def load_segmentation_model(modeldata):
model = HiddenMarkovModel('model')
states = {}
for s in modeldata:
if len(s['emission']) == 1:
emission = NormalDistribution(*s['emission'][0][:2])
else:
weights = np.array([w for _, _, w in s['emission']])
dists = [NormalDistribution(mu, sigma)
for mu, sigma, _ in s['emission']]
emission = GeneralMixtureModel(dists, weights=weights)
state = State(emission, name=s['name'])
states[s['name']] = state
model.add_state(state)
if 'start_prob' in s:
model.add_transition(model.start, state, s['start_prob'])
for s in modeldata:
current = states[s['name']]
for nextstate, prob in s['transition']:
model.add_transition(current, states[nextstate], prob)
model.bake()
return model
def train_hmm_tagger(data):
# HMM
# Use the tag unigrams and bigrams calculated above to construct a hidden Markov tagger.
#
# - Add one state per tag
# - The emission distribution at each state should be estimated with the formula: $P(w|t) = \frac{C(t, w)}{C(t)}$
# - Add an edge from the starting state `basic_model.start` to each tag
# - The transition probability should be estimated with the formula: $P(t|start) = \frac{C(start, t)}{C(start)}$
# - Add an edge from each tag to the end state `basic_model.end`
# - The transition probability should be estimated with the formula: $P(end|t) = \frac{C(t, end)}{C(t)}$
# - Add an edge between _every_ pair of tags
# - The transition probability should be estimated with the formula: $P(t_2|t_1) = \frac{C(t_1, t_2)}{C(t_1)}$
basic_model = HiddenMarkovModel(name="base-hmm-tagger")
state_dict = {}
states = []
emission_counts = pair_counts(*list(zip(
*data.training_set.stream()))[::-1])
for tag in emission_counts.keys():
tag_count = tag_unigrams[tag]
probs = {}
for w in emission_counts[tag]:
probs[w] = emission_counts[tag][w] / tag_count
emission_p = DiscreteDistribution(probs)
state = State(emission_p, name="" + tag)
basic_model.add_state(state)
state_dict[tag] = state
for tag in tag_starts:
basic_model.add_transition(basic_model.start, state_dict[tag],
tag_starts[tag] / len(data.training_set.Y))
basic_model.add_transition(state_dict[tag], basic_model.end,
tag_ends[tag] / tag_unigrams[tag])
for (tag1, tag2) in tag_bigrams:
basic_model.add_transition(
state_dict[tag1], state_dict[tag2],
tag_bigrams[(tag1, tag2)] / tag_unigrams[tag1])
# finalize the model
basic_model.bake()
assert all(
tag in set(s.name for s in basic_model.states)
for tag in data.training_set.tagset
), "Every state in your network should use the name of the associated tag, which must be one of the training set tags."
assert basic_model.edge_count() == 168, (
"Your network should have an edge from the start node to each state, one edge between every "
+
"pair of tags (states), and an edge from each state to the end node.")
