def _test11():
# A reduced version of test10
ret = ""
# GmmMgr setup
num_states = 2
dimension = 2
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
gmm_mgr = GmmMgr(models)
gb = GraphBuilder()
node_id0 = gb.new_node((0, 0))
node_id1 = gb.new_node((1, 1))
node_id2 = gb.new_node((2, 1))
node_id3 = gb.new_node((3, 1))
node_id4 = gb.new_node((4, 2))
# The topology here is slightly complex than the previous example
arc_id = gb.new_arc(node_id0, node_id1)
arc_id = gb.new_arc(node_id1, node_id4)
arc_id = gb.new_arc(node_id0, node_id2)
arc_id = gb.new_arc(node_id2, node_id3)
arc_id = gb.new_arc(node_id3, node_id4)
arc_id = gb.new_arc(node_id2, node_id4)
gr0 = FrozenGraph(gb)
# Make two Hmms with 3 states and order 2 (self loop, forward 1)
# The models in the middle are special and can skip.
seed(0)
hmm0 = make_forward_hmm(gmm_mgr, num_states, order=2, exact=False)
hmm1 = Hmm(1)
trans = array(((0.0, 0.5, 0.5), (0.0, 0.5, 0.5), (0.0, 0.0, 0.0)))
hmm1.build_model(gmm_mgr, (0, ), 1, 1, trans)
hmm2 = make_forward_hmm(gmm_mgr, num_states, order=2, exact=True)
hmm_mgr = HmmMgr((hmm0, hmm1, hmm2))
spd = {}
spd[(0, 1)] = (0.4, )
spd[(0, 2)] = (0.6, )
spd[(2, 3)] = (0.4, )
spd[(2, 4)] = (0.6, )
tg0 = TrainingGraph(gr0, hmm_mgr, split_prob_dict=spd)
if do_display:
tg0.dot_display()
tg0.dot_display(expand_hmms=True)
with DebugPrint("bwt_ctsh") if True else DebugPrint():
result_hmm = tg0.convert_to_standalone_hmm()
ret += "\n\n========= TG CONVERTED TO Hmm =========\n\n" + result_hmm.to_string(
full=True)
return ret
def _test11():
# A reduced version of test10
ret = ""
# GmmMgr setup
num_states = 2
dimension = 2
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
gmm_mgr = GmmMgr(models)
gb = GraphBuilder()
node_id0 = gb.new_node((0, 0))
node_id1 = gb.new_node((1, 1))
node_id2 = gb.new_node((2, 1))
node_id3 = gb.new_node((3, 1))
node_id4 = gb.new_node((4, 2))
# The topology here is slightly complex than the previous example
arc_id = gb.new_arc(node_id0, node_id1)
arc_id = gb.new_arc(node_id1, node_id4)
arc_id = gb.new_arc(node_id0, node_id2)
arc_id = gb.new_arc(node_id2, node_id3)
arc_id = gb.new_arc(node_id3, node_id4)
arc_id = gb.new_arc(node_id2, node_id4)
gr0 = FrozenGraph(gb)
# Make two Hmms with 3 states and order 2 (self loop, forward 1)
# The models in the middle are special and can skip.
seed(0)
hmm0 = make_forward_hmm(gmm_mgr, num_states, order=2, exact=False)
hmm1 = Hmm(1)
trans = array(((0.0, 0.5, 0.5), (0.0, 0.5, 0.5), (0.0, 0.0, 0.0)))
hmm1.build_model(gmm_mgr, (0,), 1, 1, trans)
hmm2 = make_forward_hmm(gmm_mgr, num_states, order=2, exact=True)
hmm_mgr = HmmMgr((hmm0, hmm1, hmm2))
spd = {}
spd[(0, 1)] = (0.4,)
spd[(0, 2)] = (0.6,)
spd[(2, 3)] = (0.4,)
spd[(2, 4)] = (0.6,)
tg0 = TrainingGraph(gr0, hmm_mgr, split_prob_dict=spd)
if do_display:
tg0.dot_display()
tg0.dot_display(expand_hmms=True)
with DebugPrint("bwt_ctsh") if True else DebugPrint():
result_hmm = tg0.convert_to_standalone_hmm()
ret += "\n\n========= TG CONVERTED TO Hmm =========\n\n" + result_hmm.to_string(full=True)
return ret
def _test9():
