def test_traits(self):
from hmm.pssm import create_background_model, PssmTraits, seq_to_numpy
from infpy import check_is_close_2
p_binding_site = .01
num_background_states = 2
emission_dists = [
[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 1., 0.],
[0., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
]
K = len(emission_dists)
test_seq = 'accagtttgcact' # matches dist above
test_seq_order_0 = seq_to_numpy(test_seq)
# for various different orders
for order in [1, 2]:
# build a model of distribution above
traits = PssmTraits(K,
p_binding_site,
order,
num_background_states,
create_background_model,
emission_dists=emission_dists)
model = traits.new_model()
converted = hmm.model_states_2_model(model)
B = converted.B
# check the reverse complement states are correct
for n in xrange(model.N):
for o in xrange(model.M):
rev_comp_state, rev_comp_obs = traits.get_non_reverse_complement(
n, o)
assert check_is_close_2(
B[rev_comp_state, rev_comp_obs],
B[n, o]), ('%d,%d %d,%d: %f %f' %
(rev_comp_state, rev_comp_obs, n, o,
B[rev_comp_state, rev_comp_obs], B[n, o]))
# check viterbi gives correct result
test_seq_order_n = converted.converter.to_order_n(test_seq_order_0)
LL, states = converted.viterbi(test_seq_order_n)
for i, state in enumerate(states):
assert state == num_background_states + i
def test_traits(self):
from hmm.pssm import create_background_model, PssmTraits, seq_to_numpy
from infpy import check_is_close_2
p_binding_site = .01
num_background_states = 2
emission_dists = [
[ 1., 0., 0., 0. ],
[ 0., 1., 0., 0. ],
[ 0., 1., 0., 0. ],
[ 1., 0., 0., 0. ],
[ 0., 0., 1., 0. ],
[ 0., 0., 0., 1. ],
[ 0., 0., 0., 1. ],
[ 0., 0., 0., 1. ],
[ 0., 0., 1., 0. ],
[ 0., 1., 0., 0. ],
[ 1., 0., 0., 0. ],
[ 0., 1., 0., 0. ],
[ 0., 0., 0., 1. ],
]
K = len(emission_dists)
test_seq = 'accagtttgcact' # matches dist above
test_seq_order_0 = seq_to_numpy(test_seq)
# for various different orders
for order in [1, 2]:
# build a model of distribution above
traits = PssmTraits(K, p_binding_site, order, num_background_states, create_background_model, emission_dists=emission_dists)
model = traits.new_model()
converted = hmm.model_states_2_model(model)
B = converted.B
# check the reverse complement states are correct
for n in xrange(model.N):
for o in xrange(model.M):
rev_comp_state, rev_comp_obs = traits.get_non_reverse_complement(n,o)
assert check_is_close_2(B[rev_comp_state,rev_comp_obs], B[n,o]), ('%d,%d %d,%d: %f %f' % (rev_comp_state,rev_comp_obs,n,o,B[rev_comp_state,rev_comp_obs],B[n,o]))
# check viterbi gives correct result
test_seq_order_n = converted.converter.to_order_n(test_seq_order_0)
LL, states = converted.viterbi(test_seq_order_n)
for i, state in enumerate(states):
assert state == num_background_states+i
