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 ])