# Like test8, but now HMMs have multiple inputs and outputs.
ret = ""
# GmmMgr setup
num_states = 3
dimension = 2
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
gmm_mgr = GmmMgr(models)
gb = GraphBuilder()
node_id0 = gb.new_node((0, 0))
node_id1 = gb.new_node((1, 1))
node_id2 = gb.new_node((2, 1))
node_id3 = gb.new_node((3, 1))
node_id4 = gb.new_node((4, 1))
node_id5 = gb.new_node((5, 2))
arc_id = gb.new_arc(node_id0, node_id1)
arc_id = gb.new_arc(node_id1, node_id2)
arc_id = gb.new_arc(node_id2, node_id3)
arc_id = gb.new_arc(node_id3, node_id4)
arc_id = gb.new_arc(node_id4, node_id5)
gr0 = FrozenGraph(gb)
# Make two Hmms with 3 states and order 3 (self loop, forward 1, forward 2)
# The models in the middle are special and can skip directly
seed(0)
hmm0 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm1 = Hmm(1)
trans = array(
(
(0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.0, 0.5, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.5),
(0.0, 0.0, 0.0, 0.5, 0.35, 0.1, 0.05),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
)
)
hmm1.build_model(gmm_mgr, (0,), 3, 3, trans)
hmm2 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm_mgr = HmmMgr((hmm0, hmm1, hmm2))
with DebugPrint("bwt_vrfy") if False else DebugPrint():
tg0 = TrainingGraph(gr0, hmm_mgr, split_prob_dict=dict())
result_hmm = tg0.convert_to_standalone_hmm()
ret += "\n\n========= TG CONVERTED TO Hmm =========\n\n" + result_hmm.to_string(full=True)
return ret
def _test9():
# Like test8, but now HMMs have multiple inputs and outputs.
ret = ""
# GmmMgr setup
num_states = 3
dimension = 2
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
gmm_mgr = GmmMgr(models)
gb = GraphBuilder()
node_id0 = gb.new_node((0, 0))
node_id1 = gb.new_node((1, 1))
node_id2 = gb.new_node((2, 1))
node_id3 = gb.new_node((3, 1))
node_id4 = gb.new_node((4, 1))
node_id5 = gb.new_node((5, 2))
arc_id = gb.new_arc(node_id0, node_id1)
arc_id = gb.new_arc(node_id1, node_id2)
arc_id = gb.new_arc(node_id2, node_id3)
arc_id = gb.new_arc(node_id3, node_id4)
arc_id = gb.new_arc(node_id4, node_id5)
gr0 = FrozenGraph(gb)
# Make two Hmms with 3 states and order 3 (self loop, forward 1, forward 2)
# The models in the middle are special and can skip directly
seed(0)
hmm0 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm1 = Hmm(1)
trans = array(((0.0, 0.0, 0.0, 0.5, 0.5, 0.0,
0.0), (0.0, 0.0, 0.0, 0.5, 0.0, 0.5,
0.0), (0.0, 0.0, 0.0, 0.5, 0.0, 0.0,
0.5), (0.0, 0.0, 0.0, 0.5, 0.35, 0.1, 0.05),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0), (0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0), (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))
hmm1.build_model(gmm_mgr, (0, ), 3, 3, trans)
hmm2 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm_mgr = HmmMgr((hmm0, hmm1, hmm2))
with DebugPrint("bwt_vrfy") if False else DebugPrint():
tg0 = TrainingGraph(gr0, hmm_mgr, split_prob_dict=dict())
result_hmm = tg0.convert_to_standalone_hmm()
ret += "\n\n========= TG CONVERTED TO Hmm =========\n\n" + result_hmm.to_string(
full=True)
return ret
def _test10():
# Like test9, but now HMMs are arranged in a diamond pattern so inter-HMM
# probabilities come into play
ret = ""
# GmmMgr setup
num_states = 3
dimension = 2
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
gmm_mgr = GmmMgr(models)
gb = GraphBuilder()
node_id0 = gb.new_node((0, 0))
node_id1 = gb.new_node((1, 1))
node_id2 = gb.new_node((2, 1))
node_id3 = gb.new_node((3, 1))
node_id4 = gb.new_node((4, 1))
node_id5 = gb.new_node((5, 2))
# The topology here is more complex than previous examples
arc_id = gb.new_arc(node_id0, node_id1)
arc_id = gb.new_arc(node_id1, node_id5)
arc_id = gb.new_arc(node_id0, node_id2)
arc_id = gb.new_arc(node_id2, node_id3)
arc_id = gb.new_arc(node_id3, node_id4)
arc_id = gb.new_arc(node_id3, node_id5)
arc_id = gb.new_arc(node_id4, node_id5)
gr0 = FrozenGraph(gb)