HTML(
'<div class="alert alert-block alert-success">Your HMM network topology looks good!</div>'
)
return basic_model
class HMMWrapper:
def __init__(self):
self.model = HiddenMarkovModel()
self.start = self.model.start
self.end = self.model.end
self.states_before_bake = []
self.states = None
def add_state(self, state, start_prob=0):
self.states_before_bake.append((state, start_prob))
self.model.add_state(state)
def add_transition(self, start_state, end_state, prob):
# print('adding from', start_state.name, 'to', end_state.name, prob)
self.model.add_transition(start_state, end_state, prob)
def bake(self):
starter_states_no_prob = []
free_start_prob = 1.0
for state in self.states_before_bake:
if 'none' not in state[0].name:
if not state[1]:
starter_states_no_prob.append(state)
else:
free_start_prob -= state[1]
print('asignado ' + str(state[1]) + ' a ' + state[0].name)
self.add_transition(self.start, state[0], state[1])
len_no_prob = len(starter_states_no_prob)
starter_prob = free_start_prob / len_no_prob
print(len_no_prob, starter_prob)
for state in starter_states_no_prob:
self.add_transition(self.start, state, starter_prob)
self.model.bake()
self.states = self.model.states
def make_states_from_alignment(self, first_state, last_state, seq_matrix,
name):
columns = column_clasify(seq_matrix)
zones = create_zones(columns)
grouped_states = group_states(zones, name)
add_states(self, grouped_states)
trans = calculate_transitions(first_state, last_state, grouped_states)
apply_transitions(self, trans)
def predict(self, *args, **kwargs):
return self.model.predict(*args, **kwargs)
def _initialize_new_hmm(hmm, new_states, new_transitions):
new_hmm = HiddenMarkovModel()
for state in new_states:
if state not in (hmm.start, hmm.end):
new_hmm.add_state(state)
for source_state, target_state, probability in new_transitions:
if source_state != hmm.start and target_state != hmm.end:
new_hmm.add_transition(source_state, target_state, probability)
elif source_state == hmm.start:
new_hmm.add_transition(new_hmm.start, target_state, probability)
elif target_state == hmm.end:
new_hmm.add_transition(source_state, new_hmm.end, probability)
new_hmm.bake()
return new_hmm
def insert_delete_main_hmm(data_matrix):
v_columns = column_clasify(data_matrix)
v_zones = create_zones(v_columns)
v_grouped_states = group_states(v_zones, 'test')
v_model = HiddenMarkovModel()
v_first_state = State(None, name='ali_start')
v_last_state = State(None, name='ali_end')
v_model.add_state(v_first_state)
v_model.add_transition(v_model.start, v_first_state, 1)
v_model.add_state(v_last_state)
add_states(v_model, v_grouped_states)
v_trans = calculate_transitions(v_first_state, v_last_state,
v_grouped_states)
apply_transitions(v_model, v_trans)
v_model.bake()
return v_model
class ModelWrapper:
def __init__(self):
self.model = HiddenMarkovModel()
def add_state(self, distribution, name):
state = State(distribution, name=name)
self.model.add_state(state)
return state
def bake(self):
self.model.bake()
def viterbi(self, seq):
return self.model.viterbi(seq)
def add_transition(self, states, next_state_data):
for state in states:
for next_data in next_state_data:
self.model.add_transition(state, next_data[0], next_data[1])
def create_hidden_MarkovModel(e_df, q_df, start_p_dict):
"""
Creates a Hidden Markov Model based on DataFrame
@args:
- e_df (pd.Dataframe): contains the emission probabilites
- q_df (pd.Dataframe): contains the emission probabilites
"""
model = HiddenMarkovModel(name="Example Model")
'#1: Create a dict for each key in trans. df'
model_dict = {}
for key in q_df.keys().values:
model_dict[key] = {}
'#2: Create the states'
for key in model_dict:
'#2.1.Step Add teh emission prob. to each state, , P(observation | state)'
emission_p = DiscreteDistribution(e_df[key].to_dict())
sunny_state = State(emission_p, name=key)
model_dict[key] = State(emission_p, name=key)
model.add_state(model_dict[key])
'#2.2.Step: Add the start probability for each state'
model.add_transition(model.start, model_dict[key], start_p_dict[key])
'#3.Step: Add the transition probability to each state'
for key, item in q_df.to_dict("index").items():
for item_name, value in item.items():
print(key, " , ", item_name, ": ", value)
tmp_origin = model_dict[key]
