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/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/
H A Dtest_multivariate.py52 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 …]
H A Dtest_kdeoth.py82 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
/dports/math/py-matplotlib2/matplotlib-2.2.4/examples/api/
H A Dpower_norm.py13 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)
/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/mpl_examples/api/
H A Dpower_norm.py13 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)
/dports/math/py-matplotlib/matplotlib-3.4.3/examples/scales/
H A Dpower_norm.py13 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)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/covariance/tests/
H A Dtest_graphical_lasso.py32 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)
/dports/math/py-autograd/autograd-1.3/autograd/scipy/stats/
H A Dmultivariate_normal.py9 pdf = primitive(scipy.stats.multivariate_normal.pdf)
10 logpdf = primitive(scipy.stats.multivariate_normal.logpdf)
11 entropy = primitive(scipy.stats.multivariate_normal.entropy)
H A D__init__.py12 from . import multivariate_normal
/dports/math/py-numpy/numpy-1.20.3/numpy/random/tests/
H A Dtest_generator_mt19937_regressions.py68 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))
H A Dtest_regression.py76 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))
H A Dtest_randomstate_regression.py80 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))
/dports/math/py-jax/jax-0.2.9/jax/_src/scipy/stats/
H A Dmultivariate_normal.py25 @_wraps(osp_stats.multivariate_normal.logpdf, update_doc=False, lax_description="""
49 @_wraps(osp_stats.multivariate_normal.pdf, update_doc=False)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/
H A Dex_multivar_kde.py27 V = np.random.multivariate_normal(mu1, cov1, size=nobs)
28 V[ix, :] = np.random.multivariate_normal(mu2, cov2, size=nobs)[ix, :]
/dports/science/pybrain/pybrain-0.3.3/docs/tutorials/
H A Dfnn.py24 from numpy.random import multivariate_normal
35 input = multivariate_normal(means[klass], cov[klass])
/dports/math/py-numpy/numpy-1.20.3/doc/source/release/
H A D1.18.3-notes.rst18 `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
/dports/science/pybrain/pybrain-0.3.3/examples/supervised/neuralnets+svm/datasets/
H A Ddatagenerator.py7 from numpy.random import multivariate_normal, rand
24 input = multivariate_normal(means[c],cov[c])
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/panel/
H A Drandom_panel.py135 noise = self.random_state.multivariate_normal(np.zeros(nobs_i),
148 noise[idx:idxupp] = self.random_state.multivariate_normal(
/dports/math/py-jax/jax-0.2.9/jax/scipy/stats/
H A Dmultivariate_normal.py17 from jax._src.scipy.stats.multivariate_normal import (
H A D__init__.py25 from . import multivariate_normal
/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/numpy/
H A Drandom.py429 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)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/numpy/
H A Drandom.py429 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)
/dports/math/py-jax/jax-0.2.9/jax/
H A Drandom.py95 multivariate_normal,
/dports/science/py-chainer/chainer-7.8.0/chainer/distributions/
H A D__init__.py16 from chainer.distributions.multivariate_normal import MultivariateNormal # NOQA
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/feature_selection/tests/
H A Dtest_mutual_info.py46 Z = rng.multivariate_normal(mean, cov, size=1000)
135 Z = rng.multivariate_normal(mean, cov, size=1000)
/dports/science/py-GPy/GPy-1.10.0/GPy/util/
H A Dinitialization.py22 …EMP = np.asfortranarray(np.random.multivariate_normal(np.zeros(Y.shape[0]), YYT, min(input_dim, Y.…

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