cupy.random.multivariate_normal¶
-
cupy.random.
multivariate_normal
(mean, cov, size=None, check_valid='ignore', tol=1e-08, dtype=<class 'float'>)[source]¶ (experimental) Multivariate normal distribution.
Returns an array of samples drawn from the multivariate normal distribution. Its probability density function is defined as
\[f(x) = \frac{1}{(2\pi|\Sigma|)^(n/2)} \exp\left(-\frac{1}{2} (x-\mu)^{\top}\Sigma^{-1}(x-\mu)\right).\]Parameters: - mean (1-D array_like, of length N) – Mean of the multivariate normal distribution \(\mu\).
- cov (2-D array_like, of shape (N, N)) – Covariance matrix \(\Sigma\) of the multivariate normal distribution. It must be symmetric and positive-semidefinite for proper sampling.
- size (int or tuple of ints) – The shape of the array. If
None
, a zero-dimensional array is generated. - check_valid ('warn', 'raise', 'ignore') – Behavior when the covariance matrix is not positive semidefinite.
- tol (float) – Tolerance when checking the singular values in covariance matrix.
- dtype – Data type specifier. Only
numpy.float32
andnumpy.float64
types are allowed.
Returns: Samples drawn from the multivariate normal distribution.
Return type: See also
numpy.random.multivariate_normal