tmp_destination = model_dict[item_name]
model.add_transition(tmp_origin, tmp_destination,
q_df.loc[key, item_name])
# finally, call the .bake() method to finalize the model
model.bake()
return model
'a': 0.25,
'c': 0.25,
'g': 0.25,
't': 0.25
}),
name='back')
fixed_state = State(DiscreteDistribution({
'a': 0.45,
'c': 0.45,
'g': 0.05,
't': 0.05
}),
name='fixed')
hmmodel.add_state(back_state)
hmmodel.add_state(fixed_state)
hmmodel.add_transition(hmmodel.start, back_state, 1)
hmmodel.add_transition(back_state, back_state, 0.9)
hmmodel.add_transition(back_state, fixed_state, 0.1)
hmmodel.add_transition(fixed_state, fixed_state, 0.9)
hmmodel.add_transition(fixed_state, back_state, 0.1)
hmmodel.bake()
seq = list('acgtacgtaaaaccccaaa')
lopg, path = hmmodel.viterbi(seq)
print([x[1].name for x in path])
A simple example highlighting how to build a model using states, add transitions, and then run the algorithms, including showing how training on a sequence improves the probability of the sequence. """ import random from pomegranate import * from pomegranate import HiddenMarkovModel as Model random.seed(0) model = Model(name="ExampleModel") distribution = UniformDistribution(0.0, 1.0) state = State(distribution, name="uniform") state2 = State(NormalDistribution(0, 2), name="normal") silent = State(None, name="silent") model.add_state(state) model.add_state(state2) model.add_transition(state, state, 0.4) model.add_transition(state, state2, 0.4) model.add_transition(state2, state2, 0.4) model.add_transition(state2, state, 0.4) model.add_transition(model.start, state, 0.5) model.add_transition(model.start, state2, 0.5) model.add_transition(state, model.end, 0.2) model.add_transition(state2, model.end, 0.2) model.bake() sequence = model.sample()
fake_back = State(DiscreteDistribution(intron_distribution.p), name='back2') in0 = State(DiscreteDistribution(intron_distribution.p), name='in0') in1 = State(DiscreteDistribution(intron_distribution.p), name='in1') in2 = State(DiscreteDistribution(intron_distribution.p), name='in2') in0_spacers = spacer_states_maker(64, intron_distribution.p, 'in0 spacer') in1_spacers = spacer_states_maker(64, intron_distribution.p, 'in1 spacer') in2_spacers = spacer_states_maker(64, intron_distribution.p, 'in2 spacer') coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0') coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1') coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2') coding_model.add_state(back) coding_model.add_state(fake_back) coding_model.add_state(coding_state0) coding_model.add_state(coding_state1) coding_model.add_state(coding_state2) coding_model.add_state(in0) coding_model.add_state(in1) coding_model.add_state(in2) coding_model.add_state(exon3_state) add_sequence(coding_model, poly_a_states) add_sequence(coding_model, post_poly_spacer) add_sequence(coding_model, in0_spacers) add_sequence(coding_model, in1_spacers)
cat_states = sequence_state_factory(cat_data, 'CAT') post_cat_var_spacers_tss = spacer_states_maker(151, no_coding.p, 'post cat var spacer tss') post_cat_spacers_tss = spacer_states_maker(42, no_coding.p, 'post cat spacer tss') post_cat_var_spacers_tata = spacer_states_maker(151, no_coding.p, 'post cat var spacer tata') post_cat_spacers_tata = spacer_states_maker(22, no_coding.p, 'post cat spacer tata') tata_states = sequence_state_factory(tata_data, 'tata') post_tata_var_spacers = spacer_states_maker(16, no_coding.p, 'post_tata_var_spacer') post_tata_spacers = spacer_states_maker(4, no_coding.p, 'post_tata_spacer') inr_states = sequence_state_factory(inr_data, 'inr') no_inr_states = sequence_state_factory(no_inr_data, 'no inr') # Add States promoter_utr_model.add_state(back) # Add Sequences #GC add_sequence(promoter_utr_model, gc_states) add_sequence(promoter_utr_model, post_gc_spacers_tata) add_variable_length_sequence(promoter_utr_model, post_gc_var_spacers_tata, post_gc_spacers_tata[0]) add_sequence(promoter_utr_model, post_gc_spacers_tss) add_variable_length_sequence(promoter_utr_model, post_gc_var_spacers_tss, post_gc_spacers_tss[0]) add_sequence(promoter_utr_model, inr_states)
