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# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
try:
from numpy import asarray
except ImportError:
from Bio import MissingExternalDependencyError
raise MissingExternalDependencyError(\
"Install NumPy if you want to use Bio.MarkovModel.")
from Bio import MarkovModel
def print_mm(markov_model):
print "STATES: %s" % ' '.join(markov_model.states)
print "ALPHABET: %s" % ' '.join(markov_model.alphabet)
print "INITIAL:"
for i in range(len(markov_model.p_initial)):
print " %s: %.2f" % (
markov_model.states[i], markov_model.p_initial[i])
print "TRANSITION:"
for i in range(len(markov_model.p_transition)):
x = ["%.2f" % x for x in markov_model.p_transition[i]]
print " %s: %s" % (markov_model.states[i], ' '.join(x))
print "EMISSION:"
for i in range(len(markov_model.p_emission)):
x = ["%.2f" % x for x in markov_model.p_emission[i]]
print " %s: %s" % (markov_model.states[i], ' '.join(x))
print "TESTING train_visible"
states = ["0", "1", "2", "3"]
alphabet = ["A", "C", "G", "T"]
training_data = [
("AACCCGGGTTTTTTT", "001112223333333"),
("ACCGTTTTTTT", "01123333333"),
("ACGGGTTTTTT", "01222333333"),
("ACCGTTTTTTTT", "011233333333"),
]
print "Training HMM"
mm = MarkovModel.train_visible(states, alphabet, training_data)
print "Classifying"
#print MarkovModel.find_states(mm, "AACGTT")
#Don't just print this, as the float may have different
#precision on different platforms. This returns a list
#containing a tuple containing a list (fine), and a float.
states = MarkovModel.find_states(mm, "AACGTT")
for state_list, state_float in states:
print "State %s, %0.10f" % (repr(state_list), state_float)
print_mm(mm)
print "TESTING baum welch"
states = ["CP", "IP"]
alphabet = ["cola", "ice_t", "lem"]
outputs = [
(2, 1, 0)
]
print "Training HMM"
p_initial = [1.0, 0.0000001]
p_transition = [[0.7, 0.3],
[0.5, 0.5]]
p_emission = [[0.6, 0.1, 0.3],
[0.1, 0.7, 0.2]]
N, M = len(states), len(alphabet)
x = MarkovModel._baum_welch(N, M, outputs,
p_initial=p_initial,
p_transition=p_transition,
p_emission=p_emission
)
p_initial, p_transition, p_emission = x
mm = MarkovModel.MarkovModel(states, alphabet,
p_initial, p_transition, p_emission)
print_mm(mm)
# Test Baum-Welch. This is hard because it is a non-deterministic
# algorithm. Each run will result in different states having to
# different emissions. In order to help this, we need to specify some
# initial probabilities to bias the final results. This is not
# implemented yet in the MarkovModel module.
## states = [
## "state0",
## "state1",
## "state2",
## "state3",
## ]
## alphabet = ["a", "c", "g", "t"]
## training_data = [
## "aacccgggttttttt",
## "accgttttttt",
## "acgggtttttt",
## "accgtttttttt",
## "aaccgtttttttt",
## "aacggttttt",
## "acccggttttt",
## "acccggggttt",
## "aacccggggtttt",
## "aaccgggtttttt"
## ]
## print "TRAINING HMM"
## ep = ones((len(states), len(alphabet)))
## hmm = MarkovModel.train_bw(states, alphabet, training_data,
## pseudo_emission=ep)
## print "CLASSIFYING"
## states = MarkovModel.find_states(hmm, "aacgtt")
## print states
## print "STATES: %s" % ' '.join(hmm.states)
## print "ALPHABET: %s" % ' '.join(hmm.alphabet)
## print "INITIAL:"
## for i in range(len(hmm.p_initial)):
## print " %s: %.2f" % (hmm.states[i], hmm.p_initial[i])
## print "TRANSITION:"
## for i in range(len(hmm.p_transition)):
## x = ["%.2f" % x for x in hmm.p_transition[i]]
## print " %s: %s" % (hmm.states[i], ' '.join(x))
## print "EMISSION:"
## for i in range(len(hmm.p_emission)):
## x = ["%.2f" % x for x in hmm.p_emission[i]]
## print " %s: %s" % (hmm.states[i], ' '.join(x))
# Do some tests from the topcoder competition.
class DNAStrand:
def mostLikely(self, normal, island, dnastrand):
states = "NR"
alphabet = "AGTC"
normal = [float(x)/100 for x in normal]
island = [float(x)/100 for x in island]
p_initial = [1.0, 0.0]
p_initial = asarray(p_initial)
p_transition = []
p_transition.append([1.0-normal[-1], normal[-1]])
p_transition.append([island[-1], 1.0-island[-1]])
p_transition = asarray(p_transition)
p_emission = [] # 2x4 matrix
p_emission.append(normal[:4])
p_emission.append(island[:4])
p_emission = asarray(p_emission)
mm = MarkovModel.MarkovModel(
states, alphabet, p_initial, p_transition, p_emission)
x = MarkovModel.find_states(mm, dnastrand)
states, x = x[0]
return ''.join(states)
ds = DNAStrand()
# NNNN
print ds.mostLikely([30, 20, 30, 20, 10],
[10, 40, 10, 40, 20],
"TGCC"
)
# NNNRRRNNRRNRRN
print ds.mostLikely([4, 14, 62, 20, 44],
[39, 15, 4, 42, 25],
"CCTGAGTTAGTCGT"
)
# NRRRRRRRRRRRNNNNRRRRRRRRR
print ds.mostLikely([45, 36, 6, 13, 25],
[24, 18, 12, 46, 25],
"CCGTACTTACCCAGGACCGCAGTCC"
)
# NRRRRRRRRRR
print ds.mostLikely([75,3,1,21,45],
[34,11,39,16,15],
"TTAGCAGTGCG"
)
# N
print ds.mostLikely([26,37,8,29,16],
[31,13,33,23,25],
"T"
)
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