def train_until_stop_condition_reached(self, word_hmm):
examples = generate_examples_for_word(word="dog", number_of_examples=500)
test_examples = generate_examples_for_word(word="dog", number_of_examples=40)
before = word_hmm.test(test_examples)
word_hmm.train_until_stop_condition_reached(examples, delta = 0.0, test_examples = test_examples)
after = word_hmm.test(test_examples)
if(after > before):
print("test_train_until_stop_condition_reached", "before", before, "after", after)
pass
else:
raise "The training does not seem to work good before " + str(before) + " after " + str(after)
def train_until_stop_condition_reached(self, word_hmm):
examples = generate_examples_for_word(word="dog",
number_of_examples=500)
test_examples = generate_examples_for_word(word="dog",
number_of_examples=40)
before = word_hmm.test(test_examples)
word_hmm.train_until_stop_condition_reached(
examples, delta=0.0, test_examples=test_examples)
after = word_hmm.test(test_examples)
if (after > before):
print("test_train_until_stop_condition_reached", "before", before,
"after", after)
pass
else:
raise "The training does not seem to work good before " + str(
before) + " after " + str(after)
def test_train(self):
word_hmm = WordHMM(word_length=3)
examples = generate_examples_for_word(word="dog", number_of_examples=1000)
test_examples = generate_examples_for_word(word="dog", number_of_examples=10)
other_test_examples = generate_examples_for_word(word="pig", number_of_examples=10)
before = word_hmm.test(test_examples)
word_hmm.train_baum_welch(examples)
after = word_hmm.test(test_examples)
other_test_examples_test = word_hmm.test(other_test_examples)
if(after > before and other_test_examples_test < after):
print("test train", "before", before, "after", after)
pass
else:
raise "The training does not seem to work good"
print(["before", before, "after", after,"other_test_examples_test", other_test_examples_test])
def test_train_until_stop_condition_reached(self):
print("random init")
self.train_until_stop_condition_reached(WordHMM(word_length=3))
print("count based init")
init_training_examples = generate_examples_for_word(word="dog", number_of_examples=40)
self.train_until_stop_condition_reached(WordHMM(3,
SpecializedHMM.InitMethod.count_based,
init_training_examples))
def test_train_until_stop_condition_reached(self):
print("random init")
self.train_until_stop_condition_reached(WordHMM(word_length=3))
print("count based init")
init_training_examples = generate_examples_for_word(
word="dog", number_of_examples=40)
self.train_until_stop_condition_reached(
WordHMM(3, SpecializedHMM.InitMethod.count_based,
init_training_examples))
def test_train(self):
word_hmm = WordHMM(word_length=3)
examples = generate_examples_for_word(word="dog",
number_of_examples=1000)
test_examples = generate_examples_for_word(word="dog",
number_of_examples=10)
other_test_examples = generate_examples_for_word(word="pig",
number_of_examples=10)
before = word_hmm.test(test_examples)
word_hmm.train_baum_welch(examples)
after = word_hmm.test(test_examples)
other_test_examples_test = word_hmm.test(other_test_examples)
if (after > before and other_test_examples_test < after):
print("test train", "before", before, "after", after)
pass
else:
raise "The training does not seem to work good"
print([
"before", before, "after", after, "other_test_examples_test",
other_test_examples_test
])
