`scipy.stats._multivariate:matrix_t_gen.rvs`

source: /scipy/stats/_multivariate.py :2018

Signature

   def     rvs (    self    ,    mean    =  None   ,    row_spread    =  1   ,    col_spread    =  1   ,    df    =  1   ,    size    =  1   ,    random_state    =  None     )   →  np.ndarray

Summary

Draw random samples from a matrix t distribution.

Parameters

mean : array_like, optional: Mean of the distribution (default: None)
row_spread : array_like, optional: Row-wise 2nd order raw central moment matrix (default: 1)
col_spread : array_like, optional: Column-wise 2nd order raw central moment matrix (default: 1)
df : scalar, optional: Degrees of freedom (default: 1)
size : integer, optional: Number of samples to draw (default 1).
seed : {None, int, np.random.RandomState, np.random.Generator}, optional: Used for drawing random variates. If seed is None, the ~np.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a RandomState or Generator instance, then that object is used. Default is None.

Returns

rvs : ndarray or scalar: Random variates of size (size, dims), where dims is the dimension of the random matrices.

Notes

If mean is set to None then a matrix of zeros is used for the mean. The dimensions of this matrix are inferred from the shape of row_spread and col_spread, if these are provided, or set to 1 if ambiguous.

row_spread and col_spread can be two-dimensional array_likes specifying the spread matrices directly. Alternatively, a one-dimensional array will be be interpreted as the entries of a diagonal matrix, and a scalar or zero-dimensional array will be interpreted as this value times the identity matrix.

This method takes advantage of the two equivalent expressions of the probability density function. It samples a Cholesky factor of a random variate of the appropriate inverse Wishart distribution using the smaller of the row/column dimensions.

Aliases

scipy.stats._multivariate.matrix_t_gen.rvs