def test_energy_discrete():
for inference_method in get_installed(["qpbo", "ad3"]):
crf = EdgeFeatureGraphCRF(n_states=3,
inference_method=inference_method,
n_edge_features=2, n_features=3)
for i in range(10):
x = np.random.normal(size=(7, 8, 3))
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
y_hat = crf.inference(x, w, relaxed=False)
energy = compute_energy(crf._get_unary_potentials(x, w),
crf._get_pairwise_potentials(x, w), edges,
y_hat)
joint_feature = crf.joint_feature(x, y_hat)
energy_svm = np.dot(joint_feature, w)
assert_almost_equal(energy, energy_svm)
def test_energy_discrete():
for inference_method in get_installed(["qpbo", "ad3"]):
crf = EdgeFeatureGraphCRF(n_states=3,
inference_method=inference_method,
n_edge_features=2, n_features=3)
for i in xrange(10):
x = np.random.normal(size=(7, 8, 3))
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
y_hat = crf.inference(x, w, relaxed=False)
energy = compute_energy(crf._get_unary_potentials(x, w),
crf._get_pairwise_potentials(x, w), edges,
y_hat)
joint_feature = crf.joint_feature(x, y_hat)
energy_svm = np.dot(joint_feature, w)
assert_almost_equal(energy, energy_svm)
def test_unary_potentials():
#print "---SIMPLE---------------------------------------------------------------------"
#g, (node_f, edges, edge_f) = get_simple_graph_structure(), get_simple_graph()
g = NodeTypeEdgeFeatureGraphCRF(
1 #how many node type?
, [4] #how many labels per node type?
, [3] #how many features per node type?
, np.array([[3]]) #how many features per node type X node type?
)
node_f = [ np.array([[1,1,1],
[2,2,2]])
]
edges = [ np.array([[0,1]])
] #an edge from 0 to 1
edge_f = [ np.array([[3,3,3]])
]
x = (node_f, edges, edge_f)
#print "- - - - - - - - - - - - - - - - - - - - - - - - - - - "
y = np.hstack([ np.array([1,2])])
# y = np.array([1,0])
#print y
g.initialize(x, y)
gref = EdgeFeatureGraphCRF(4,3,3)
xref = (node_f[0], edges[0], edge_f[0])
wref = np.arange(gref.size_joint_feature)
potref = gref._get_unary_potentials(xref, wref)
#print `potref`
w = np.arange(g.size_joint_feature)
pot = g._get_unary_potentials(x, w)
#print `pot`
assert_array_equal(pot, [potref])
pwpotref = gref._get_pairwise_potentials(xref, wref)
#print `pwpotref`
pwpot = g._get_pairwise_potentials(x, w)
#print `pwpot`
assert_array_equal(pwpot, [pwpotref])
