def _draw_subject_specific_and_estimate(cov_u, dim, vnum,
outliers_num, b, show_corrs=False,
repeat_one=0, label=None):
covars = []
noise_covars = []
for i in range(0, vnum-outliers_num):
# ---construct covariance of subject#
LambdaMatrix = _draw_eigenvalues(dim=args.dim, b=args.b, distribution="unif", overlap=args.overlap_eigenvalues)
rotMatrix = _draw_rotation_matrix(args.dim)
noise_cov = rotMatrix.dot(LambdaMatrix.dot(rotMatrix.transpose()))
cov = np.add(cov_u, noise_cov)
noise_covars.append(noise_cov)
covars.append(cov)
for i in range(repeat_one):
covars.append(cov)
# --------------------------------------#
print("snr is :" + str(snr(np.asarray(noise_covars), cov_u)))
if(show_corrs):
factor =0.03 + (not args.overlap_eigenvalues)*dim*0.03
f, axarr = plt.subplots(3, 3, figsize=(11, 5))
idxs = np.random.choice(range(len(covars)), size=(3, 3))
for i in range(idxs.shape[0]):
for j in range(idxs.shape[1]):
im = axarr[i, j].pcolormesh(np.flipud((connectivity_matrices.cov_to_corr(covars[idxs[i, j]])).T),
cmap=plt.cm.gist_earth_r,
edgecolors='k', vmin=-factor*b, vmax=factor*b, **{'linewidth': 0.1})
axarr[i, j].set_axis_off()
axarr[i, j].set_aspect('equal', 'box')
if(j == 2):
divider = make_axes_locatable(axarr[i, j])
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = f.colorbar(im, cax=cax)
tick_locations = (-factor*b, 0, factor*b)
tick_labels = ('{:{width}.{prec}f}'.format(-factor*b, width=1, prec=1),
'{:{width}.{prec}f}'.format(0, width=1, prec=1),
'{:{width}.{prec}f}'.format(factor*b, width=1, prec=1))
cb.locator = ticker.FixedLocator(tick_locations)
cb.formatter = ticker.FixedFormatter(tick_labels)
cb.update_ticks()
if label is None:
plt.savefig('./subjects_covars.eps', format='eps', dpi=1000)
else:
plt.savefig('./subjects_covars_' + str(label) + '.eps', format='eps', dpi=1000)
# construct outliers
w, v = LA.eig(cov_u)
for i in range(0, outliers_num):
LambdaMatrix = _draw_eigenvalues(dim=args.dim, b=args.b, distribution="unif", overlap=args.overlap_eigenvalues)
rotMatrix = _draw_rotation_matrix(dim=3)
cov = rotMatrix.dot(LambdaMatrix.dot(rotMatrix.transpose()))
covars.append(cov)
our_est = est_common_cov(covars, outliers=args.outliers)
eucl_mean = mean_euclid(np.array(covars))
riemann_mean = mean_covariance(np.array(covars), metric='riemann', sample_weight=None)
return our_est, eucl_mean, riemann_mean
def test_distance_wrapper_random(met, gfunc, get_covmats):
n_trials, n_channels = 2, 5
covmats = get_covmats(n_trials, n_channels)
A, B = covmats[0], covmats[1]
if gfunc is geodesic_euclid:
Ctrue = mean_euclid(covmats)
elif gfunc is geodesic_logeuclid:
Ctrue = mean_logeuclid(covmats)
elif gfunc is geodesic_riemann:
Ctrue = mean_riemann(covmats)
assert geodesic(A, B, 0.5, metric=met) == approx(Ctrue)
def test_random_mat(self, geodesic_func, get_covmats):
n_trials, n_channels = 2, 5
covmats = get_covmats(n_trials, n_channels)
A, B = covmats[0], covmats[1]
if geodesic_func is geodesic_euclid:
Ctrue = mean_euclid(covmats)
elif geodesic_func is geodesic_logeuclid:
Ctrue = mean_logeuclid(covmats)
elif geodesic_func is geodesic_riemann:
Ctrue = mean_riemann(covmats)
self.geodesic_0(geodesic_func, A, B)
self.geodesic_1(geodesic_func, A, B)
self.geodesic_middle(geodesic_func, A, B, Ctrue)
def test_power_mean(get_covmats):
"""Test the power mean"""
n_matrices, n_channels = 3, 3
covmats = get_covmats(n_matrices, n_channels)
C_power_1 = mean_power(covmats, 1)
C_power_0 = mean_power(covmats, 0)
C_power_m1 = mean_power(covmats, -1)
C_arithm = mean_euclid(covmats)
C_geom = mean_riemann(covmats)
C_harm = mean_harmonic(covmats)
assert C_power_1 == approx(C_arithm)
assert C_power_0 == approx(C_geom)
assert C_power_m1 == approx(C_harm)
def test_euclid_mean():
"""Test the euclidean mean"""
covmats, diags, A = generate_cov(100, 3)
C = mean_euclid(covmats)
assert_array_almost_equal(C, covmats.mean(axis=0))
def test_mean_covariance_euclid():
"""Test mean_covariance for euclidean metric"""
covmats, diags, A = generate_cov(100, 3)
C = mean_covariance(covmats, metric='euclid')
Ctrue = mean_euclid(covmats)
assert_array_equal(C, Ctrue)
def test_mean_covariance_euclid():
"""Test mean_covariance for euclidean metric"""
covmats = generate_cov(100, 3)
C = mean_covariance(covmats, metric='euclid')
Ctrue = mean_euclid(covmats)
assert_array_equal(C, Ctrue)
def test_euclid_mean():
"""Test the euclidean mean"""
covmats = generate_cov(100, 3)
C = mean_euclid(covmats)
assert_array_almost_equal(C, covmats.mean(axis=0))
def test_euclid_mean(get_covmats):
"""Test the euclidean mean"""
n_matrices, n_channels = 100, 3
covmats = get_covmats(n_matrices, n_channels)
C = mean_euclid(covmats)
assert C == approx(covmats.mean(axis=0))
C = np.dot(Q, np.dot(D, Q.T))
theta = np.pi / 2.0
R = np.array([[cos(theta), -sin(theta)], [sin(theta), cos(theta)]])
Coutlier = np.dot(R, np.dot(C, R.T))
covs = [C, Coutlier]
for _ in range(5):
theta = 1 * np.pi / 20 * np.random.randn()
R = np.array([[cos(theta), -sin(theta)], [sin(theta), cos(theta)]])
Cr = np.dot(R, np.dot(C, R.T))
covs.append(Cr)
for cov in covs:
e = gen_ellipse(cov)
ax.add_artist(e)
covs = np.stack(covs)
Crmn = mean_riemann(covs)
Ceuc = mean_euclid(covs)
e = gen_ellipse(Crmn, ec='blue', lw=2.0)
ax.add_artist(e)
e = gen_ellipse(Ceuc, ec='red', lw=2.0)
ax.add_artist(e)
ax.set_xlim(-1.2, +1.2)
ax.set_ylim(-1.2, +1.2)
plt.show()
