def test_iterative_max_product_chain():
rnd = np.random.RandomState(0)
chain = np.c_[np.arange(9), np.arange(1, 10)]
for i in xrange(10):
unary_potentials = rnd.normal(size=(10, 3))
pairwise_potentials = rnd.normal(size=(9, 3, 3))
result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, chain, branch_and_bound=True)
result_mp = iterative_max_product(unary_potentials, pairwise_potentials, chain)
assert_array_equal(result_ad3, result_mp)
def test_iterative_max_product_chain():
rnd = np.random.RandomState(0)
chain = np.c_[np.arange(9), np.arange(1, 10)]
for i in xrange(10):
unary_potentials = rnd.normal(size=(10, 3))
pairwise_potentials = rnd.normal(size=(9, 3, 3))
result_ad3 = inference_ad3(unary_potentials,
pairwise_potentials,
chain,
branch_and_bound=True)
result_mp = iterative_max_product(unary_potentials,
pairwise_potentials, chain)
assert_array_equal(result_ad3, result_mp)
def test_iterative_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 = iterative_max_product(unary_potentials, pairwise_potentials, tree_edges)
assert_array_equal(result_ad3, result_mp)
def test_iterative_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 = iterative_max_product(unary_potentials,
pairwise_potentials, tree_edges)
assert_array_equal(result_ad3, result_mp)
