/dports/science/py-cirq-core/Cirq-0.13.1/cirq-core/cirq/testing/ |
H A D | lin_alg_utils.py | 40 random_state = value.parse_random_state(random_state) 42 state_vector = random_state.randn(dim).astype(complex) 43 state_vector += 1j * random_state.randn(dim) 64 random_state = value.parse_random_state(random_state) 66 mat = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 87 random_state = value.parse_random_state(random_state) 89 z = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 113 random_state = value.parse_random_state(random_state) 115 m = random_state.randn(dim, dim) 135 r = random_unitary(dim, random_state=random_state) [all …]
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/dports/science/py-cirq-aqt/Cirq-0.12.0/cirq-core/cirq/testing/ |
H A D | lin_alg_utils.py | 40 random_state = value.parse_random_state(random_state) 42 state_vector = random_state.randn(dim).astype(complex) 43 state_vector += 1j * random_state.randn(dim) 64 random_state = value.parse_random_state(random_state) 66 mat = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 87 random_state = value.parse_random_state(random_state) 89 z = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 113 random_state = value.parse_random_state(random_state) 115 m = random_state.randn(dim, dim) 135 r = random_unitary(dim, random_state=random_state) [all …]
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/dports/science/py-cirq-pasqal/Cirq-0.13.1/cirq-core/cirq/testing/ |
H A D | lin_alg_utils.py | 40 random_state = value.parse_random_state(random_state) 42 state_vector = random_state.randn(dim).astype(complex) 43 state_vector += 1j * random_state.randn(dim) 64 random_state = value.parse_random_state(random_state) 66 mat = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 87 random_state = value.parse_random_state(random_state) 89 z = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 113 random_state = value.parse_random_state(random_state) 115 m = random_state.randn(dim, dim) 135 r = random_unitary(dim, random_state=random_state) [all …]
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/dports/science/py-cirq-google/Cirq-0.13.0/cirq-core/cirq/testing/ |
H A D | lin_alg_utils.py | 40 random_state = value.parse_random_state(random_state) 42 state_vector = random_state.randn(dim).astype(complex) 43 state_vector += 1j * random_state.randn(dim) 64 random_state = value.parse_random_state(random_state) 66 mat = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 87 random_state = value.parse_random_state(random_state) 89 z = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 113 random_state = value.parse_random_state(random_state) 115 m = random_state.randn(dim, dim) 135 r = random_unitary(dim, random_state=random_state) [all …]
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/dports/science/py-cirq-ionq/Cirq-0.13.1/cirq-core/cirq/testing/ |
H A D | lin_alg_utils.py | 40 random_state = value.parse_random_state(random_state) 42 state_vector = random_state.randn(dim).astype(complex) 43 state_vector += 1j * random_state.randn(dim) 64 random_state = value.parse_random_state(random_state) 66 mat = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 87 random_state = value.parse_random_state(random_state) 89 z = random_state.randn(dim, dim) + 1j * random_state.randn(dim, dim) 113 random_state = value.parse_random_state(random_state) 115 m = random_state.randn(dim, dim) 135 r = random_unitary(dim, random_state=random_state) [all …]
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/dports/science/py-nilearn/nilearn-0.8.1/nilearn/mass_univariate/tests/ |
H A D | test_permuted_least_squares.py | 286 n_perm=0, random_state=random_state) 292 n_perm=0, random_state=random_state) 299 n_perm=0, random_state=random_state) 366 n_perm=0, random_state=random_state) 374 n_perm=0, random_state=random_state) 409 n_perm=0, random_state=random_state) 416 n_perm=0, random_state=random_state) 495 random_state=random_state) 531 random_state=random_state) 562 random_state=random_state) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/tests/ |
H A D | test_base.py | 32 random_state = np.random.RandomState(3) 33 ensemble._make_estimator(random_state=random_state) 34 ensemble._make_estimator(random_state=random_state) 41 assert ensemble[0].random_state is None 44 assert ensemble[1].random_state != ensemble[2].random_state 78 clf1 = Perceptron(random_state=None) 79 assert clf1.random_state is None 82 assert isinstance(clf1.random_state, int) 86 assert isinstance(clf1.random_state, int) 87 clf2 = Perceptron(random_state=None) [all …]
