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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/decomposition/tests/
H A Dtest_dict_learning.py49 n_components = 5
53 n_components = 1
60 n_components = 12
123 n_components = 5
141 n_components = 5
164 n_components = 5
180 n_components = 5
247 n_components = 4
263 n_components = 5
287 X, n_components=n_components, alpha=1, random_state=rng
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H A Dtest_online_lda.py42 n_components=n_components,
58 n_components=n_components,
77 n_components=n_components,
98 n_components=n_components,
200 n_components=n_components,
220 n_components=n_components,
242 n_components=n_components,
264 n_components=n_components,
271 n_components=n_components,
295 n_components=n_components,
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H A Dtest_pca.py22 pca = PCA(n_components=n_components, svd_solver=svd_solver)
42 n_components = 10
45 pca = PCA(n_components=n_components)
58 n_components = 30
78 n_components=n_components,
96 n_components=n_components, whiten=False, copy=copy, svd_solver=solver
398 pca = PCA(n_components=n_components, svd_solver="full")
400 assert pca.n_components == pytest.approx(n_components)
494 pca_auto = PCA(n_components=n_components, random_state=0)
496 n_components=n_components, svd_solver=expected_solver, random_state=0
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H A Dtest_incremental_pca.py22 pca = PCA(n_components=2)
34 for n_components in [1, 2, X.shape[1]]:
50 pca = PCA(n_components=2)
65 for n_components in [1, 2, X.shape[1]]:
126 for n_components in [-1, 0, 0.99, 4]:
139 n_components = 3
148 IncrementalPCA(n_components=n_components).partial_fit(X)
180 ipca.set_params(n_components=10)
184 ipca.set_params(n_components=15)
188 ipca.set_params(n_components=20)
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H A Dtest_sparse_pca.py21 U = rng.randn(n_samples, n_components)
22 V = rng.randn(n_components, n_features)
26 for k in range(n_components):
46 spca = SparsePCA(n_components=8, random_state=rng)
51 spca = SparsePCA(n_components=13, random_state=rng)
93 estimator = SparsePCA(n_components=8)
180 pca = PCA(n_components=2)
195 def test_spca_n_components_(SPCA, n_components): argument
200 model = SPCA(n_components=n_components).fit(X)
202 if n_components is not None:
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H A Dtest_factor_analysis.py23 n_samples, n_features, n_components = 20, 5, 3
26 W = rng.randn(n_components, n_features)
28 h = rng.randn(n_samples, n_components)
45 fa = FactorAnalysis(n_components=n_components, svd_method=method)
50 assert X_t.shape == (n_samples, n_components)
66 n_components=n_components, noise_variance_init=np.ones(n_features)
85 for n_components in [0, 2, X.shape[1]]:
86 fa.n_components = n_components
93 n_components = 2
97 fa_var = FactorAnalysis(n_components=n_components, rotation=method)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/decomposition/
H A D_pca.py348 n_components=None, argument
357 self.n_components = n_components
441 n_components = self.n_components
449 elif n_components >= 1 and n_components < 0.8 * min(X.shape):
469 if n_components == "mle":
480 elif n_components >= 1:
485 "was of type=%r" % (n_components, type(n_components))
505 if n_components == "mle":
514 n_components = np.searchsorted(ratio_cumsum, n_components, side="right") + 1
553 % (n_components, type(n_components))
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H A D_sparse_pca.py130 n_components=None, argument
143 self.n_components = n_components
178 if self.n_components is None:
179 n_components = X.shape[1]
181 n_components = self.n_components
186 n_components,
354 n_components=None, argument
368 n_components=n_components,
404 if self.n_components is None:
407 n_components = self.n_components
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H A D_fastica.py68 n_components = w_init.shape[0]
69 W = np.zeros((n_components, n_components), dtype=X.dtype)
73 for j in range(n_components):
152 n_components=None, argument
285 n_components=n_components,
435 n_components=None, argument
451 self.n_components = n_components
510 n_components = self.n_components
546 random_state.normal(size=(n_components, n_components)), dtype=X1.dtype
