def extract_features_from_images(self): if self.train_model: num_samples = 10000 else: num_samples = 10 # Take random samples from the images to train the model random_idxs = np.random.randint(0, len(self.cars), num_samples) test_cars = np.array(self.cars)[random_idxs] test_notcars = np.array(self.notcars)[random_idxs] print("Extracting Car Features") fE_cars = featureExtractor() car_features = fE_cars.get_features_from_images( test_cars, self.size, self.nbins, self.orientation, self.pixels_per_cell, self.cell_per_block) print("Extracting Non Car Features") fE_notcars = featureExtractor() not_car_features = fE_notcars.get_features_from_images( test_notcars, self.size, self.nbins, self.orientation, self.pixels_per_cell, self.cell_per_block) return car_features, not_car_features
def getReviewSentiment(app, tknRevs, classifier): revAggSentiment = 0 for revList in tknRevs: sentAggSentiment = 0 for sent in revList: sent = unicode(sent.strip()) # print sent featdata = extractor.featureExtractor(sent) # pprint(featdata) #pdb.set_trace() cl = classifier.classify(featdata) if cl == 'pos': label = 1 elif cl == 'neutral': label = 0 else: label = -1 sentAggSentiment += label revAggSentiment += sentAggSentiment name = app['name'].encode('utf-8') print "App: \t %s, Aggregate Review Sentiment: \t %s" % (name, revAggSentiment) return revAggSentiment
def getReviewSentiment(app, tknRevs, classifier): revAggSentiment = 0 for revList in tknRevs: sentAggSentiment = 0 for sent in revList: sent = unicode(sent.strip()) # print sent featdata = extractor.featureExtractor(sent) # pprint(featdata) #pdb.set_trace() cl= classifier.classify(featdata) if cl == 'pos': label = 1 elif cl == 'neutral': label = 0 else: label = -1 sentAggSentiment += label revAggSentiment += sentAggSentiment name = app['name'].encode('utf-8') print "App: \t %s, Aggregate Review Sentiment: \t %s" % (name , revAggSentiment) return revAggSentiment
def getReviewSentiment(tknRevs, classifier): revAggSentiment = 0 for sent in tknRevs: sent = unicode(sent.strip()) featdata = extractor.featureExtractor(sent) cl = classifier.classify(featdata) if cl == 'pos': label = 1 elif cl == 'neutral': label = 0 else: label = -1 revAggSentiment += label return revAggSentiment
def getReviewSentiment(tknRevs, classifier): revAggSentiment = 0 for sent in tknRevs: sent = unicode(sent.strip()) featdata = extractor.featureExtractor(sent) cl= classifier.classify(featdata) if cl == 'pos': label = 1 elif cl == 'neutral': label = 0 else: label = -1 revAggSentiment += label return revAggSentiment