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H A D | test_bagging.py | 129 random_state=1, 137 random_state=1, 255 random_state=rng, 642 n_estimators=10, random_state=random_state, warm_start=False 728 random_state=1, 742 random_state=1, 811 random_state=1, 820 random_state = 5 826 BaggingClassifier(oob_score=True, random_state=random_state) 831 BaggingClassifier(oob_score=True, random_state=random_state) [all …]
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H A D | test_gradient_boosting.py | 172 random_state=0, 185 random_state=0, 206 random_state=1, 269 X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state) 279 X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state) 365 random_state=1, 389 random_state=0, 1173 random_state=42, 1181 random_state=42, 1220 random_state=42, [all …]
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/dports/finance/py-quantecon/quantecon-0.5.2/quantecon/game_theory/ |
H A D | random.py | 13 def random_game(nums_actions, random_state=None): argument 38 random_state = check_random_state(random_state) 40 Player(random_state.random_sample(nums_actions[i:]+nums_actions[:i])) 47 def covariance_game(nums_actions, rho, random_state=None): argument 92 random_state = check_random_state(random_state) 94 random_state.multivariate_normal(mean, cov, nums_actions) 99 def random_pure_actions(nums_actions, random_state=None): argument 120 random_state = check_random_state(random_state) 127 def random_mixed_actions(nums_actions, random_state=None): argument 148 random_state = check_random_state(random_state) [all …]
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/dports/finance/py-quantecon/quantecon-0.5.2/quantecon/markov/ |
H A D | random.py | 58 random_state=random_state) 64 random_state=None): argument 102 random_state=random_state) 107 random_state=None): argument 119 probvecs = probvec(m, k, random_state=random_state) 132 n, k, num_trials=m, random_state=random_state 147 random_state=None): argument 197 random_state = check_random_state(random_state) 198 R = scale * random_state.randn(L) 201 random_state=random_state) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/manifold/tests/ |
H A D | test_t_sne.py | 297 X, _ = make_blobs(n_features=3, random_state=random_state) 305 random_state=0, 326 random_state=0, 410 random_state=42, 424 random_state=42, 798 random_state=0, 824 random_state=0, 1170 random_state=0, 1178 random_state=0, 1276 random_state=0, [all …]
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H A D | test_spectral_embedding.py | 90 random_state = np.random.RandomState(seed) 158 random_state=0, 286 n_components=n_clusters, affinity="rbf", random_state=random_state 292 random_state=random_state, 295 km = KMeans(n_clusters=n_clusters, random_state=random_state) 353 random_state = np.random.RandomState(36) 354 data = random_state.randn(10, 30) 364 random_state = np.random.RandomState(36) 365 data = random_state.randn(10, 30) 385 data = random_state.randn(10, 30) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/tests/ |
H A D | test_theil_sen.py | 37 random_state = np.random.RandomState(0) 46 x = random_state.normal(size=n_samples) 65 random_state = np.random.RandomState(0) 68 X = random_state.normal(size=(n_samples, 2)) 81 random_state = np.random.RandomState(0) 84 X = random_state.normal(size=(n_samples, 4)) 141 random_state = np.random.RandomState(0) 229 random_state = np.random.RandomState(0) 232 y = random_state.normal(size=n_samples) 275 random_state = np.random.RandomState(0) [all …]
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H A D | test_ransac.py | 64 random_state=0, 82 random_state=0, 96 random_state=0, 119 random_state=0, 133 random_state=0, 179 random_state=0, 344 random_state=0, 435 random_state=0, 442 random_state=0, 468 random_state=0, [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/cluster/ |
H A D | plot_kmeans_assumptions.py | 26 random_state = 170 variable 27 X, y = make_blobs(n_samples=n_samples, random_state=random_state) 30 y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X) 39 y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso) 47 n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state 49 y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied) 57 y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_filtered)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ |
H A D | random_projection.py | 188 rng = check_random_state(random_state) 258 rng = check_random_state(random_state) 274 n_features, n_nonzero_i, random_state=rng 307 self.random_state = random_state 502 random_state=random_state, 523 random_state = check_random_state(self.random_state) 525 n_components, n_features, random_state=random_state 657 random_state=None, 663 random_state=random_state, 686 random_state = check_random_state(self.random_state) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/tests/ |
H A D | test_spectral.py | 49 random_state=0, 77 n_samples=100, centers=centers, cluster_std=1.0, random_state=42 96 n_samples=100, centers=centers, cluster_std=1.0, random_state=42 135 random_state=0, 161 sp = SpectralClustering(n_clusters=2, gamma=2, random_state=0) 198 random_state = np.random.RandomState(seed=8) 201 y_true = random_state.randint(0, n_class + 1, n_samples) 211 y_pred = discretize(y_true_noisy, random_state=random_state) 249 graph, n_clusters=2, eigen_solver="arpack", random_state=0 256 graph, n_clusters=2, eigen_solver="amg", random_state=0 [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/decomposition/tests/ |
H A D | test_sparse_pca.py | 17 def generate_toy_data(n_components, n_samples, image_size, random_state=None): argument 20 rng = check_random_state(random_state) 46 spca = SparsePCA(n_components=8, random_state=rng) 51 spca = SparsePCA(n_components=13, random_state=rng) 121 pca = MiniBatchSparsePCA(n_components=8, random_state=rng) 126 pca = MiniBatchSparsePCA(n_components=13, random_state=rng) 148 n_components=3, n_jobs=2, alpha=alpha, random_state=0 160 n_components=3, method="cd", alpha=alpha, random_state=0 168 Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng) 177 Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/covariance/ |
H A D | _robust_covariance.py | 95 random_state = check_random_state(random_state) 103 random_state=random_state, 323 random_state=random_state, 338 random_state=random_state, 499 random_state=random_state, 520 random_state=random_state, 539 random_state=random_state, 557 random_state=random_state, 566 random_state=random_state, 714 self.random_state = random_state [all …]
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/dports/math/py-nevergrad/nevergrad-0.4.3.post2/nevergrad/optimization/ |
H A D | sequences.py | 40 if random_state is None: 42 self.random_state = random_state 95 random_state: tp.Optional[RandomState] = None, 99 super().__init__(dimension, budget, random_state=random_state) 123 random_state: tp.Optional[RandomState] = None, 127 super().__init__(dimension, budget, random_state=random_state) 141 if random_state is None: 167 random_state: tp.Optional[RandomState] = None, 169 super().__init__(dimension, budget, random_state=random_state) 170 self.permgen = HaltonPermutationGenerator(dimension, scrambling, random_state=random_state) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/ |
H A D | test_dummy.py | 250 y = random_state.randn(4) 267 y_test = random_state.randn(20, 5) 290 y = random_state.randn(5) 307 y_test = random_state.randn(20, 5) 324 y = random_state.randn(5) 354 y_test = random_state.randn(20, 5) 418 y = random_state.randn(5) 437 constants = random_state.randn(5) 482 X = random_state.randn(10, 10) 483 y = random_state.randn(10, 5) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/ |
H A D | _spectral.py | 79 random_state = check_random_state(random_state) 112 rotation[:, 0] = vectors[random_state.randint(n_samples), :].T 166 random_state=None, argument 291 random_state = check_random_state(random_state) 304 random_state=random_state, 313 maps, n_clusters, random_state=random_state, n_init=n_init, verbose=verbose 316 labels = discretize(maps, random_state=random_state) 527 random_state=None, argument 543 self.random_state = random_state 621 random_state = check_random_state(self.random_state) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/tests/ |
H A D | test_common.py | 560 random_state = check_random_state(0) 591 random_state = check_random_state(0) 610 random_state = check_random_state(0) 630 random_state = check_random_state(0) 675 random_state = check_random_state(0) 797 random_state = check_random_state(0) 1019 random_state=0, 1026 random_state=1, 1175 random_state=0, 1182 random_state=1, [all …]
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/dports/textproc/py-wordcloud/word_cloud-1.5.0/wordcloud/ |
H A D | wordcloud.py | 81 if random_state is None: 82 random_state = Random() 105 if random_state is None: 106 random_state = Random() 141 if random_state is None: 324 random_state = Random(random_state) 325 self.random_state = random_state 393 random_state = self.random_state 509 random_state=random_state, 661 random_state = Random(random_state) [all …]
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