551 if w_init.shape != (n_components, n_components):
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/
H A Drandom_projection.py145 def _check_input_size(n_components, n_features):
147 if n_components <= 0:
187 _check_input_size(n_components, n_features)
190 loc=0.0, scale=1.0 / np.sqrt(n_components), size=(n_components, n_features)
270 for _ in range(n_components):
304 self.n_components = n_components
351 if self.n_components == "auto":
370 if self.n_components <= 0:
499 n_components=n_components,
652 n_components="auto",
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/mixture/tests/
H A Dtest_gaussian_mixture.py75 self.n_components = n_components
194 n_components=n_components,
880 n_components=n_components,
888 n_components=n_components,
907 n_components=n_components,
916 n_components=n_components,
971 n_components=n_components,
995 n_components=n_components,
1013 n_components=n_components,
1307 n_components=n_components,
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H A Dtest_bayesian_mixture.py315 n_components = rand_data.n_components
322 n_components=2 * n_components,
348 n_components = rand_data.n_components
354 n_components=2 * n_components,
369 n_components=2 * n_components,
384 n_components=2 * n_components,
401 n_components=2 * n_components,
456 n_components = 2 * rand_data.n_components
463 n_components=n_components,
471 n_components=n_components,
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/decomposition/
H A Dplot_faces_decomposition.py29 n_components = n_row * n_col
72 n_components=n_components, svd_solver="randomized", whiten=True
78 decomposition.NMF(n_components=n_components, init="nndsvda", tol=5e-3),
83 decomposition.FastICA(n_components=n_components, whiten=True),
89 n_components=n_components,
107 n_clusters=n_components,
117 decomposition.FactorAnalysis(n_components=n_components, max_iter=20),
132 print("Extracting the top %d %s..." % (n_components, name))
178 n_components=15,
190 n_components=15,
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/
H A Dtest_random_projection.py86 for n_components, n_features in inputs:
88 random_matrix(n_components, n_features)
93 for n_components, n_features in inputs:
95 n_components,
111 n_components, n_features = 5, 10
141 n_components = 100
151 n_components = 100
164 assert np.sqrt(s) / np.sqrt(n_components) in values
217 RandomProjection(n_components=n_components).fit(fit_data)
230 rp = RandomProjection(n_components="auto", eps=0.1)
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/dports/science/py-scipy/scipy-1.7.1/scipy/sparse/csgraph/tests/
H A Dtest_connected_components.py14 n_components, labels =\
18 assert_equal(n_components, 2)
32 n_components, labels =\
36 assert_equal(n_components, 3)
41 n_components, labels =\
57 n_components, labels =\
60 assert_equal(n_components, 5)
72 n_components, labels =\
75 assert_equal(n_components, 2)
89 assert_equal(n_components, 2)
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/dports/math/deal.ii/dealii-803d21ff957e349b3799cd3ef2c840bc78734305/include/deal.II/base/
H A Dfunction.templates.h43 , n_components(n_components) in Function()
48 Assert(n_components > 0, ExcZero()); in Function()
58 AssertDimension(n_components, f.n_components);
326 const unsigned int n_components) in ZeroFunction() argument
339 const unsigned int n_components) in ConstantFunction() argument
541 const unsigned int n_components) in ComponentSelectFunction() argument
551 const unsigned int n_components) in ComponentSelectFunction() argument
665 const unsigned int n_components) in VectorFunctionFromScalarFunctionObject() argument
804 const unsigned int n_components, in FunctionFromFunctionObjects() argument
807 , function_values(n_components) in FunctionFromFunctionObjects()
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/dports/graphics/gegl/gegl-0.4.34/operations/workshop/
H A Dintegral-image.c52 for (b = 0; b < n_components; b++)
65 src_row += n_components;
66 top_row += n_components * 2;
67 dst_row += n_components * 2;
80 src_row += n_components;
81 top_row += n_components;
82 dst_row += n_components;
93 gint n_components = 3;
104 n_components = 3;
110 n_components = 1;
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/dports/misc/orange3/orange3-3.29.1/Orange/projection/