A simple example highlighting how to build a model using states, add transitions, and then run the algorithms, including showing how training on a sequence improves the probability of the sequence. """ import random from pomegranate import * from pomegranate import HiddenMarkovModel as Model random.seed(0) model = Model(name="ExampleModel") distribution = UniformDistribution(0.0, 1.0) state = State(distribution, name="uniform") state2 = State(NormalDistribution(0, 2), name="normal") silent = State(None, name="silent") model.add_state(state) model.add_state(state2) model.add_transition(state, state, 0.4) model.add_transition(state, state2, 0.4) model.add_transition(state2, state2, 0.4) model.add_transition(state2, state, 0.4) model.add_transition(model.start, state, 0.5) model.add_transition(model.start, state2, 0.5) model.add_transition(state, model.end, 0.2) model.add_transition(state2, model.end, 0.2) model.bake() sequence = model.sample()
mdd_states_sequences = []
for index, l_em in enumerate(leaf_emissions):
sequence = state_sequence_from(l_em, 'donor_' + str(index))
add_sequence(hm_model, sequence[0])
set_transition_probabilities(hm_model, sequence[0], sequence[1])
mdd_states_sequences.append(sequence[0])
background = State(DiscreteDistribution({
'a': 0.25,
'c': 0.25,
'g': 0.25,
't': 0.25
}),
name='background')
hm_model.add_state(background)
hm_model.add_transition(hm_model.start, background, 0.9)
fork_sequence(hm_model, [hm_model.start], mdd_states_sequences,
[0.025, 0.025, 0.025, 0.025])
hm_model.add_transition(background, background, 0.9)
fork_sequence(hm_model, [background], mdd_states_sequences,
[0.025, 0.025, 0.025, 0.025])
reunify_sequences(hm_model, mdd_states_sequences, background, [1, 1, 1, 1])
hm_model.bake()
a = 'a'
c = 'c'
g = 'g'
from converter_to import converter_to
c0, c1, c2 = calculator.calculate_proba2('../data extractors/new_cuts.txt')
matrixStop = numpy.array(matrix_from_exa('../data extractors/new_stops.exa'))
coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0')
coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1')
coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2')
post = State(DiscreteDistribution(equal_distribution), name='post')
model = HiddenMarkovModel('coding_to_stop')
stop_data = classify(matrixStop, 2)
stop_states = sequence_state_factory(stop_data, 'stop')
model.add_state(coding_state0)
model.add_state(coding_state1)
model.add_state(coding_state2)
add_sequence(model, stop_states)
model.add_state(post)
model.add_transition(model.start, coding_state1, 1)
model.add_transition(coding_state0, coding_state1, 1)
model.add_transition(coding_state1, coding_state2, 1)
model.add_transition(coding_state2, coding_state0, 0.6)
model.add_transition(coding_state2, stop_states[0], 0.4)
model.add_transition(stop_states[-1], post, 1)
model.add_transition(post, post, 0.9)
model.add_transition(post, model.end, 0.1)
def crop_type_hmm_model(nn_pobability_matrix, timeseries_steps,
n_observed_classes):
# 0 1 2 3 4 5
[
'unknown_plant', 'large_grass', 'small_grass', 'other', 'fallow',
'no_crop'
]
d0 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=0,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d1 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=1,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d2 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=2,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d3 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=3,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d4 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=4,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d5 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=5,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
s0_unk = State(d0, name='unknown_plant')
s1_large = State(d1, name='large_grass')
s2_small = State(d2, name='small_grass')
s3_other = State(d3, name='other')
s4_fallow = State(d4, name='fallow')
s5_none = State(d5, name='no_crop')
model = HiddenMarkovModel()
# Initialize each hidden state.
# All states have an equal chance of being the starting state.
for s in [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none]:
model.add_state(s)
model.add_transition(model.start, s, 1)
model.add_transitions(
s0_unk, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[95., 0., 0., 0., 0., 5.])
