n_neighbors, mode="distance", metric=metric
236 AnnoyTransformer(n_neighbors=n_neighbors, metric=metric),
243 NMSlibTransformer(n_neighbors=n_neighbors, metric=metric),
[all …]
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| H A D | plot_nca_classification.py | 30 n_neighbors = 1 variable
55 ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
62 ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
89 plt.title("{} (k = {})".format(name, n_neighbors))
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/manifold/tests/ |
| H A D | test_isomap.py | 23 n_neighbors = Npts - 1
34 n_neighbors=n_neighbors,
51 n_neighbors = Npts - 1
70 n_neighbors=n_neighbors,
126 n_neighbors = 10
134 n_neighbors=n_neighbors, algorithm=algorithm, mode="distance"
136 manifold.Isomap(n_neighbors=n_neighbors, metric="precomputed"),
139 n_neighbors=n_neighbors, neighbors_algorithm=algorithm
182 model.set_params(n_neighbors=n_neighbors)
184 assert model.nbrs_.n_neighbors == n_neighbors
[all …]
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/manifold/ |
| H A D | _locally_linear.py | 113 indptr = np.arange(0, n_samples * n_neighbors + 1, n_neighbors)
196 n_neighbors, argument
332 nbrs, n_neighbors=n_neighbors, reg=reg, n_jobs=n_jobs
355 X, n_neighbors=n_neighbors + 1, return_distance=False
403 X, n_neighbors=n_neighbors + 1, return_distance=False
410 V = np.zeros((N, n_neighbors, n_neighbors))
505 X, n_neighbors=n_neighbors + 1, return_distance=False
694 self.n_neighbors = n_neighbors
709 n_neighbors=self.n_neighbors,
719 n_neighbors=self.n_neighbors,
[all …]
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| /dports/graphics/opendx/dx-4.4.4/src/exec/dxmods/ |
| H A D | _connectvor.c | 603 suspectedges[2].tri2 = n_neighbors.p; in ConnectVoronoiField()
611 suspectedges[3].tri2 = n_neighbors.r; in ConnectVoronoiField()
633 suspectedges[2].tri2 = n_neighbors.r; in ConnectVoronoiField()
642 suspectedges[3].tri2 = n_neighbors.p; in ConnectVoronoiField()
665 suspectedges[2].tri2 = n_neighbors.r; in ConnectVoronoiField()
673 suspectedges[3].tri2 = n_neighbors.q; in ConnectVoronoiField()
693 suspectedges[2].tri2 = n_neighbors.q; in ConnectVoronoiField()
701 suspectedges[3].tri2 = n_neighbors.r; in ConnectVoronoiField()
721 suspectedges[2].tri2 = n_neighbors.q; in ConnectVoronoiField()
729 suspectedges[3].tri2 = n_neighbors.p; in ConnectVoronoiField()
[all …]
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/ |
| H A D | _base.py | 254 if n_neighbors is not None and row_nnz_min < n_neighbors:
331 n_neighbors=None, argument
341 self.n_neighbors = n_neighbors
702 if n_neighbors is None:
703 n_neighbors = self.n_neighbors
704 elif n_neighbors <= 0:
723 n_neighbors += 1
736 X, n_neighbors=n_neighbors, return_distance=return_distance
742 n_neighbors=n_neighbors,
874 if n_neighbors is None:
[all …]
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| H A D | _graph.py | 40 n_neighbors, argument
115 n_neighbors=n_neighbors,
125 return X.kneighbors_graph(X=query, n_neighbors=n_neighbors, mode=mode)
352 n_neighbors=5, argument
361 n_neighbors=n_neighbors,
409 X, mode=self.mode, n_neighbors=self.n_neighbors + add_one
585 n_neighbors=None,
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| H A D | _lof.py | 197 n_neighbors=20, argument
209 n_neighbors=n_neighbors,
283 if self.n_neighbors > n_samples:
288 % (self.n_neighbors, n_samples)
290 self.n_neighbors_ = max(1, min(self.n_neighbors, n_samples - 1))
293 n_neighbors=self.n_neighbors_
462 X, n_neighbors=self.n_neighbors_
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/feature_selection/ |
| H A D | _mutual_info.py | 17 def _compute_mi_cc(x, y, n_neighbors): argument
54 nn = NearestNeighbors(metric="chebyshev", n_neighbors=n_neighbors)
72 + digamma(n_neighbors)
80 def _compute_mi_cd(c, d, n_neighbors): argument
123 k = min(n_neighbors, count - 1)
124 nn.set_params(n_neighbors=k)
153 def _compute_mi(x, y, x_discrete, y_discrete, n_neighbors=3): argument
162 return _compute_mi_cd(y, x, n_neighbors)
164 return _compute_mi_cd(x, y, n_neighbors)
166 return _compute_mi_cc(x, y, n_neighbors)
[all …]
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| /dports/graphics/gegl/gegl-0.4.34/operations/common-gpl3+/ |
| H A D | value-propagate.c | 128 gint n_neighbors = 0;
135 n_neighbors++;
142 n_neighbors++;
149 n_neighbors++;
152 return n_neighbors;
209 for (i = 0; i < n_neighbors; i++)
238 for (i = 0; i < n_neighbors; i++)
277 for (i = 0; i < n_neighbors; i++)
345 for (i = 0; i < n_neighbors; i++)
396 for (i = 0; i < n_neighbors; i++)
[all …]
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/manifold/ |
| H A D | plot_lle_digits.py | 27 n_neighbors = 30 variable
122 "Isomap embedding": Isomap(n_neighbors=n_neighbors, n_components=2),
124 n_neighbors=n_neighbors, n_components=2, method="standard"
127 n_neighbors=n_neighbors, n_components=2, method="modified"
130 n_neighbors=n_neighbors, n_components=2, method="hessian"
133 n_neighbors=n_neighbors, n_components=2, method="ltsa"
