def test_canica_square_img():
shape = (20, 20)
rng = np.random.RandomState(0)
# Create two images with "activated regions"
component1 = np.zeros(shape)
component1[:5, :10] = 1
component1[5:10, :10] = -1
component2 = np.zeros(shape)
component2[:5, -10:] = 1
component2[5:10, -10:] = -1
component3 = np.zeros(shape)
component3[-5:, -10:] = 1
component3[-10:-5, -10:] = -1
component4 = np.zeros(shape)
component4[-5:, :10] = 1
component4[-10:-5, :10] = -1
components = np.vstack((component1.ravel(), component2.ravel(), component3.ravel(), component4.ravel()))
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = np.dot(rng.normal(size=(40, 4)), components)
this_data += 0.01 * rng.normal(size=this_data.shape)
data.append(this_data)
canica = CanICA(n_components=4, random_state=rng)
sparsity = np.infty
for rs in range(50):
canica.random_state = np.random.RandomState(rs)
canica.fit(data)
maps_ = canica.maps_
sparsity_ = np.sum(np.abs(maps_), 1).max()
if sparsity_ < sparsity:
sparsity = sparsity_
maps = maps_
# FIXME: This could be done more efficiently, e.g. thanks to hungarian
# Find pairs of matching components
indices = range(4)
for i in range(4):
map = np.abs(maps[i]) > np.abs(maps[i]).max() * 0.95
for j in indices:
ref_map = components[j].ravel() != 0
if np.all(map == ref_map):
indices.remove(j)
break
else:
assert False, "Non matching component"
def test_canica_square_img():
rng = np.random.RandomState(0)
# Create two images with an "activated regions"
component1 = np.zeros((100, 100))
component1[:25, :50] = 1
component1[25:50, :50] = -1
component2 = np.zeros((100, 100))
component2[:25, -50:] = 1
component2[25:50, -50:] = -1
component3 = np.zeros((100, 100))
component3[-25:, -50:] = 1
component3[-50:-25, -50:] = -1
component4 = np.zeros((100, 100))
component4[-25:, :50] = 1
component4[-50:-25, :50] = -1
components = np.vstack((component1.ravel(), component2.ravel(),
component3.ravel(), component4.ravel()))
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = np.dot(rng.normal(size=(40, 4)), components)
this_data += .01 * rng.normal(size=this_data.shape)
data.append(this_data)
canica = CanICA(n_components=4, random_state=rng)
canica.fit(data)
maps = canica.maps_
# FIXME: This could be done more efficiently, e.g. thanks to hungarian
# Find pairs of matching components
indices = range(4)
for i in range(4):
map = np.abs(maps[i]) > np.abs(maps[i]).max() * 0.95
for j in indices:
ref_map = components[j].ravel() != 0
if np.all(map == ref_map):
indices.remove(j)
break;
else:
assert False, "Non matching component"
def test_canica_square_img():
rng = np.random.RandomState(0)
# Create two images with an "activated regions"
component1 = np.zeros((100, 100))
component1[:25, :50] = 1
component1[25:50, :50] = -1
component2 = np.zeros((100, 100))
component2[:25, -50:] = 1
component2[25:50, -50:] = -1
component3 = np.zeros((100, 100))
component3[-25:, -50:] = 1
component3[-50:-25, -50:] = -1
component4 = np.zeros((100, 100))
component4[-25:, :50] = 1
component4[-50:-25, :50] = -1
components = np.vstack((component1.ravel(), component2.ravel(),
component3.ravel(), component4.ravel()))
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = np.dot(rng.normal(size=(40, 4)), components)
this_data += .01 * rng.normal(size=this_data.shape)
data.append(this_data)
canica = CanICA(n_components=4, random_state=rng)
canica.fit(data)
maps = canica.maps_
# FIXME: This could be done more efficiently, e.g. thanks to hungarian
# Find pairs of matching components
indices = range(4)
for i in range(4):
map = np.abs(maps[i]) > np.abs(maps[i]).max() * 0.95
for j in indices:
ref_map = components[j].ravel() != 0
if np.all(map == ref_map):
indices.remove(j)
break
else:
assert False, "Non matching component"
component3 = np.zeros((100, 100))
component3[-25:, -50:] = 1
component3[-50:-25, -50:] = -1
component4 = np.zeros((100, 100))
component4[-25:, :50] = 1
component4[-50:-25, :50] = -1
components = np.vstack((component1.ravel(), component2.ravel(),
component3.ravel(), component4.ravel()))
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = np.dot(rng.normal(size=(40, 4)), components)
this_data += .01 * rng.normal(size=this_data.shape)
data.append(this_data)
canica = CanICA(n_components=4, random_state=rng)
canica.fit(data)
maps = canica.maps_
assert_array_equal(np.abs(maps[0]) > np.abs(maps[0]).max() * 0.95,
component2.ravel() != 0)
assert_array_equal(np.abs(maps[2]) > np.abs(maps[2]).max() * 0.95,
component3.ravel() != 0)
assert_array_equal(np.abs(maps[1]) > np.abs(maps[1]).max() * 0.95,
component1.ravel() != 0)
assert_array_equal(np.abs(maps[3]) > np.abs(maps[3]).max() * 0.95,
component4.ravel() != 0)