model.add_transitions(
s1_large, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 95., 0., 0., 0., 5.])
model.add_transitions(
s2_small, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 0., 95., 0., 0., 5.])
model.add_transitions(
s3_other, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 0., 0., 95., 0., 5.])
model.add_transitions(
s4_fallow, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 0., 0., 0., 95., 5.])
model.add_transitions(
s5_none, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[2., 2., 2., 2., 2., 90.])
model.bake(verbose=False)
return model
def train_and_test():
with open('../data extractors/exons_start_1.txt') as in_file:
total = []
for line in in_file:
no_p_line = line.replace('P', '').lower().replace('\n', '')
total.append(no_p_line)
converted_total = [converter_to(x, 2) for x in total]
matrixDonor0 = numpy.array(
matrix_from_exa('../data extractors/new_donor1.exa'))
c0, c1, c2 = calculator.calculate_proba2('../data extractors/new_cuts.txt')
print(c0.p, c1.p, c2.p)
coding_state0 = State(DiscreteDistribution(c0.p), 'coding state 0')
coding_state1 = State(DiscreteDistribution(c1.p), 'coding state 1')
coding_state2 = State(DiscreteDistribution(c2.p), 'coding state 2')
donor0_data = classify(matrixDonor0, 2)
donor0_states = sequence_state_factory(donor0_data, 'donor0')
post = State(DiscreteDistribution(equal_distribution), name='post')
model = HiddenMarkovModel('coding to donor')
model.add_state(coding_state0)
model.add_state(coding_state1)
model.add_state(coding_state2)
add_sequence(model, donor0_states)
model.add_state(post)
model.add_transition(model.start, coding_state0, 1)
model.add_transition(coding_state0, coding_state1, 0.6)
model.add_transition(coding_state0, donor0_states[0], 0.4)
model.add_transition(coding_state1, coding_state2, 0.6)
model.add_transition(coding_state1, donor0_states[0], 0.4)
model.add_transition(coding_state2, coding_state0, 0.6)
model.add_transition(coding_state2, donor0_states[0], 0.4)
model.add_transition(donor0_states[-1], post, 1)
model.add_transition(post, post, 0.9)
model.add_transition(post, model.end, 0.1)
model.bake()
test_model(model)
model.fit(converted_total,
transition_pseudocount=1,
emission_pseudocount=1,
verbose=True)
test_model(model)
with open('partial_model_coding_to_donor_model0.json', 'w') as out:
out.write(model.to_json())
def crop_status_hmm_model(nn_pobability_matrix, timeseries_steps,
n_observed_classes):
# 0 1 2 3 4 5
['emergence', 'growth', 'flowers', 'senescing', 'senesced', 'no_crop']
d0 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=0,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d1 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=1,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d2 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=2,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d3 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=3,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d4 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=4,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d5 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=5,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
s0_emerge = State(d0, name='emergence')
s1_growth = State(d1, name='growth')
s2_fls = State(d2, name='flowers')
s3_sencing = State(d3, name='senescing')
s4_senced = State(d4, name='senesced')
s5_none = State(d5, name='no_crop')
model = HiddenMarkovModel()
# Initialize each hidden state.
# All states have an equal chance of being the starting state.
for s in [s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none]:
model.add_state(s)
model.add_transition(model.start, s, 1)
model.add_transitions(
s0_emerge,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[90., 5., 0., 0., 0., 5.])
model.add_transitions(
s1_growth,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 90., 2.5, 2.5, 0., 5.])
model.add_transitions(
s2_fls, [s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 0., 90., 5., 0., 5.])
model.add_transitions(
s3_sencing,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 0., 0., 90., 5., 5.])
model.add_transitions(
s4_senced,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 0., 0., 0., 90., 10.])
model.add_transitions(
s5_none,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[10., 0, 0., 0., 0., 90.])
