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())
from matrix_from_aln import matrix_from_exa
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)