def test_feedback(self): test_file = "test_feedback.txt" feedback_file = MachineLoader.feedback(machines.number_recognizer, None, file_name=test_file) if os.path.isfile(feedback_file): os.remove(feedback_file) data = [0] * 64 target = [0] feedback = target + data # create file MachineLoader.feedback(machines.number_recognizer, feedback, file_name=test_file) # append file MachineLoader.feedback(machines.number_recognizer, feedback, file_name=test_file) with open(feedback_file, mode="rb") as r: lines = r.readlines() self.assertEqual(2, len(lines)) os.remove(feedback_file)
def post(self): data = self.get_arguments("data[]") result = "" feedback = Validator.validate_feedback(data) if len(feedback) > 0: MachineLoader.feedback(machines.number_recognizer, feedback) else: result = "feedback format is wrong." resp = {"result": result} self.write(resp)
def post(self): resp = {"result": str(-1)} data = self.get_arguments("data[]") validated = Validator.validate_data(data) machine = MachineLoader.load(machines.number_recognizer) #save train data # with open('C:/Github/number_recognizer/train_1.txt','a') as thefile: # for item in validated: # thefile.write("%s\t" %item) # thefile.write("\n") #save target data # b = self.get_arguments("b") # with open('C:/Github/number_recognizer/target_1.txt','a') as thefile: # thefile.write(b[0]) # thefile.write("\n") if len(validated) > 0: predicted = machine.predict(validated) resp["result"] = str(predicted[0]) self.write(resp)
def post(self): resp = {"result": str(-1)} data = self.get_arguments("data[]") new_number= self.get_arguments("new_number") print(new_number) validated = Validator.validate_data(data) machine = MachineLoader.load(machines.number_recognizer) with open('C:/git_number/number_recognizer/data_text.txt','a') as thefile: for item in validated: thefile.write("%s" % item) thefile.write("\n") with open('C:/git_number/number_recognizer/new_number.txt','a') as thefile: for item in new_number: thefile.write("%s" % item) thefile.write("\n") if len(validated) > 0: predicted = machine.predict(validated) resp["result"] = str(predicted[0]) self.write(resp)
def post(self): data = self.get_arguments("data[]") result = "" feedback = Validator.validate_feedback(data) if len(feedback) > 0: # save feedback to file MachineLoader.feedback(machines.number_recognizer, feedback) # online training machine = MachineLoader.load(machines.number_recognizer) machine.partial_fit(feedback[1:], [feedback[0]]) MachineLoader.save(machines.number_recognizer, machine) else: result = "feedback format is wrong." resp = {"result": result} self.write(resp)
def post(self): resp = {"result": str(-1)} data = self.get_arguments("data[]") validated = Validator.validate_data(data) machine = MachineLoader.load(machines.number_recognizer) if len(validated) > 0: predicted = machine.predict(np.array(validated).reshape(1, -1)) resp["result"] = str(predicted[0]) self.write(resp)
def images_to_mood(self, image_urls): import machines.photo_mood machine = MachineLoader.load(machines.photo_mood) mood_score = {} for url in image_urls: score = self.rekognize(url) mood = machine.predict(score)[0] mood_score[self.MOODS[int(mood)]] = 1 return mood_score
def post(self): resp = {"result": str(-1)} data = self.get_arguments("data[]") validated = Validator.validate_data(data) machine = MachineLoader.load(machines.number_recognizer) if len(validated) > 0: predicted = machine.predict(validated) resp["result"] = str(predicted[0]) self.write(resp)
def photo2mood(image_urls): vr = VisionRecognizer() photo_to_mood = MachineLoader.load(machines.photo_mood) TARGET_LABELS = ['Boat', 'Human', 'Insect', 'Invertebrate', 'Mammal', 'Man Made Scene', 'Outdoors', 'People Activity', 'Placental Mammal', 'Vertebrate'] moods = Counter() matrix = vr.recognize(image_urls).to_matrix(TARGET_LABELS) for r in matrix: mood = photo_to_mood.predict(r)[0] moods[int(mood)] += 1 target_mood = moods.most_common(1)[0][0] # get top and its score target_mood = Echonest.MOOD[target_mood] return target_mood
def post(self): resp = {"result": str(-1)} data = self.get_arguments("data[]") # new_number = self.get_arguments("new_number") #print(new_number) validated = Validator.validate_data(data) machine = MachineLoader.load(machines.number_recognizer) if len(validated) > 0: predicted = machine.predict(validated) #print(validated) # import numpy as np # import scipy #scipy.mise.toimage(np.array(validated).reshape(8,8), cmin=0.0, cmax=16).save('outfile.jpg') resp["result"] = str(int(predicted[0])) self.write(resp)
def photo2mood(image_urls): vr = VisionRecognizer() photo_to_mood = MachineLoader.load(machines.photo_mood) TARGET_LABELS = [ 'Boat', 'Human', 'Insect', 'Invertebrate', 'Mammal', 'Man Made Scene', 'Outdoors', 'People Activity', 'Placental Mammal', 'Vertebrate' ] moods = Counter() matrix = vr.recognize(image_urls).to_matrix(TARGET_LABELS) for r in matrix: mood = photo_to_mood.predict(r)[0] moods[int(mood)] += 1 target_mood = moods.most_common(1)[0][0] # get top and its score target_mood = Echonest.MOOD[target_mood] return target_mood
def post(self): resp = {"result": str(-1)} data = self.get_arguments("data[]") validated = Validator.validate_data(data) machine = MachineLoader.load(machines.number_recognizer) with open('c:/temp/test.txt','a') as thefile: for item in validated: thefile.write("%s\n" % item) from random import randint b=randint(0,9) print("please input number %d" % b) with open('c:/temp/target.txt','a') as thefile: thefile.write("%s\n" % b) if len(validated) > 0: predicted = machine.predict(validated) print(validated) print(predicted) resp["result"] = str(predicted[0]) #resp["result"] = str(b) self.write(resp)
def test_load(self): machine = MachineLoader.load(machines.number_recognizer) self.assertTrue(machine)