model.bake(verbose=False)
return model
basic_model = HiddenMarkovModel(name="base-hmm-tagger")
states = {}
for tag in data.tagset:
emission_prob = {}
for word, number in emission_counts[tag].items():
emission_prob[word] = number / tag_unigrams[tag]
tag_distribution = DiscreteDistribution(emission_prob)
state = State(tag_distribution, name=tag)
states[tag] = state
basic_model.add_state(state)
for tag in data.tagset:
state = states[tag]
start_probability = tag_starts[tag] / sum(tag_starts.values())
basic_model.add_transition(basic_model.start, state, start_probability)
end_probability = tag_ends[tag] / sum(tag_ends.values())
basic_model.add_transition(state, basic_model.end, end_probability)
for tag1 in data.tagset:
state_1 = states[tag1]
for tag2 in data.tagset:
def dominant_cover_hmm_model(nn_pobability_matrix, timeseries_steps,
n_observed_classes):
d0 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=0,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d1 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=1,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d2 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=2,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d3 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=3,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d4 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=4,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
s0_veg = State(d0, name='vegetation')
s1_residue = State(d1, name='residue')
s2_soil = State(d2, name='soil')
s3_snow = State(d3, name='snow')
s4_water = State(d4, name='water')
model = HiddenMarkovModel()
# Initialize each hidden state.
# All states have an equal chance of being the starting state.
for s in [s0_veg, s1_residue, s2_soil, s3_snow, s4_water]:
model.add_state(s)
model.add_transition(model.start, s, 1)
model.add_transitions(s0_veg,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[95., 1.0, 1.0, 1.0, 1.0])
model.add_transitions(s1_residue,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 95., 1.0, 1.0, 1.0])
model.add_transitions(s2_soil,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 1.0, 95., 1.0, 1.0])
model.add_transitions(s3_snow,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 1.0, 1.0, 95., 1.0])
model.add_transitions(s4_water,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 1.0, 1.0, 1.0, 95.])
model.bake(verbose=False)
return model
from model_maker_utils import add_sequence
from model_maker_utils import equal_distribution
from matrix_from_aln import matrix_from_exa
matrixAcceptor0 = numpy.array(matrix_from_exa('new_acceptor1.exa'))
acceptor0_data = classify(matrixAcceptor0, 2)
model = HiddenMarkovModel('intron_acceptor')
intron = State(DiscreteDistribution(
calculator.intron_calculator('cuts_intron.txt').p),
name='in')
acceptor0_states = sequence_state_factory(acceptor0_data, 'acceptor0')
post = State(DiscreteDistribution(equal_distribution), name='post')
model.add_state(intron)
add_sequence(model, acceptor0_states)
model.add_state(post)
model.add_transition(model.start, intron, 1)
model.add_transition(intron, intron, 0.9)
model.add_transition(intron, acceptor0_states[0], 0.1)
model.add_transition(acceptor0_states[-1], post, 1)
model.add_transition(post, post, 0.5)
model.add_transition(post, model.end, 0.5)
model.bake()
