def create_mutable_model(self, model):
"""
Creates a mutable version of the given model. This mutable version
will be the model that receives updates during training, as defined
by L{update_model}.
"""
return DictionaryHmmModel.from_ngram_model(model, mutable=True)
def create_mutable_model(self, model):
"""
Creates a mutable version of the given model. This mutable version
will be the model that receives updates during training, as defined
by L{update_model}.
"""
return DictionaryHmmModel.from_ngram_model(model, mutable=True)
def clear_cache(self):
"""
Initializes or empties probability distribution caches.
Make sure to call this if you change or update the distributions.
No caches used thus far, so this does nothing for now, except call
the super method.
"""
self._small_transition_matrix_cache = None
self._small_transition_matrix_cache_trans = None
return DictionaryHmmModel.clear_cache(self)
def setUp(self):
"""
Prepare some training data.
"""
self.TRAINING_DATA = [
[0, 5, 5, 7, 6, 7, 8, 5, 2, 0, 3, 1, 2, 2, 2, 9, 9, 8, 0, 8, 9, 9, 1, 3, 2, 2, 1],
[3, 3, 1, 2, 1, 1, 0, 1, 9, 7, 8, 7, 7, 9, 0],
[7, 8, 6, 9, 8, 9, 9, 1, 3, 0, 1, 3, 0, 1, 1, 0, 5, 7, 5, 4, 5, 7, 7]
]
self.TEST_DATA = [0, 1, 2, 3, 4, 3, 5, 6, 7, 8, 8, 9, 7, 7, 0, 0, 1]
ems = list(range(10))
states = ['H', 'M', 'L']
# Construct some initial distributions
# Emission
hprobs = {
0:0.0, 1:0.0, 2:0.0, 3:0.0, 4:0.0, 5:0.0, 6:0.1, 7:0.3,
8:0.3, 9:0.3 }
mprobs = {
0:0.0, 1:0.0, 2:0.0, 3:0.1, 4:0.3, 5:0.3, 6:0.3, 7:0.0,
8:0.0, 9:0.0 }
lprobs = {
0:0.2, 1:0.2, 2:0.2, 3:0.2, 4:0.2, 5:0.0, 6:0.0, 7:0.0,
8:0.0, 9:0.0 }
conddist = {
'H' : DictionaryProbDist(hprobs),
'M' : DictionaryProbDist(mprobs),
'L' : DictionaryProbDist(lprobs),
}
emdist = DictionaryConditionalProbDist(conddist)
# And transition
conddist = {}
for first in states+[None]:
probs = dict([(second, 1.0/3) for second in states+[None]])
dist = DictionaryProbDist(probs)
conddist[(first,)] = dist
transdist = DictionaryConditionalProbDist(conddist)
# Initialize an ngram model with these distributions
self.model = DictionaryHmmModel(transdist, emdist, states, ems)
def setUp(self):
"""
Prepare some training data.
"""
self.TRAINING_DATA = [[
0, 5, 5, 7, 6, 7, 8, 5, 2, 0, 3, 1, 2, 2, 2, 9, 9, 8, 0, 8, 9, 9,
1, 3, 2, 2, 1
], [3, 3, 1, 2, 1, 1, 0, 1, 9, 7, 8, 7, 7, 9, 0],
[
7, 8, 6, 9, 8, 9, 9, 1, 3, 0, 1, 3, 0, 1, 1,
0, 5, 7, 5, 4, 5, 7, 7
]]
self.TEST_DATA = [0, 1, 2, 3, 4, 3, 5, 6, 7, 8, 8, 9, 7, 7, 0, 0, 1]
ems = list(range(10))
states = ['H', 'M', 'L']
# Construct some initial distributions
# Emission
hprobs = {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.1,
7: 0.3,
8: 0.3,
9: 0.3
}
mprobs = {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.1,
4: 0.3,
5: 0.3,
6: 0.3,
7: 0.0,
8: 0.0,
9: 0.0
}
lprobs = {
0: 0.2,
1: 0.2,
2: 0.2,
3: 0.2,
4: 0.2,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0
}
conddist = {
'H': DictionaryProbDist(hprobs),
'M': DictionaryProbDist(mprobs),
'L': DictionaryProbDist(lprobs),
}
emdist = DictionaryConditionalProbDist(conddist)
# And transition
conddist = {}
for first in states + [None]:
probs = dict([(second, 1.0 / 3) for second in states + [None]])
dist = DictionaryProbDist(probs)
conddist[(first, )] = dist
transdist = DictionaryConditionalProbDist(conddist)
# Initialize an ngram model with these distributions
self.model = DictionaryHmmModel(transdist, emdist, states, ems)
class TestTrain(unittest.TestCase):
def setUp(self):
"""
Prepare some training data.
