def test_io(tmpdir, nmm_example):
alphabet = nmm_example["alphabet"]
hmm = nmm_example["hmm"]
dp = nmm_example["dp"]
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AUGAUU", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(r.sequence, r.path), -7.069201008427531)
filepath = Path(tmpdir / "prof.nmm")
with Output.create(bytes(filepath)) as output:
prof = Profile.create(alphabet)
prof.append_model(Model.create(hmm, dp))
output.write(prof)
output.write(prof)
output.write(prof)
with Input.create(bytes(filepath)) as input:
nmodels = 0
for prof in input:
alphabet = prof.alphabet
model = prof.models[0]
dp_task = DPTask.create(model.dp)
seq = Sequence.create(b"AUGAUU", alphabet)
dp_task.setup(seq)
r = model.dp.viterbi(dp_task)
hmm = model.hmm
score = hmm.loglikelihood(r.sequence, r.path)
assert_allclose(score, -7.069201008427531)
nmodels += 1
assert nmodels == 3
def test_codon_state():
base = BaseAlphabet.create(b"ACGU", b"X")
codonp = CodonLprob.create(base)
codonp.set_lprob(Codon.create(b"AUG", base), log(0.8))
codonp.set_lprob(Codon.create(b"AUU", base), log(0.1))
state = CodonState.create(b"M1", codonp)
assert state.name == b"M1"
assert_allclose(state.lprob(Sequence.create(b"AUG", base)), log(0.8))
assert_allclose(state.lprob(Sequence.create(b"AUU", base)), log(0.1))
assert_allclose(state.lprob(Sequence.create(b"ACU", base)), -inf)
def test_sequence_table():
alphabet = Alphabet.create(b"ACGT", b"X")
seqt = SequenceTable.create(alphabet)
with pytest.raises(RuntimeError):
seqt.normalize()
seqt.add(Sequence.create(b"AGTG", alphabet), log(0.2))
seqt.add(Sequence.create(b"T", alphabet), log(1.2))
assert_allclose(seqt.lprob(Sequence.create(b"AGTG", alphabet)), log(0.2))
assert_allclose(seqt.lprob(Sequence.create(b"T", alphabet)), log(1.2))
assert lprob_is_zero(seqt.lprob(Sequence.create(b"", alphabet)))
with pytest.raises(RuntimeError):
seqt.lprob(Sequence.create(b"AT", Alphabet.create(b"AT", b"X")))
with pytest.raises(RuntimeError):
seqt.add(Sequence.create(b"AT", Alphabet.create(b"AT", b"X")), log(0.2))
seqt.normalize()
assert_allclose(seqt.lprob(Sequence.create(b"AGTG", alphabet)), log(0.2 / 1.4))
assert_allclose(
seqt.lprob(Sequence.create(b"T", alphabet)), log(1.2 / 1.4), rtol=1e-6
)
def test_mute_state():
alphabet = Alphabet.create(b"ACGU", b"X")
state = MuteState.create(b"S", alphabet)
assert state.name == b"S"
assert_allclose(state.lprob(Sequence.create(b"", alphabet)), log(1.0))
assert lprob_is_zero(state.lprob(Sequence.create(b"AC", alphabet)))
assert state.min_seq == 0
assert state.max_seq == 0
assert str(state) == "S"
assert repr(state) == "<MuteState:S>"
def test_table_state():
alphabet = Alphabet.create(b"ACGU", b"X")
seqt = SequenceTable.create(alphabet)
seqt.add(Sequence.create(b"AUG", alphabet), log(0.8))
seqt.add(Sequence.create(b"AUU", alphabet), log(0.4))
state = TableState.create(b"M2", seqt)
assert state.name == b"M2"
assert_allclose(state.lprob(Sequence.create(b"AUG", alphabet)), log(0.8))
assert_allclose(state.lprob(Sequence.create(b"AUU", alphabet)), log(0.4))
assert str(state) == "M2"
assert repr(state) == "<TableState:M2>"
def test_io(tmpdir, imm_example):
alphabet = imm_example["alphabet"]
hmm = imm_example["hmm"]
dp = imm_example["dp"]
dp_task = DPTask.create(dp)
dp_task.setup(Sequence.create(b"AC", alphabet))
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(r.sequence, r.path), log(0.48))
filepath = Path(tmpdir / "tmp.imm")
with Output.create(bytes(filepath)) as output:
prof = Profile.create(alphabet)
prof.append_model(Model.create(hmm, dp))
output.write(prof)
prof = Profile.create(alphabet)
prof.append_model(Model.create(hmm, dp))
output.write(prof)
prof = Profile.create(alphabet)
