pyriemann.datasets.make_matrices¶
- pyriemann.datasets.make_matrices(n_matrices, n_dim, kind, rs=None, return_params=False, evals_low=0.5, evals_high=2.0, eigvecs_same=False)¶
Generate a set of matrices, with specific properties.
- Parameters
- n_matricesint
Number of matrices to generate.
- n_dimint
Dimension of square matrices to generate.
- kind{‘real’, ‘comp’, ‘spd’, ‘spsd’, ‘hpd’, ‘hpsd’}
Kind of matrices to generate:
‘real’ for real-valued matrices;
‘comp’ for complex-valued matrices;
‘spd’ for symmetric positive-definite matrices;
‘spsd’ for symmetric positive semi-definite matrices;
‘hpd’ for Hermitian positive-definite matrices;
‘hpsd’ for Hermitian positive semi-definite matrices.
- rsRandomState instance, default=None
Random state for reproducible output across multiple function calls.
- return_paramsbool, default=False
If True, then returns evals and evecs for ‘spd’, ‘spsd’, ‘hpd’ and ‘hpsd’.
- evals_lowfloat, default=0.5
Lowest value of the uniform distribution to draw eigen values.
- evals_highfloat, default=2.0
Highest value of the uniform distribution to draw eigen values.
- eigvecs_samebool, default False
If True, then uses the same eigen vectors for all matrices.
- Returns
- matsndarray, shape (n_matrices, n_dim, n_dim)
Generated matrices.
- evalsndarray, shape (n_matrices, n_dim)
Eigen values used for ‘spd’, ‘spsd’, ‘hpd’ and ‘hpsd’. Only returned if
return_params=True
.- evecsndarray, shape (n_matrices, n_dim, n_dim) or (n_dim, n_dim)
Eigen vectors used for ‘spd’, ‘spsd’, ‘hpd’ and ‘hpsd’. Only returned if
return_params=True
.
Notes
New in version 0.5.