def test_k_means_initialization_directional_grid_crf():
X, Y = generate_big_checker(n_samples=10)
crf = LatentDirectionalGridCRF(n_states_per_label=1, n_features=2,
n_labels=2)
#crf.initialize(X, Y)
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_k_means_initialization_directional_grid_crf():
X, Y = generate_big_checker(n_samples=10)
crf = LatentDirectionalGridCRF(n_states_per_label=1, n_features=2,
n_labels=2)
#crf.initialize(X, Y)
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_k_means_initialization_graph_crf():
# with only 1 state per label, nothing happends
X, Y = generate_big_checker(n_samples=10)
crf = LatentGraphCRF(n_states_per_label=1, n_features=2, n_labels=2)
# convert grid model to graph model
X = [(x.reshape(-1, x.shape[-1]), make_grid_edges(x, return_lists=False))
for x in X]
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_k_means_initialization_graph_crf():
# with only 1 state per label, nothing happends
X, Y = generate_big_checker(n_samples=10)
crf = LatentGraphCRF(n_states_per_label=1, n_features=2, n_labels=2)
# convert grid model to graph model
X = [(x.reshape(-1, x.shape[-1]), make_grid_edges(x, return_lists=False))
for x in X]
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_k_means_initialization():
n_samples = 10
X, Y = generate_big_checker(n_samples=n_samples)
edges = [make_grid_edges(x, return_lists=True) for x in X]
# flatten the grid
Y = Y.reshape(Y.shape[0], -1)
X = X.reshape(X.shape[0], -1, X.shape[-1])
n_labels = len(np.unique(Y))
X = X.reshape(n_samples, -1, n_labels)
# sanity check for one state
H = kmeans_init(X,
Y,
edges,
n_states_per_label=[1] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(Y, H)
# check number of states
H = kmeans_init(X,
Y,
edges,
n_states_per_label=[3] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(np.unique(H), np.arange(6))
assert_array_equal(Y, H / 3)
# for dataset with more than two states
X, Y = generate_blocks_multinomial(n_samples=10)
edges = [make_grid_edges(x, return_lists=True) for x in X]
Y = Y.reshape(Y.shape[0], -1)
X = X.reshape(X.shape[0], -1, X.shape[-1])
n_labels = len(np.unique(Y))
# sanity check for one state
H = kmeans_init(X,
Y,
edges,
n_states_per_label=[1] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(Y, H)
# check number of states
H = kmeans_init(X,
Y,
edges,
n_states_per_label=[2] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(np.unique(H), np.arange(6))
assert_array_equal(Y, H / 2)
def test_k_means_initialization():
n_samples = 10
X, Y = generate_big_checker(n_samples=n_samples)
edges = [make_grid_edges(x, return_lists=True) for x in X]
# flatten the grid
Y = Y.reshape(Y.shape[0], -1)
X = X.reshape(X.shape[0], -1, X.shape[-1])
n_labels = len(np.unique(Y))
X = X.reshape(n_samples, -1, n_labels)
# sanity check for one state
H = kmeans_init(X, Y, edges, n_states_per_label=[1] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(Y, H)
# check number of states
H = kmeans_init(X, Y, edges, n_states_per_label=[3] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(np.unique(H), np.arange(6))
assert_array_equal(Y, H // 3)
# for dataset with more than two states
X, Y = generate_blocks_multinomial(n_samples=10)
edges = [make_grid_edges(x, return_lists=True) for x in X]
Y = Y.reshape(Y.shape[0], -1)
X = X.reshape(X.shape[0], -1, X.shape[-1])
n_labels = len(np.unique(Y))
# sanity check for one state
H = kmeans_init(X, Y, edges, n_states_per_label=[1] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(Y, H)
# check number of states
H = kmeans_init(X, Y, edges, n_states_per_label=[2] * n_labels,
n_labels=n_labels)
H = np.vstack(H)
assert_array_equal(np.unique(H), np.arange(6))
assert_array_equal(Y, H // 2)
def test_k_means_initialization_grid_crf():
# with only 1 state per label, nothing happends
X, Y = generate_big_checker(n_samples=10)
crf = LatentGridCRF(n_states_per_label=1, n_features=2, n_labels=2)
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_k_means_initialization_grid_crf():
# with only 1 state per label, nothing happends
X, Y = generate_big_checker(n_samples=10)
crf = LatentGridCRF(n_states_per_label=1, n_features=2, n_labels=2)
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