prof.append_model(Model.create(hmm, dp))
output.write(prof)
input = Input.create(bytes(filepath))
nmodels = 0
for prof in input:
model = prof.models[0]
dp_task = DPTask.create(model.dp)
dp_task.setup(Sequence.create(b"AC", model.hmm.alphabet))
r = model.dp.viterbi(dp_task)
hmm = model.hmm
assert_allclose(hmm.loglikelihood(r.sequence, r.path), log(0.48))
nmodels += 1
input.close()
assert nmodels == 3
with Input.create(bytes(filepath)) as input:
nmodels = 0
for prof in input:
model = prof.models[0]
dp_task = DPTask.create(model.dp)
dp_task.setup(Sequence.create(b"AC", model.hmm.alphabet))
r = model.dp.viterbi(dp_task)
hmm = model.hmm
assert_allclose(hmm.loglikelihood(r.sequence, r.path), log(0.48))
nmodels += 1
assert nmodels == 3
def test_fragment():
alphabet = BaseAlphabet.create(b"ACGT", b"X")
seq = Sequence.create(b"ACAAAGATX", alphabet)
S = MuteState.create(b"S", alphabet)
E = MuteState.create(b"E", alphabet)
M1 = NormalState.create(
b"M1",
alphabet,
[log(0.8), log(0.2), log(0.01),
log(0.01)],
)
M2 = NormalState.create(
b"M2", alphabet,
[log(0.4), log(0.6), log(0.1), log(0.6)])
path = Path.create([
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0)
])
fragment = Fragment(seq, path)
i = iter(fragment)
frag_step = next(i)
assert bytes(frag_step.sequence) == b""
assert frag_step.step.seq_len == 0
assert frag_step.step.state.name == S.name
frag_step = next(i)
assert bytes(frag_step.sequence) == b"A"
assert frag_step.step.seq_len == 1
assert frag_step.step.state.name == M1.name
def test_hmm():
abc = BaseAlphabet.create(b"ACGU", b"X")
baset = BaseLprob.create(abc, (log(0.25), log(0.25), log(0.25), log(0.25)))
codonp = CodonLprob.create(abc)
codonp.set_lprob(Codon.create(b"AUG", abc), log(0.8))
codonp.set_lprob(Codon.create(b"AUU", abc), log(0.1))
B = MuteState.create(b"B", abc)
M1 = FrameState.create(b"M1", baset, CodonMarg.create(codonp), 0.02)
M2 = FrameState.create(b"M2", baset, CodonMarg.create(codonp), 0.01)
E = MuteState.create(b"E", abc)
hmm = HMM.create(abc)
hmm.add_state(B, log(0.5))
hmm.add_state(M1)
hmm.add_state(M2)
hmm.add_state(E)
hmm.set_transition(B, M1, log(0.8))
hmm.set_transition(B, M2, log(0.2))
hmm.set_transition(M1, M2, log(0.1))
hmm.set_transition(M1, E, log(0.4))
hmm.set_transition(M2, E, log(0.3))
dp = hmm.create_dp(E)
task = DPTask.create(dp)
task.setup(Sequence.create(b"AUGAUU", abc))
result = dp.viterbi(task)
loglik = hmm.loglikelihood(task.sequence, result.path)
assert_allclose(loglik, -7.069201008427531)
def test_hmm_states():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S)
seqt = SequenceTable.create(alphabet)
seqt.add(Sequence.create(b"AGU", alphabet), log(0.8))
seqt.add(Sequence.create(b"AGG", alphabet), log(0.2))
M = TableState.create(b"M", seqt)
hmm.add_state(M)
with pytest.raises(ValueError):
hmm.add_state(S)
with pytest.raises(ValueError):
hmm.add_state(M)
assert len(hmm.states()) == 2
def test_hmm_viterbi_2():
alphabet = Alphabet.create(b"AC", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(b"M1", alphabet, [log(0.8), log(0.2)])
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(b"M2", alphabet, [log(0.4), log(0.6)])
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.48))
seq = Sequence.create(b"AA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.32))
seq = Sequence.create(b"CA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.08))
seq = Sequence.create(b"CC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.12))
hmm.set_transition(M1, E, log(1.0))
seq = Sequence.create(b"AC", alphabet)
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.48))
seq = Sequence.create(b"AA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.32))