"""
self.TRAINING_DATA = [[
0, 5, 5, 7, 6, 7, 8, 5, 2, 0, 3, 1, 2, 2, 2, 9, 9, 8, 0, 8, 9, 9,
1, 3, 2, 2, 1
], [3, 3, 1, 2, 1, 1, 0, 1, 9, 7, 8, 7, 7, 9, 0],
[
7, 8, 6, 9, 8, 9, 9, 1, 3, 0, 1, 3, 0, 1, 1,
0, 5, 7, 5, 4, 5, 7, 7
]]
self.TEST_DATA = [0, 1, 2, 3, 4, 3, 5, 6, 7, 8, 8, 9, 7, 7, 0, 0, 1]
ems = list(range(10))
states = ['H', 'M', 'L']
# Construct some initial distributions
# Emission
hprobs = {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.1,
7: 0.3,
8: 0.3,
9: 0.3
}
mprobs = {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.1,
4: 0.3,
5: 0.3,
6: 0.3,
7: 0.0,
8: 0.0,
9: 0.0
}
lprobs = {
0: 0.2,
1: 0.2,
2: 0.2,
3: 0.2,
4: 0.2,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0
}
conddist = {
'H': DictionaryProbDist(hprobs),
'M': DictionaryProbDist(mprobs),
'L': DictionaryProbDist(lprobs),
}
emdist = DictionaryConditionalProbDist(conddist)
# And transition
conddist = {}
for first in states + [None]:
probs = dict([(second, 1.0 / 3) for second in states + [None]])
dist = DictionaryProbDist(probs)
conddist[(first, )] = dist
transdist = DictionaryConditionalProbDist(conddist)
# Initialize an ngram model with these distributions
self.model = DictionaryHmmModel(transdist, emdist, states, ems)
def test_init_model(self):
"""
Check that the initialized model is doing something sensible.
"""
# We don't check these, just that they're not generating errors:
# that would really confuse the rest of it
self.model.emission_probability(2, 'H')
self.model.emission_probability(6, 'H')
self.model.transition_probability('H', 'L')
self.model.transition_probability('H', 'H')
def test_init_decode(self):
"""
Try running the viterbi decoder using the initial model.
"""
self.model.viterbi_decode(self.TEST_DATA)
def test_baum_welch(self):
"""
Runs the Baum Welch trainer using the training data.
"""
options = BaumWelchTrainer.process_option_dict({})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
model = trainer.model
# Try decoding using the trained model to check it still works
model.viterbi_decode(self.TEST_DATA)
def test_baum_welch_mp(self):
"""
Does the same as L{test_baum_welch}, but uses multiprocessing.
"""
options = BaumWelchTrainer.process_option_dict({'trainprocs': -1})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
class TestTrain(unittest.TestCase):
def setUp(self):
"""
Prepare some training data.
"""
self.TRAINING_DATA = [
[0, 5, 5, 7, 6, 7, 8, 5, 2, 0, 3, 1, 2, 2, 2, 9, 9, 8, 0, 8, 9, 9, 1, 3, 2, 2, 1],
[3, 3, 1, 2, 1, 1, 0, 1, 9, 7, 8, 7, 7, 9, 0],
[7, 8, 6, 9, 8, 9, 9, 1, 3, 0, 1, 3, 0, 1, 1, 0, 5, 7, 5, 4, 5, 7, 7]
]
self.TEST_DATA = [0, 1, 2, 3, 4, 3, 5, 6, 7, 8, 8, 9, 7, 7, 0, 0, 1]
ems = list(range(10))
states = ['H', 'M', 'L']
# Construct some initial distributions
# Emission
hprobs = {
0:0.0, 1:0.0, 2:0.0, 3:0.0, 4:0.0, 5:0.0, 6:0.1, 7:0.3,
8:0.3, 9:0.3 }
mprobs = {
0:0.0, 1:0.0, 2:0.0, 3:0.1, 4:0.3, 5:0.3, 6:0.3, 7:0.0,
8:0.0, 9:0.0 }
lprobs = {
0:0.2, 1:0.2, 2:0.2, 3:0.2, 4:0.2, 5:0.0, 6:0.0, 7:0.0,
8:0.0, 9:0.0 }
conddist = {
'H' : DictionaryProbDist(hprobs),
'M' : DictionaryProbDist(mprobs),
'L' : DictionaryProbDist(lprobs),
}
emdist = DictionaryConditionalProbDist(conddist)
# And transition
conddist = {}
for first in states+[None]:
probs = dict([(second, 1.0/3) for second in states+[None]])
dist = DictionaryProbDist(probs)
conddist[(first,)] = dist
transdist = DictionaryConditionalProbDist(conddist)
# Initialize an ngram model with these distributions
self.model = DictionaryHmmModel(transdist, emdist, states, ems)
def test_init_model(self):
"""
Check that the initialized model is doing something sensible.
"""
# We don't check these, just that they're not generating errors:
# that would really confuse the rest of it
self.model.emission_probability(2, 'H')
self.model.emission_probability(6, 'H')
self.model.transition_probability('H','L')
self.model.transition_probability('H','H')
def test_init_decode(self):
"""
Try running the viterbi decoder using the initial model.
"""
self.model.viterbi_decode(self.TEST_DATA)
def test_baum_welch(self):
"""
Runs the Baum Welch trainer using the training data.
"""
options = BaumWelchTrainer.process_option_dict({})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
model = trainer.model
# Try decoding using the trained model to check it still works
model.viterbi_decode(self.TEST_DATA)
def test_baum_welch_mp(self):
"""
Does the same as L{test_baum_welch}, but uses multiprocessing.
"""
options = BaumWelchTrainer.process_option_dict({'trainprocs':-1})
trainer = BaumWelchTrainer(self.model, options)
# Train the model with Baum Welch
trainer.train(self.TRAINING_DATA)
