/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/ |
H A D | test_multivariate.py | 52 pdf = multivariate_normal.pdf(x, mean, cov) 89 d1 = multivariate_normal.logpdf(x) 90 d2 = multivariate_normal.pdf(x) 93 d4 = multivariate_normal.pdf(x, None, 1) 112 d1 = multivariate_normal.logcdf(x) 113 d2 = multivariate_normal.cdf(x) 116 d4 = multivariate_normal.cdf(x, None, 1) 412 u = multivariate_normal(mean=0, cov=1) 441 rv = multivariate_normal(mean, cov) 839 multivariate_normal(mu, sigma) [all …]
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H A D | test_kdeoth.py | 82 xn = np.random.multivariate_normal(mean, covariance, size=n_basesample).T 93 normpdf = stats.multivariate_normal.pdf(np.dstack([x, y]), mean=mean, cov=covariance) 121 xn = np.random.multivariate_normal(mean, covariance, size=n_basesample).T 133 normpdf = stats.multivariate_normal.pdf(np.dstack([x, y]), mean=mean, cov=covariance) 483 xn_2d = np.random.multivariate_normal(mean, covariance, size=n_basesample).T
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/dports/math/py-matplotlib2/matplotlib-2.2.4/examples/api/ |
H A D | power_norm.py | 13 from numpy.random import multivariate_normal 16 multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000), 17 multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
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/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/mpl_examples/api/ |
H A D | power_norm.py | 13 from numpy.random import multivariate_normal 16 multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000), 17 multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
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/dports/math/py-matplotlib/matplotlib-3.4.3/examples/scales/ |
H A D | power_norm.py | 13 from numpy.random import multivariate_normal 20 multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000), 21 multivariate_normal([30, 20], [[3, 1], [1, 3]], size=1000)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/covariance/tests/ |
H A D | test_graphical_lasso.py | 32 X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples) 148 X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples) 176 X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200) 215 X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200) 254 X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200)
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/dports/math/py-autograd/autograd-1.3/autograd/scipy/stats/ |
H A D | multivariate_normal.py | 9 pdf = primitive(scipy.stats.multivariate_normal.pdf) 10 logpdf = primitive(scipy.stats.multivariate_normal.logpdf) 11 entropy = primitive(scipy.stats.multivariate_normal.entropy)
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H A D | __init__.py | 12 from . import multivariate_normal
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/dports/math/py-numpy/numpy-1.20.3/numpy/random/tests/ |
H A D | test_generator_mt19937_regressions.py | 68 mt19937.multivariate_normal([0], [[0]], size=1) 69 mt19937.multivariate_normal([0], [[0]], size=np.int_(1)) 70 mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
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H A D | test_regression.py | 76 np.random.multivariate_normal([0], [[0]], size=1) 77 np.random.multivariate_normal([0], [[0]], size=np.int_(1)) 78 np.random.multivariate_normal([0], [[0]], size=np.int64(1))
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H A D | test_randomstate_regression.py | 80 random.multivariate_normal([0], [[0]], size=1) 81 random.multivariate_normal([0], [[0]], size=np.int_(1)) 82 random.multivariate_normal([0], [[0]], size=np.int64(1))
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/dports/math/py-jax/jax-0.2.9/jax/_src/scipy/stats/ |
H A D | multivariate_normal.py | 25 @_wraps(osp_stats.multivariate_normal.logpdf, update_doc=False, lax_description=""" 49 @_wraps(osp_stats.multivariate_normal.pdf, update_doc=False)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/ |
H A D | ex_multivar_kde.py | 27 V = np.random.multivariate_normal(mu1, cov1, size=nobs) 28 V[ix, :] = np.random.multivariate_normal(mu2, cov2, size=nobs)[ix, :]
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/dports/science/pybrain/pybrain-0.3.3/docs/tutorials/ |
H A D | fnn.py | 24 from numpy.random import multivariate_normal 35 input = multivariate_normal(means[klass], cov[klass])
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/dports/math/py-numpy/numpy-1.20.3/doc/source/release/ |
H A D | 1.18.3-notes.rst | 18 `numpy.random.multivariate_normal`. Those were producing samples from the 41 …/numpy/numpy/pull/15916>`__: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
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/dports/science/pybrain/pybrain-0.3.3/examples/supervised/neuralnets+svm/datasets/ |
H A D | datagenerator.py | 7 from numpy.random import multivariate_normal, rand 24 input = multivariate_normal(means[c],cov[c])
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/panel/ |
H A D | random_panel.py | 135 noise = self.random_state.multivariate_normal(np.zeros(nobs_i), 148 noise[idx:idxupp] = self.random_state.multivariate_normal(
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/dports/math/py-jax/jax-0.2.9/jax/scipy/stats/ |
H A D | multivariate_normal.py | 17 from jax._src.scipy.stats.multivariate_normal import (
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H A D | __init__.py | 25 from . import multivariate_normal
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/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/numpy/ |
H A D | random.py | 429 def multivariate_normal(mean, cov, size=None, check_valid=None, tol=None): function 503 return _mx_nd_np.random.multivariate_normal(mean, cov, size=size, check_valid=None, tol=None)
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/numpy/ |
H A D | random.py | 429 def multivariate_normal(mean, cov, size=None, check_valid=None, tol=None): function 503 return _mx_nd_np.random.multivariate_normal(mean, cov, size=size, check_valid=None, tol=None)
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/dports/math/py-jax/jax-0.2.9/jax/ |
H A D | random.py | 95 multivariate_normal,
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/dports/science/py-chainer/chainer-7.8.0/chainer/distributions/ |
H A D | __init__.py | 16 from chainer.distributions.multivariate_normal import MultivariateNormal # NOQA
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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 | 46 Z = rng.multivariate_normal(mean, cov, size=1000) 135 Z = rng.multivariate_normal(mean, cov, size=1000)
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/dports/science/py-GPy/GPy-1.10.0/GPy/util/ |
H A D | initialization.py | 22 …EMP = np.asfortranarray(np.random.multivariate_normal(np.zeros(Y.shape[0]), YYT, min(input_dim, Y.…
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