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 )