def test_test_score(self):
words = ["pig","dog","cat","bee","ape","elk","hen","cow"]
examples = generate_examples_for_words(words,number_of_examples=200)
classifier = WordClassifier(examples,
nr_of_hmms_to_try = 1,
fraction_of_examples_for_test = 0.3,
train_with_examples=False)
test_examples = generate_examples_for_words(words,number_of_examples=200)
before = classifier.test(test_examples)
classifier.train()
after = classifier.test(test_examples)
print("test_test_score", "before", before, "after", after)
def get_examples(nr_of_examples):
return generate_examples_for_words(
words=word_list,
number_of_examples=nr_of_examples,
poelap=0.03,
poelenl=0.7,
powlap=0.1,
polmap=0.03)
def test_create_classifier(self):
words = ["pig","dog","cat","bee","ape","elk","hen","cow"]
examples = generate_examples_for_words(words,number_of_examples=1000)
classifier = WordClassifier(examples,
nr_of_hmms_to_try = 1,
fraction_of_examples_for_test = 0)
def test_classify(word):
print("classification of " + word + " = "+ classifier.classify(word))
#test
map(test_classify, words)
test_examples = ["iig","dag","catt","bae","appe","elck","hel","row"]
map(test_classify, test_examples)
pass
def create_word_classifier(word_list, save_to_file_path):
training_examples = generate_examples_for_words(words=word_list,
number_of_examples=800,
poelap=0.03,
poelenl=0.7,
powlap=0.1,
polmap=0.03)
classifier = WordClassifier(training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=True,
initialisation_method=SpecializedHMM.InitMethod.count_based)
classifier_string = classifier.to_string()
file = open(save_to_file_path,'w')
file.write(classifier_string)
file.close()
def create_word_classifier(word_list, save_to_file_path):
training_examples = generate_examples_for_words(words=word_list,
number_of_examples=800,
poelap=0.03,
poelenl=0.7,
powlap=0.1,
polmap=0.03)
classifier = WordClassifier(
training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=True,
initialisation_method=SpecializedHMM.InitMethod.count_based)
classifier_string = classifier.to_string()
file = open(save_to_file_path, 'w')
file.write(classifier_string)
file.close()
def get_examples(nr_of_examples):
return generate_examples_for_words(
words=word_list, number_of_examples=nr_of_examples, poelap=0.03, poelenl=0.7, powlap=0.1, polmap=0.03
)