test_l = 'GTAACACTGAATACTCAGGAACAATTAATGGATGGTAACATATGAGGAATATCTAGGAGGCACACCCTCTCTGGCATCTATGATGGGCCAAAAACCCGCATTCGCTTGGCCACAGTATGTGAAATATAACCCAGCTTAGACACAGGGTGCGGCAGCTGTCATGTTTCTCTGTGTGTGCCGAGTGTCATGTCTGCACCGTACAGGGATAGCTGAGTCTTCATCCTCCTCAGCTCCTATCTGTCCAGTGCAATGAACAGCAGCTGCTCTCTTCCTCTCTGGTTCCCATGGCAGCCATGCTCTGTTGCAGAGAGAACAGGATTGCATGTTCCCTCTTAATGGGAACGTCCATTTTGCTTTCTGGGACCACTCTCTTAATGCCGCCTGTCAAAACCAGCTAGGACTCCCTGGGGTCCAATCCCTCTGTGTTTAATCTTCTGTCATCTCTGTCCCACCTGGCTCATCAGGGAGATGCAGAAGGCTGAAGAAAAGGAAGTCCCTGAGGACTCACTGGAGGAATGTGCCATCACTTGTTCAAATAGCCATGGCCCTTATGACTCCAACCATGACTCCAACC'
converted = converter_to(test_l.lower().replace(' ', '').replace('p', ''))
#logp, path = model.viterbi(converted)
oks += 1
else:
not_ok += 1
print(oks / (oks + not_ok))
back = State(DiscreteDistribution(equal_distribution), name='back')
back2 = State(DiscreteDistribution(equal_distribution), name='back2')
matrixZE = numpy.array(matrix_from_exa('../data extractors/starts.exa'))
start_states_data = classify(matrixZE, 2)
start_states = sequence_state_factory(start_states_data, 'start zone')
model = HiddenMarkovModel()
model.add_state(back)
model.add_state(back2)
add_sequence(model, start_states)
model.add_transition(model.start, back, 1)
model.add_transition(back, back, 0.55)
model.add_transition(back, start_states[0], 0.45)
model.add_transition(start_states[-1], back2, 1)
model.add_transition(back2, back2, 0.5)
model.bake()
def train_and_test():
test(model)
tag_starts = starting_counts(data.training_set.Y)
# Calculate the count of each tag ending a sequence
tag_ends = ending_counts(data.training_set.Y)
basic_model = HiddenMarkovModel(name="base-hmm-tagger")
# Create states with emission probability distributions P(word | tag) and add to the model
tag_states = {}
for tag in data.training_set.tagset:
tag_emissions = DiscreteDistribution({
word: emission_counts[tag][word] / tag_unigrams[tag]
for word in emission_counts[tag]
})
tag_states[tag] = State(tag_emissions, name=tag)
basic_model.add_state(tag_states[tag])
# Add edges between states for the observed transition frequencies P(tag_i | tag_i-1)
for tag in data.training_set.tagset:
basic_model.add_transition(basic_model.start, tag_states[tag],
tag_starts[tag] / tag_unigrams[tag])
for tag1 in data.training_set.tagset:
basic_model.add_transition(
tag_states[tag], tag_states[tag1],
tag_bigrams[(tag, tag1)] / tag_unigrams[tag])
basic_model.add_transition(tag_states[tag], basic_model.end,
tag_ends[tag] / tag_unigrams[tag])
# finalize the model
basic_model.bake()
acceptor0_data = classify(matrixAcceptor0, 2)
no_coding_dist = calculator.intron_calculator('cuts_intron.txt').p
donor_states = sequence_state_factory(donor0_data, 'donor0')
acceptor_states = sequence_state_factory(acceptor0_data, 'acceptor0')
intron_spacer_states = spacer_states_maker(10, no_coding_dist, 'intron spacer')
utr_model = HiddenMarkovModel('utr_model')
# States
exon_state = State(DiscreteDistribution(calculator.utr_exon_5('mcutsa.txt').p),
name='utr exon')
intron_state = State(DiscreteDistribution(no_coding_dist), name='utr intron')
utr_model.add_model(promoter_model)
utr_model.add_state(exon_state)
utr_model.add_state(intron_state)
add_sequence(utr_model, donor_states)
add_sequence(utr_model, acceptor_states)
add_sequence(utr_model, intron_spacer_states)
utr_model.add_transition(utr_model.start, get_state(promoter_model, 'back'), 1)
utr_model.add_transition(get_state(promoter_model, 'inr7'), exon_state, 1)
utr_model.add_transition(get_state(promoter_model, 'no inr7'), exon_state, 1)
utr_model.add_transition(exon_state, exon_state, 0.7)
utr_model.add_transition(exon_state, donor_states[0], 0.2)
utr_model.add_transition(exon_state, utr_model.end, 0.1)
utr_model.add_transition(donor_states[-1], intron_state, 1)