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| H A D | plot_compare_methods.py | 40 n_neighbors = 10 variable
46 "Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14
57 n_neighbors=n_neighbors,
67 methods["Isomap"] = manifold.Isomap(n_neighbors=n_neighbors, n_components=n_components)
70 n_components=n_components, n_neighbors=n_neighbors
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| H A D | plot_manifold_sphere.py | 46 n_neighbors = 10 variable
66 "Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14
83 n_neighbors=n_neighbors, n_components=2, method=method
101 manifold.Isomap(n_neighbors=n_neighbors, n_components=2)
131 se = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors)
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/impute/ |
| H A D | _knn.py | 121 n_neighbors=5, argument
128 self.n_neighbors = n_neighbors
133 def _calc_impute(self, dist_pot_donors, n_neighbors, fit_X_col, mask_fit_X_col): argument
158 donors_idx = np.argpartition(dist_pot_donors, n_neighbors - 1, axis=1)[
159 :, :n_neighbors
204 if self.n_neighbors <= 0:
206 "Expected n_neighbors > 0. Got {}".format(self.n_neighbors)
318 n_neighbors = min(self.n_neighbors, len(potential_donors_idx))
321 n_neighbors,
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| /dports/science/py-skrebate/skrebate-0.62/skrebate/ |
| H A D | relieff.py | 76 self.n_neighbors = n_neighbors
107 self.n_neighbors = int(self.n_neighbors * self._datalen * 0.5)
389 if match_count >= self.n_neighbors:
394 if miss_count >= self.n_neighbors:
399 if match_count >= self.n_neighbors and miss_count >= self.n_neighbors:
408 if match_count >= self.n_neighbors:
415 if miss_count[label] >= self.n_neighbors:
420 … if match_count >= self.n_neighbors and all(v >= self.n_neighbors for v in miss_count.values()):
428 if match_count >= self.n_neighbors:
433 if miss_count >= self.n_neighbors:
[all …]
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/impute/tests/ |
| H A D | test_knn.py | 14 def test_knn_imputer_shape(weights, n_neighbors): argument
22 imputer = KNNImputer(n_neighbors=n_neighbors, weights=weights)
73 KNNImputer(missing_values=na, n_neighbors=0).fit(X_fit)
115 knn = KNNImputer(missing_values=na, n_neighbors=2).fit(X)
247 imputer = KNNImputer(n_neighbors=1, missing_values=na)
258 n_neighbors = X.shape[0] - 1
259 imputer = KNNImputer(n_neighbors=n_neighbors, missing_values=na)
263 n_neighbors = X.shape[0]
264 imputer_plus1 = KNNImputer(n_neighbors=n_neighbors, missing_values=na)
462 imputer = KNNImputer(n_neighbors=2, metric=custom_callable)
[all …]
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| /dports/math/py-pynndescent/pynndescent-0.5.4/pynndescent/tests/ |
| H A D | test_pynndescent_.py | 61 sparse_nn_data, "euclidean", n_neighbors=20, random_state=None
239 n_neighbors = 10
244 n_neighbors,
261 n_neighbors = 10
266 n_neighbors,
284 proportion_correct = num_correct / (data.shape[0] * n_neighbors)
297 n_neighbors=4,
315 n_neighbors=4,
331 n_neighbors=4,
348 n_neighbors=4,
[all …]
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/semi_supervised/ |
| H A D | _label_propagation.py | 113 n_neighbors=7,
126 self.n_neighbors = n_neighbors
142 n_neighbors=self.n_neighbors, n_jobs=self.n_jobs
146 self.nn_fit._fit_X, self.n_neighbors, mode="connectivity"
425 n_neighbors=7,
433 n_neighbors=n_neighbors,
581 n_neighbors=7,
592 n_neighbors=n_neighbors,
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| /dports/math/py-pynndescent/pynndescent-0.5.4/pynndescent/ |
| H A D | sparse_nndescent.py | 106 def init_random(n_neighbors, inds, indptr, data, heap, dist, rng_state):
110 for j in range(n_neighbors - np.sum(heap[0][i] >= 0.0)):
181 n_neighbors,
223 if c <= delta * n_neighbors * n_vertices:
235 n_neighbors,
282 if c <= delta * n_neighbors * n_vertices:
293 n_neighbors,
309 current_graph = make_heap(n_samples, n_neighbors)
314 init_random(n_neighbors, inds, indptr, data, current_graph, dist, rng_state)
326 n_neighbors,
[all …]
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/utils/tests/ |
| H A D | test_graph.py | 13 graph = kneighbors_graph(X, n_neighbors=2, mode="distance")
27 graph = kneighbors_graph(X, n_neighbors=2, mode="distance")
50 graph = kneighbors_graph(X, n_neighbors=2, mode="distance")
62 graph = kneighbors_graph(X, n_neighbors=1, mode="connectivity")
73 graph = kneighbors_graph(X, n_neighbors=1, mode="distance")
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/feature_selection/tests/ |
| H A D | test_mutual_info.py | 52 for n_neighbors in [3, 5, 7]:
53 I_computed = _compute_mi(x, y, False, False, n_neighbors)
89 for n_neighbors in [3, 5, 7]:
90 I_computed = _compute_mi(x, y, True, False, n_neighbors)
152 mi = mutual_info_classif(X, y, discrete_features=[2], n_neighbors=3, random_state=0)
154 for n_neighbors in [5, 7, 9]:
156 X, y, discrete_features=[2], n_neighbors=n_neighbors, random_state=0
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