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"
def test_train_visible(self):
states = ["0", "1", "2", "3"]
alphabet = ["A", "C", "G", "T"]
training_data = [
("AACCCGGGTTTTTTT", "001112223333333"),
("ACCGTTTTTTT", "01123333333"),
("ACGGGTTTTTT", "01222333333"),
("ACCGTTTTTTTT", "011233333333"),
]
markov_model = MarkovModel.train_visible(states, alphabet, training_data)
states = MarkovModel.find_states(markov_model, "AACGTT")
self.assertEqual(len(states), 1)
state_list, state_float = states[0]
self.assertEqual(state_list, ['0', '0', '1', '2', '3', '3'])
self.assertAlmostEqual(state_float, 0.0082128906)
self.assertEqual(markov_model.states, ['0', '1', '2', '3'])
self.assertEqual(markov_model.alphabet, ['A', 'C', 'G', 'T'])
self.assertEqual(len(markov_model.p_initial), 4)
self.assertAlmostEqual(markov_model.p_initial[0], 1.0)
self.assertAlmostEqual(markov_model.p_initial[1], 0.0)
self.assertAlmostEqual(markov_model.p_initial[2], 0.0)
self.assertAlmostEqual(markov_model.p_initial[3], 0.0)
self.assertEqual(len(markov_model.p_transition), 4)
self.assertEqual(len(markov_model.p_transition[0]), 4)
self.assertEqual(len(markov_model.p_transition[1]), 4)
self.assertEqual(len(markov_model.p_transition[2]), 4)
self.assertEqual(len(markov_model.p_transition[3]), 4)
self.assertAlmostEqual(markov_model.p_transition[0][0], 0.2)
self.assertAlmostEqual(markov_model.p_transition[0][1], 0.8)
self.assertAlmostEqual(markov_model.p_transition[0][2], 0.0)
self.assertAlmostEqual(markov_model.p_transition[0][3], 0.0)
self.assertAlmostEqual(markov_model.p_transition[1][0], 0.0)
self.assertAlmostEqual(markov_model.p_transition[1][1], 0.5)
self.assertAlmostEqual(markov_model.p_transition[1][2], 0.5)
self.assertAlmostEqual(markov_model.p_transition[1][3], 0.0)
self.assertAlmostEqual(markov_model.p_transition[2][0], 0.0)
self.assertAlmostEqual(markov_model.p_transition[2][1], 0.0)
self.assertAlmostEqual(markov_model.p_transition[2][2], 0.5)
self.assertAlmostEqual(markov_model.p_transition[2][3], 0.5)
self.assertAlmostEqual(markov_model.p_transition[3][0], 0.0)
self.assertAlmostEqual(markov_model.p_transition[3][1], 0.0)
self.assertAlmostEqual(markov_model.p_transition[3][2], 0.0)
self.assertAlmostEqual(markov_model.p_transition[3][3], 1.0)
self.assertEqual(len(markov_model.p_emission), 4)
self.assertEqual(len(markov_model.p_emission[0]), 4)
self.assertEqual(len(markov_model.p_emission[1]), 4)
self.assertEqual(len(markov_model.p_emission[2]), 4)
self.assertEqual(len(markov_model.p_emission[3]), 4)
self.assertAlmostEqual(markov_model.p_emission[0][0], 0.666667,
places=4)
self.assertAlmostEqual(markov_model.p_emission[0][1], 0.111111,
places=4)
self.assertAlmostEqual(markov_model.p_emission[0][2], 0.111111,
places=4)
self.assertAlmostEqual(markov_model.p_emission[0][3], 0.111111,
places=4)
self.assertAlmostEqual(markov_model.p_emission[1][0], 0.083333,
places=4)
self.assertAlmostEqual(markov_model.p_emission[1][1], 0.750000,
places=4)
self.assertAlmostEqual(markov_model.p_emission[1][2], 0.083333,
places=4)
self.assertAlmostEqual(markov_model.p_emission[1][3], 0.083333,
places=4)
self.assertAlmostEqual(markov_model.p_emission[2][0], 0.083333,
places=4)
self.assertAlmostEqual(markov_model.p_emission[2][1], 0.083333,
places=4)
self.assertAlmostEqual(markov_model.p_emission[2][2], 0.750000,
places=4)
self.assertAlmostEqual(markov_model.p_emission[2][3], 0.083333,
places=4)
self.assertAlmostEqual(markov_model.p_emission[3][0], 0.031250,
places=4)
self.assertAlmostEqual(markov_model.p_emission[3][1], 0.031250,
places=4)
self.assertAlmostEqual(markov_model.p_emission[3][2], 0.031250,
places=4)
self.assertAlmostEqual(markov_model.p_emission[3][3], 0.906250,
places=4)
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"