H A Dpca.py83 return U[:, :n_components], s[:n_components], V[:n_components, :]
111 if self.n_components is None:
113 n_components = min(X.shape)
115 n_components = min(X.shape) - 1
117 n_components = self.n_components
140 return self._fit_full(X, n_components)
152 if isinstance(n_components, six.string_types):
155 (n_components, svd_solver)
167 "equal to 1, was of type=%r" % (n_components, type(n_components))
198 n_components=n_components,
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/dports/math/py-python-picard/python-picard-0.7/picard/tests/
H A Dtest_sklearn.py34 ica = Picard(n_components=1, whiten=False, random_state=0)
43 for whiten, n_components in [[True, 5], [False, None]]:
44 n_components_ = (n_components if n_components is not None else
47 ica = Picard(n_components=n_components, whiten=whiten, random_state=0)
52 ica = Picard(n_components=n_components, whiten=whiten, random_state=0)
72 for n_components in [n1, n2]:
73 n_components_ = (n_components if n_components is not None else
75 ica = Picard(n_components=n_components, random_state=rng,
86 if n_components == X.shape[1]:
/dports/science/py-nilearn/nilearn-0.8.1/nilearn/decomposition/tests/
H A Dtest_dict_learning.py19 dict_learning = DictLearning(n_components=4, random_state=0,
105 dict_learning = DictLearning(n_components=3,
114 n_components = 3
115 dict_learning = DictLearning(n_components=n_components, mask=mask_img)
119 check_shape = data[0].shape + (n_components,)
126 n_components = 3
127 dictlearn = DictLearning(n_components=n_components, mask=mask_img)
141 n_components = 3
142 dictlearn = DictLearning(n_components=n_components, mask=mask_img)
157 n_components = 10
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H A Dtest_canica.py122 canica = CanICA(n_components=4, random_state=rng,
183 canica = CanICA(mask=mask_img, n_components=3)
195 canica = CanICA(n_components=3,
204 n_components = 3
205 canica = CanICA(n_components=n_components, mask=mask_img)
216 n_components = 3
217 canica = CanICA(n_components=n_components, mask=mask_img)
231 n_components = 3
232 canica = CanICA(n_components=n_components, mask=mask_img)
247 n_components = 10
[all …]
/dports/math/py-python-picard/python-picard-0.7/picard/
H A Ddropin_sklearn.py95 def __init__(self, n_components=None, *, ortho=True, extended=None, argument
103 self.n_components = n_components
140 n_components = self.n_components
141 if not self.whiten and n_components is not None:
142 n_components = None
145 if n_components is None:
146 n_components = min(n_samples, n_features)
147 if (n_components > min(n_samples, n_features)):
148 n_components = min(n_samples, n_features)
151 % n_components
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H A Dsolver.py16 def picard(X, fun='tanh', n_components=None, ortho=True, extended=None, argument
157 if not whiten and n_components is not None:
160 n_components = None
162 if n_components is None:
163 n_components = min(n, p)
174 K = (u / d).T[:n_components]
178 covariance = np.eye(n_components) # For extended
186 w_init = np.asarray(random_state.normal(size=(n_components,
187 n_components)), dtype=X1.dtype)
192 if w_init.shape != (n_components, n_components):
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/
H A D_nca.py180 n_components=None, argument
190 self.n_components = n_components
334 if self.n_components is not None:
337 if self.n_components > X.shape[1]:
391 if self.n_components is not None:
393 if self.n_components != init.shape[0]:
439 n_components = self.n_components or n_features
444 elif n_components < min(n_features, n_samples):
449 transformation = np.eye(n_components, X.shape[1])
456 n_components=n_components, random_state=self.random_state_
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/mixture/
H A D_gaussian_mixture.py20 def _check_weights(weights, n_components): argument
55 def _check_means(means, n_components, n_features): argument
133 "diag": (n_components, n_features),
134 "spherical": (n_components,),
174 n_components, n_features = means.shape
176 for k in range(n_components):
417 n_components, _ = means.shape
634 n_components=1, argument
651 n_components=n_components,
691 self.n_components,
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