covmats[i_matrix, i_channel] = np.nan
covmats[i_matrix, :, i_channel] = np.nan
all_corrup_channels.append(i_channel)
all_n_corrup_channels[i_matrix] = n_corrupt_channels
return covmats, all_n_corrup_channels, all_corrup_channels
###############################################################################
# Generate data
# -------------
rs = np.random.RandomState(42)
n_matrices, n_channels = 100, 10
covmats = make_covariances(n_matrices,
n_channels,
rs,
evals_mean=100.,
evals_std=20.)
# Compute the reference, the Riemannian mean on all covariance matrices
C_ref = mean_riemann(covmats)
# Corrupt data randomly
n_corrup_channels_max = n_channels // 2
print("Maximum number of corrupted channels: {} over {}".format(
n_corrup_channels_max, n_channels))
covmats, all_n_corrup_channels, all_corrup_channels = corrupt(
covmats, n_corrup_channels_max, rs)
fig, ax = plt.subplots(nrows=1, ncols=1)
def _gen_cov_params(n_matrices, n_channels):
return make_covariances(n_matrices,
n_channels,
rndstate,
return_params=True)
"MeanField",
]
classifiers = [
MDM(),
KNearestNeighbor(n_neighbors=3),
SVC(probability=True),
MeanField(power_list=[-1, -0.25, 0, 0.25, 1]),
]
n_classifs = len(classifiers)
rs = np.random.RandomState(2022)
n_matrices, n_channels = 50, 2
y = np.concatenate([np.zeros(n_matrices), np.ones(n_matrices)])
datasets = [(np.concatenate([
make_covariances(n_matrices, n_channels, rs, evals_mean=10, evals_std=1),
make_covariances(n_matrices, n_channels, rs, evals_mean=15, evals_std=1)
]), y),
(np.concatenate([
make_covariances(n_matrices,
n_channels,
rs,
evals_mean=10,
evals_std=2),
make_covariances(n_matrices,
n_channels,
rs,
evals_mean=12,
evals_std=2)
]), y),
make_gaussian_blobs(2 * n_matrices,
def _gen_cov_params(n_trials, n_chan):
return make_covariances(n_trials, n_chan, rndstate, return_params=True)