/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/decomposition/tests/ |
H A D | test_dict_learning.py | 49 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 [all …]
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H A D | test_online_lda.py | 42 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, [all …]
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H A D | test_pca.py | 22 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 [all …]
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H A D | test_incremental_pca.py | 22 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) [all …]
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H A D | test_sparse_pca.py | 21 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: [all …]
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H A D | test_factor_analysis.py | 23 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) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/decomposition/ |
H A D | _pca.py | 348 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)) [all …]
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H A D | _sparse_pca.py | 130 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 [all …]
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H A D | _fastica.py | 68 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): [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ |
H A D | random_projection.py | 145 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", [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/mixture/tests/ |
H A D | test_gaussian_mixture.py | 75 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, [all …]
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H A D | test_bayesian_mixture.py | 315 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, [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/decomposition/ |
H A D | plot_faces_decomposition.py | 29 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, [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/ |
H A D | test_random_projection.py | 86 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) [all …]
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/dports/science/py-scipy/scipy-1.7.1/scipy/sparse/csgraph/tests/ |
H A D | test_connected_components.py | 14 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) [all …]
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/dports/math/deal.ii/dealii-803d21ff957e349b3799cd3ef2c840bc78734305/include/deal.II/base/ |
H A D | function.templates.h | 43 , 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() [all …]
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/dports/graphics/gegl/gegl-0.4.34/operations/workshop/ |
H A D | integral-image.c | 52 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; [all …]
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/dports/misc/orange3/orange3-3.29.1/Orange/projection/ |
H A D | pca.py | 83 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, [all …]
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/dports/math/py-python-picard/python-picard-0.7/picard/tests/ |
H A D | test_sklearn.py | 34 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]:
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/dports/science/py-nilearn/nilearn-0.8.1/nilearn/decomposition/tests/ |
H A D | test_dict_learning.py | 19 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 [all …]
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H A D | test_canica.py | 122 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 …]
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/dports/math/py-python-picard/python-picard-0.7/picard/ |
H A D | dropin_sklearn.py | 95 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 [all …]
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H A D | solver.py | 16 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): [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/ |
H A D | _nca.py | 180 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_ [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/mixture/ |
H A D | _gaussian_mixture.py | 20 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, [all …]
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