treeple.experimental.simulate.simulate_multivariate_gaussian#
- treeple.experimental.simulate.simulate_multivariate_gaussian(mean=None, cov=None, d=2, n_samples=1000, seed=1234)[source]#
Multivariate gaussian simulation for testing entropy and MI estimators.
Simulates samples from a “known” multivariate gaussian distribution and then passes those samples, along with the true analytical MI/CMI.
- Parameters:
- meanarray_like of shape (n_features,)
The optional mean array. If None (default), a random standard normal vector is drawn.
- covarray_like of shape (n_features, n_features)
The covariance array. If None (default), a random standard normal 2D array is drawn. It is then converted to a PD matrix.
- d
int The dimensionality of the multivariate gaussian. By default 2.
- n_samples
int The number of samples to generate. By default 1000.
- seed
int The random seed to feed to
numpy.random.default_rng().
- Returns:
- dataarray_like of shape (n_samples, n_features)
The generated data from the distribution.
- meanarray_like of shape (n_features,)
The mean vector of the distribution.
- covarray_like of shape (n_features, n_features)
The covariance matrix of the distribution.