import numpy
from pomegranate import State
from pomegranate import DiscreteDistribution
from pomegranate import HiddenMarkovModel
import calculator
from converter_to import converter_to
from model_maker_utils import sequence_state_factory
from model_maker_utils import classify
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)
import numpy
from pomegranate import State
from pomegranate import DiscreteDistribution
from pomegranate import HiddenMarkovModel
import calculator
from converter_to import converter_to
from model_maker_utils import sequence_state_factory
from model_maker_utils import classify
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('../data extractors/new_acceptor1.exa'))
acceptor0_data = classify(matrixAcceptor0, 2)
model = HiddenMarkovModel('intron_acceptor')
intron = State(DiscreteDistribution(
calculator.intron_calculator('../data extractors/new_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)
import itertools
import calculator
from model_maker_utils import sequence_state_factory
from model_maker_utils import classify
from model_maker_utils import add_sequence
from model_maker_utils import spacer_states_maker
from model_maker_utils import percentage_matrix_maker
from stop_example_divider import divider as stop_divider
def foo(l):
yield from itertools.product(*([l] * 3))
c0, c1, c2 = calculator.calculate_proba2('cuts.txt')
matrixZE = numpy.array(matrix_from_exa('new_tss.exa'))
# matrixEZ = numpy.array(matrix_from_exa('new_tts.exa'))
taa_matrix, tga_matrix, tag_matrix = stop_divider('new_tts.exa')
matrixDonor0 = numpy.array(matrix_from_exa('new_donor0.exa'))
matrixDonor1 = numpy.array(matrix_from_exa('new_donor1.exa'))
matrixDonor2 = numpy.array(matrix_from_exa('new_donor2.exa'))
matrixAcceptor0 = numpy.array(matrix_from_exa('new_acceptor0.exa'))
matrixAcceptor1 = numpy.array(matrix_from_exa('new_acceptor1.exa'))
matrixAcceptor2 = numpy.array(matrix_from_exa('new_acceptor2.exa'))
polyASeqs = [('AATAAA', 592), ('ATTAAA', 149), ('AGTAAA', 27), ('TATAAA', 32),
('CATAAA', 13), ('GATAAA', 13), ('AATATA', 17), ('AATACA', 12),
('AATAGA', 7), ('ACTAAA', 6), ('AAGAAA', 11), ('AATGAA', 8)]
import numpy
from pomegranate import State
from pomegranate import DiscreteDistribution
from pomegranate import HiddenMarkovModel
import calculator
from model_maker_utils import sequence_state_factory, classify, add_sequence, equal_distribution
from matrix_from_aln import matrix_from_exa
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)
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())
import numpy
from pomegranate import State
from pomegranate import DiscreteDistribution
from pomegranate import HiddenMarkovModel
import calculator
from stop_example_divider import divider as stop_divider
from model_maker_utils import sequence_state_factory, classify, add_sequence, equal_distribution
from matrix_from_aln import matrix_from_exa
from converter_to import converter_to
c0, c1, c2 = calculator.calculate_proba2('cuts.txt')
matrixStop = numpy.array(matrix_from_exa('new_tts.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')
taa_matrix, tga_matrix, tag_matrix = stop_divider('new_tts.exa')
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)
for x_line in in_file:
test_line = x_line.lower().replace('\n', '').replace(' ', '')
tonight = converter_to(test_line, 2)
logp, path = model.viterbi(tonight)
path = [x[1].name for i, x in enumerate(path) if i < len(tonight)]
if path[48] == 'start zone7':
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)
if path_names[-50] == 'donor04':
oks += 1
else:
noks += 1
print(oks / (oks + noks))
with open('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('new_donor1.exa'))
c0, c1, c2 = calculator.calculate_proba2('cuts.txt')
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('codiing to donor')
model.add_state(coding_state0)
for x_line in in_file:
test_line = x_line.lower().replace('\n', '').replace(' ', '')
tonight = converter_to(test_line, 2)
logp, path = model.viterbi(tonight)
path = [x[1].name for i, x in enumerate(path) if i < len(tonight)]
if path[48] == 'start zone7':
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('new_tss.exa'))
start_states_data = classify(matrixZE, 2)
start_states = sequence_state_mulfactory(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)
from model_maker_utils import add_variable_length_sequence
from model_maker_utils import load_long_training_examples
from model_maker_utils import get_state
from converter_to import converter_to
import calculator
from pomegranate import State
from pomegranate import HiddenMarkovModel
from pomegranate import DiscreteDistribution
from matrix_from_aln import matrix_from_exa
with open('promoter_utr_model_base.json') as base_model_file:
promoter_model_json = base_model_file.read()
promoter_model = HiddenMarkovModel.from_json(promoter_model_json)
matrixDonor0 = numpy.array(matrix_from_exa('new_donor0.exa'))
matrixAcceptor0 = numpy.array(matrix_from_exa('new_acceptor0.exa'))
donor0_data = classify(matrixDonor0, 2)
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')