def test_sequence():
alphabet = Alphabet.create(b"ACGT", b"X")
seq = Sequence.create(b"ACAAAGATX", alphabet)
assert len(seq) == 9
assert bytes(seq) == b"ACAAAGATX"
assert str(seq) == "ACAAAGATX"
assert repr(seq) == "<Sequence:ACAAAGATX>"
Sequence.create(b"ACGXXT", alphabet)
with pytest.raises(RuntimeError):
Sequence.create(b"ACGWT", alphabet)
with pytest.raises(RuntimeError):
Sequence.create("ACGTç".encode(), alphabet)
def test_hmm_viterbi_1():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(
b"M1",
alphabet,
[log(0.8), log(0.2), lprob_zero(), lprob_zero()],
)
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(
b"M2",
alphabet,
[log(0.4 / 1.6), log(0.6 / 1.6), lprob_zero(), log(0.6 / 1.6)],
)
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
assert_allclose(hmm.transition(E, E), lprob_zero())
assert_allclose(hmm.transition(S, S), lprob_zero())
assert_allclose(hmm.transition(S, E), lprob_zero())
assert_allclose(hmm.transition(E, S), lprob_zero())
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
result = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, result.path), log(0.3))
def test_normal_state():
alphabet = Alphabet.create(b"ACGT", b"X")
state = NormalState.create(
b"M0",
alphabet,
[log(0.1), log(0.2), log(0.3), log(0.3)],
)
assert state.name == b"M0"
assert_allclose(state.lprob(Sequence.create(b"A", alphabet)), log(0.1))
assert_allclose(state.lprob(Sequence.create(b"C", alphabet)), log(0.2))
assert_allclose(state.lprob(Sequence.create(b"G", alphabet)), log(0.3))
assert_allclose(state.lprob(Sequence.create(b"T", alphabet)), log(0.3))
assert state.min_seq == 1
assert state.max_seq == 1
with pytest.raises(RuntimeError):
state.lprob(Sequence.create(b"T", Alphabet.create(b"ACGT", b"X")))
assert lprob_is_zero(state.lprob(Sequence.create(b"AC", alphabet)))
assert str(state) == "M0"
assert repr(state) == "<NormalState:M0>"
def test_hmm_loglikelihood():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(
b"M1",
alphabet,
[log(0.8), log(0.2), lprob_zero(), lprob_zero()],
)
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(
b"M2", alphabet, [log(0.4 / 1.6), log(0.6 / 1.6), lprob_zero(), log(0.6 / 1.6)]
)
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
p = hmm.loglikelihood(
Sequence.create(b"AC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.3))
p = hmm.loglikelihood(
Sequence.create(b"AA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.2))
p = hmm.loglikelihood(
Sequence.create(b"AG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"AU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.3))
p = hmm.loglikelihood(
Sequence.create(b"CC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.075))
p = hmm.loglikelihood(
Sequence.create(b"CA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.05))
p = hmm.loglikelihood(
Sequence.create(b"CG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"CG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"CU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.075))
p = hmm.loglikelihood(
Sequence.create(b"GC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"GA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"GG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"GU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
M3 = NormalState.create(
b"M2",
alphabet,
[log(0.4), log(0.6), lprob_zero(), log(0.6)],
)
with pytest.raises(ValueError):
hmm.loglikelihood(
Sequence.create(b"UU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M3, 1),
Step.create(E, 0),
]
),
)
def test_frame_state():
base = BaseAlphabet.create(b"ACGU", b"X")
basep = BaseLprob.create(base,
(log(0.25), log(0.25), log(0.25), log(0.25)))
codonp = CodonLprob.create(base)
