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):
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):
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 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):
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 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)
def test_load(self):
machine = MachineLoader.load(machines.number_recognizer)
self.assertTrue(machine)
