def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True):
"""Compute the Mean Squared Error between two covariance estimators.
(In the sense of the Frobenius norm).
Parameters
----------
comp_cov : array-like, shape = [n_features, n_features]
The covariance to compare with.
norm : str
The type of norm used to compute the error. Available error types:
- 'frobenius' (default): sqrt(tr(A^t.A))
- 'spectral': sqrt(max(eigenvalues(A^t.A))
where A is the error ``(comp_cov - self.covariance_)``.
scaling : bool
If True (default), the squared error norm is divided by n_features.
If False, the squared error norm is not rescaled.
squared : bool
Whether to compute the squared error norm or the error norm.
If True (default), the squared error norm is returned.
If False, the error norm is returned.
Returns
-------
The Mean Squared Error (in the sense of the Frobenius norm) between
`self` and `comp_cov` covariance estimators.
"""
return error_norm_time(self.covariance_, comp_cov, norm=norm, scaling=scaling, squared=squared)
def set_results(
vs, model, name, i, labels_true, labels_pred, thetas_true_sparse,
thetas_true_rep, obs_precs_sparse, obs_precs, tac):
th = name in ['wp', 'ticc']
vs.setdefault((name, i), {}).setdefault('model', []).append(model)
vs.setdefault(
(name, i),
{}).setdefault('v_measure',
[]).append(v_measure_score(labels_true, labels_pred))
vs.setdefault((name, i), {}).setdefault('structure_error', []).append(
structure_error(
thetas_true_sparse, obs_precs_sparse, no_diagonal=True,
thresholding=th, eps=1e-5))
vs.setdefault(
(name, i),
{}).setdefault('error_norm',
[]).append(error_norm_time(thetas_true_rep, obs_precs))
vs.setdefault((name, i), {}).setdefault('error_norm_sparse', []).append(
error_norm_time(thetas_true_sparse, obs_precs_sparse))
vs.setdefault((name, i), {}).setdefault('time', []).append(tac)
def test_error_norm_time():
"""Test error_norm_time function."""
a = np.arange(27).reshape(3, 3, 3)
a += a.T
assert_equal(utils.error_norm_time(a, a), 0)