# Make two Hmms with 3 states and order 3 (self loop, forward 1, forward 2)
# The models in the middle are special and can skip.
seed(0)
hmm0 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm1 = Hmm(1)
trans = array(
(
(0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.0, 0.5, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.5),
(0.0, 0.0, 0.0, 0.5, 0.35, 0.1, 0.05),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
)
)
hmm1.build_model(gmm_mgr, (0,), 3, 3, trans)
hmm2 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm_mgr = HmmMgr((hmm0, hmm1, hmm2))
spd = {}
spd[(0, 1)] = (0.4, 0.3, 0.8)
spd[(0, 2)] = (0.6, 0.7, 0.2)
spd[(3, 4)] = (0.4, 0.3, 0.8)
spd[(3, 5)] = (0.6, 0.7, 0.2)
tg0 = TrainingGraph(gr0, hmm_mgr, split_prob_dict=spd)
with DebugPrint("bwt_ctsh") if True else DebugPrint():
result_hmm = tg0.convert_to_standalone_hmm()
ret += "\n\n========= TG CONVERTED TO Hmm =========\n\n" + result_hmm.to_string(full=True)
return ret
def _test10():
# Like test9, but now HMMs are arranged in a diamond pattern so inter-HMM
# probabilities come into play
ret = ""
# GmmMgr setup
num_states = 3
dimension = 2
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
gmm_mgr = GmmMgr(models)
gb = GraphBuilder()
node_id0 = gb.new_node((0, 0))
node_id1 = gb.new_node((1, 1))
node_id2 = gb.new_node((2, 1))
node_id3 = gb.new_node((3, 1))
node_id4 = gb.new_node((4, 1))
node_id5 = gb.new_node((5, 2))
# The topology here is more complex than previous examples
arc_id = gb.new_arc(node_id0, node_id1)
arc_id = gb.new_arc(node_id1, node_id5)
arc_id = gb.new_arc(node_id0, node_id2)
arc_id = gb.new_arc(node_id2, node_id3)
arc_id = gb.new_arc(node_id3, node_id4)
arc_id = gb.new_arc(node_id3, node_id5)
arc_id = gb.new_arc(node_id4, node_id5)
gr0 = FrozenGraph(gb)
# Make two Hmms with 3 states and order 3 (self loop, forward 1, forward 2)
# The models in the middle are special and can skip.
seed(0)
hmm0 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm1 = Hmm(1)
trans = array(((0.0, 0.0, 0.0, 0.5, 0.5, 0.0,
0.0), (0.0, 0.0, 0.0, 0.5, 0.0, 0.5,
0.0), (0.0, 0.0, 0.0, 0.5, 0.0, 0.0,
0.5), (0.0, 0.0, 0.0, 0.5, 0.35, 0.1, 0.05),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0), (0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0), (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))
hmm1.build_model(gmm_mgr, (0, ), 3, 3, trans)
hmm2 = make_forward_hmm(gmm_mgr, num_states, order=3, exact=True)
hmm_mgr = HmmMgr((hmm0, hmm1, hmm2))
spd = {}
spd[(0, 1)] = (0.4, 0.3, 0.8)
spd[(0, 2)] = (0.6, 0.7, 0.2)
spd[(3, 4)] = (0.4, 0.3, 0.8)
spd[(3, 5)] = (0.6, 0.7, 0.2)
tg0 = TrainingGraph(gr0, hmm_mgr, split_prob_dict=spd)
with DebugPrint("bwt_ctsh") if True else DebugPrint():
result_hmm = tg0.convert_to_standalone_hmm()
ret += "\n\n========= TG CONVERTED TO Hmm =========\n\n" + result_hmm.to_string(
full=True)
return ret
