def choose_question(initial_questions, objects_values, asked_questions, how_many=10):
'''Returns a question with the lowest entropy.'''
if initial_questions:
question = initial_questions.pop(0)
else:
sorted_objects_values = sorted_objects_values = sort_objects_values(objects_values)
if len(sorted_objects_values) <= how_many: ### possibly some proportion of the objects in the database
max = len(sorted_objects_values)
else:
max = how_many
most_likely_objects = sorted_objects_values[:max]
objects = [object[1] for object in most_likely_objects]
questions = model.get_questions()
best_question_entropy = abs(float('inf'))
best_question = None
for question in questions: # loop through all the questions
if not(question.id in asked_questions): # if we have not already asked it, condider it
question_entropy = entropy(objects, question)
if question_entropy <= best_question_entropy:
best_question_entropy = question_entropy
best_question = question
question = best_question
return question
def GET(self, object_id):
'''Renders a page with all of the questions and values for a specified
object_id so that it can be retrained manually.'''
object = model.get_object_by_id(object_id)
questions = model.get_questions()
data = model.get_data_dictionary()
if object:
return render.retrain(object, list(questions), data)
else:
raise web.seeother('/') # returns to admin page
def GET(self, object_id):
'''Renders a page with all of the questions and values for a specified
object_id so that it can be retrained manually.'''
object = model.get_object_by_id(object_id)
questions = model.get_questions()
data = model.get_data_dictionary()
if object:
return render.retrain(object, list(questions), data)
else:
raise web.seeother('/') # returns to admin page
def load_initial_questions():
'''Loads questions we always want to ask as well as some random ones so that we can learn more
about the objects.'''
initial_questions = []
initial_questions.append(model.get_question_by_id(1)) # is character real
questions = list(model.get_questions()) # converts from webpy's IterBetter to a list
for i in range(2): # up to 2 initial random questions
q = random.choice(questions)
if not(q in initial_questions) and not(q.id in [1,6]): # real/man
initial_questions.append(q)
initial_questions.append(model.get_question_by_id(6)) # is the character a man
return initial_questions
def choose_question (objects_values, asked_questions):
bestCanidates = get_nearby_objects(objects_values, 2)
object1 = bestCanidates[0].id
object2 = bestCanidates[1].id
questions = model.get_questions()
biggest_difference = [0, 0] #[Question id, difference]
for question in questions:
if not(question.id in asked_questions):
objectOneAnswer = model.get_value(object1, question.id)
objectTwoAnswer = model.get_value(object2, question.id)
difference = abs(objectOneAnswer-objectTwoAnswer)
if difference > biggest_difference[1]:
biggest_difference = [question.id, difference]
return model.get_question_by_id(biggest_difference[0])
def GET(self):
'''Lists all of the questions so that selected questions can be deleted.'''
questions = model.get_questions()
return render.delete_question(questions)
new_object_id = model.add_object(name) ### adds to database and trains
learn(asked_questions, new_object_id)
return new_object_id
def learn(asked_questions, object_id):
'''Updates the data for the correct object based on information in asked_questions.
Also updates times played for the object and stores the playlog.'''
for question in asked_questions:
current_weight = model.get_value(object_id, question)
if not(current_weight): current_weight = 0
new_weight = current_weight + asked_questions[question]
model.update_data(object_id, question, new_weight)
model.update_times_played(object_id)
model.record_playlog(object_id, asked_questions, True)
if __name__ == '__main__':
##### Tests entropy! #####
objects = model.get_objects()
objects = [object.id for object in objects]
objects = tuple(objects)
questions = model.get_questions()
for question in questions:
print question.id
print 'DAN:', simple_entropy(objects, question)
print 'ANDY:', entropy(objects, question)
def GET(self):
'''Lists all of the questions so that selected questions can be deleted.'''
questions = model.get_questions()
return render.delete_question(questions)