def test_tree_max_product_chain():
rnd = np.random.RandomState(0)
forward = np.c_[np.arange(9), np.arange(1, 10)]
backward = np.c_[np.arange(1, 10), np.arange(9)]
for i in xrange(10):
unary_potentials = rnd.normal(size=(10, 3))
pairwise_potentials = rnd.normal(size=(9, 3, 3))
for chain in [forward, backward]:
result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, chain, branch_and_bound=True)
result_mp = inference_max_product(unary_potentials, pairwise_potentials, chain)
assert_array_equal(result_ad3, result_mp)
def test_tree_max_product_chain():
rnd = np.random.RandomState(0)
forward = np.c_[np.arange(9), np.arange(1, 10)]
backward = np.c_[np.arange(1, 10), np.arange(9)]
for i in xrange(10):
unary_potentials = rnd.normal(size=(10, 3))
pairwise_potentials = rnd.normal(size=(9, 3, 3))
for chain in [forward, backward]:
result_ad3 = inference_ad3(unary_potentials,
pairwise_potentials,
chain,
branch_and_bound=True)
result_mp = inference_max_product(unary_potentials,
pairwise_potentials, chain)
assert_array_equal(result_ad3, result_mp)
def test_tree_max_product_tree():
try:
from scipy.sparse.csgraph import minimum_spanning_tree
except:
raise SkipTest("Not testing trees, scipy version >= 0.11 required")
rnd = np.random.RandomState(0)
for i in xrange(100):
# generate random tree using mst
graph = rnd.uniform(size=(10, 10))
tree = minimum_spanning_tree(sparse.csr_matrix(graph))
tree_edges = np.c_[tree.nonzero()]
unary_potentials = rnd.normal(size=(10, 3))
pairwise_potentials = rnd.normal(size=(9, 3, 3))
result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, tree_edges, branch_and_bound=True)
result_mp = inference_max_product(unary_potentials, pairwise_potentials, tree_edges)
assert_array_equal(result_ad3, result_mp)
def test_tree_max_product_tree():
try:
from scipy.sparse.csgraph import minimum_spanning_tree
except:
raise SkipTest("Not testing trees, scipy version >= 0.11 required")
rnd = np.random.RandomState(0)
for i in xrange(100):
# generate random tree using mst
graph = rnd.uniform(size=(10, 10))
tree = minimum_spanning_tree(sparse.csr_matrix(graph))
tree_edges = np.c_[tree.nonzero()]
unary_potentials = rnd.normal(size=(10, 3))
pairwise_potentials = rnd.normal(size=(9, 3, 3))
result_ad3 = inference_ad3(unary_potentials,
pairwise_potentials,
tree_edges,
branch_and_bound=True)
result_mp = inference_max_product(unary_potentials,
pairwise_potentials, tree_edges)
assert_array_equal(result_ad3, result_mp)
def batch_marginals(self, param_x, param_y, w, num_classes=None):
ys = []
marginalss = []
for x in param_x:
if self.inference_method == "max-product":
y = inference_max_product(self._get_unary_potentials(x, w),
self._get_pairwise_potentials(x, w),
self._get_edges(x),
relaxed=True)
marginals = self.turn_dense_to_one_matrix(y, num_classes)
elif self.inference_method == "ad3":
y, marginals = inference_ad3_local(
self._get_unary_potentials(x, w),
self._get_pairwise_potentials(x, w),
self._get_edges(x),
relaxed=True,
branch_and_bound=False,
return_marginals=True)
else:
print("Unknown inference method !")
ys.append(y)
marginalss.append(marginals)
return np.array(ys), np.array(marginalss)