def fit(self, x, y):
digits = []
labels = []
for image, param_labels in zip(x, y):
separator = DigitSeparator(image)
digits.extend(map(self.feature_extractor, separator.get_digits()))
labels.extend(param_labels)
self.vectorizer = DictVectorizer()
train_array = self.vectorizer.fit_transform(digits).toarray()
self.engine.fit(train_array, labels)
def __make_prediction(self, image):
separator = DigitSeparator(image)
features = map(self.feature_extractor, separator.get_digits())
digits = self.vectorizer.transform(features).toarray()
labels = self.engine.predict(digits)
return ''.join(map(lambda x: '%d' % x, labels))