codonp.set_lprob(Codon.create(b"AUG", base), log(0.8))
codonp.set_lprob(Codon.create(b"AUU", base), log(0.1))
frame_state = FrameState.create(b"M1", basep, CodonMarg.create(codonp),
0.0)
assert lprob_is_zero(frame_state.lprob(Sequence.create(b"AUA", base)))
assert_allclose(frame_state.lprob(Sequence.create(b"AUG", base)), log(0.8))
assert_allclose(frame_state.lprob(Sequence.create(b"AUU", base)), log(0.1))
assert lprob_is_zero(frame_state.lprob(Sequence.create(b"AU", base)))
assert lprob_is_zero(frame_state.lprob(Sequence.create(b"A", base)))
assert lprob_is_zero(frame_state.lprob(Sequence.create(b"AUUA", base)))
assert lprob_is_zero(frame_state.lprob(Sequence.create(b"AUUAA", base)))
codonp.normalize()
frame_state = FrameState.create(b"M1", basep, CodonMarg.create(codonp),
0.1)
assert_allclose(frame_state.lprob(Sequence.create(b"AUA", base)),
-6.905597115665666)
assert_allclose(frame_state.lprob(Sequence.create(b"AUG", base)),
-0.5347732882047062,
rtol=1e-6)
assert_allclose(frame_state.lprob(Sequence.create(b"AUU", base)),
-2.5902373304999466,
rtol=1e-6)
assert_allclose(frame_state.lprob(Sequence.create(b"AU", base)),
-2.9158434238698336)
assert_allclose(frame_state.lprob(Sequence.create(b"A", base)),
-5.914503505971854)
assert_allclose(frame_state.lprob(Sequence.create(b"AUUA", base)),
-6.881032208841384)
assert_allclose(frame_state.lprob(Sequence.create(b"AUUAA", base)),
-12.08828960987379)
assert lprob_is_zero(frame_state.lprob(Sequence.create(b"AUUAAA", base)))
lprob, codon = frame_state.decode(Sequence.create(b"AUA", base))
assert_allclose(lprob, -7.128586690537968)
assert codon.symbols == b"AUG"
lprob, codon = frame_state.decode(Sequence.create(b"AUAG", base))
assert_allclose(lprob, -4.813151489562624)
assert codon.symbols == b"AUG"
lprob, codon = frame_state.decode(Sequence.create(b"A", base))
assert_allclose(lprob, -6.032286541628237)
assert codon.symbols == b"AUG"
lprob, codon = frame_state.decode(Sequence.create(b"UUU", base))
assert_allclose(lprob, -8.110186062956258)
assert codon.symbols == b"AUU"
def create_sequence(self, sequence: bytes) -> Sequence:
return Sequence.create(sequence, self.alphabet)
def test_hmm_viterbi_3():
alphabet = Alphabet.create(b"AC", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(b"M1", alphabet, [log(0.8), log(0.2)])
hmm.add_state(M1, lprob_zero())
D1 = MuteState.create(b"D1", alphabet)
hmm.add_state(D1, lprob_zero())
M2 = NormalState.create(b"M2", alphabet, [log(0.4), log(0.6)])
hmm.add_state(M2, lprob_zero())
D2 = MuteState.create(b"D2", alphabet)
hmm.add_state(D2, lprob_zero())
hmm.set_transition(S, M1, log(0.8))
hmm.set_transition(S, D1, log(0.2))
hmm.set_transition(M1, M2, log(0.8))
hmm.set_transition(M1, D2, log(0.2))
hmm.set_transition(D1, D2, log(0.2))
hmm.set_transition(D1, M2, log(0.8))
hmm.set_transition(D2, E, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
result = dp.viterbi(dp_task)
score = hmm.loglikelihood(seq, result.path)
assert bytes(result.sequence) == b"AC"
path = result.path
steps = list(path)
assert steps[0].seq_len == 0
assert steps[1].seq_len == 1
assert steps[2].seq_len == 1
assert steps[3].seq_len == 0
assert_allclose(score, log(0.3072))
seq = Sequence.create(b"AA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.2048))
seq = Sequence.create(b"A", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.128))
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.3072))
dp = hmm.create_dp(M2)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.3072))
hmm.del_state(E)
dp = hmm.create_dp(M2)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
result = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, result.path), log(0.3072))