def should_add_up(self):
schedule = Schedule()
schedule.add_lesson(RandomWeeklyLesson())
schedule.add_lesson(RandomWeeklyLesson())
b = Bins(schedule=schedule)
b_ = b.bins()
expect(b[0] + b[1] + b[2] + b[3] + b[4] + b[5]).to.equal(2)
expect(b_[0] + b_[1] + b_[2] + b_[3] + b_[4] + b_[5]).to.equal(2)
def should_add_up(self):
schedule = Schedule()
schedule.add_lesson(RandomWeeklyLesson())
schedule.add_lesson(RandomWeeklyLesson())
b = Bins(schedule=schedule)
b_ = b.bins()
expect(b[0] + b[1] + b[2] + b[3] + b[4] + b[5]).to.equal(2)
expect(b_[0] + b_[1] + b_[2] + b_[3] + b_[4] + b_[5]).to.equal(2)
class DumbModel(Model):
def __init__(self, start_time_range=(0,23.75,), day_of_week_range=(0,6,)):
self.start_time_range = start_time_range
self.day_of_week_range = day_of_week_range
schedule = Schedule()
lower_limit_start_time, upper_limit_start_time = start_time_range
lower_limit_day_of_week, upper_limit_day_of_week = day_of_week_range
for i in np.linspace(lower_limit_start_time, upper_limit_start_time, 96):
for j in range(lower_limit_day_of_week, upper_limit_day_of_week + 1):
schedule.add_lesson( WeeklyLesson(start_time=i, day_of_week=j))
self.bins = Bins(schedule=schedule).bins()
def fit(self, training_data):
pass
def generate_sample_schedule(self, business_forecast):
schedule = Schedule()
for i in business_forecast:
for j in range(0, int(i['frequency'] * i['schedule_type'])):
schedule.add_lesson(\
RandomWeeklyLesson(\
start_time_range=self.start_time_range,
day_of_week_range=self.day_of_week_range))
return schedule
def predict(self, business_forecast, training_data=pd.DataFrame()):
return self.bins / self.bins.sum() * \
self.num_business_forecast_lessons(business_forecast)
def __init__(self, start_time_range=(0,23.75,), day_of_week_range=(0,6,)):
self.start_time_range = start_time_range
self.day_of_week_range = day_of_week_range
schedule = Schedule()
lower_limit_start_time, upper_limit_start_time = start_time_range
lower_limit_day_of_week, upper_limit_day_of_week = day_of_week_range
for i in np.linspace(lower_limit_start_time, upper_limit_start_time, 96):
for j in range(lower_limit_day_of_week, upper_limit_day_of_week + 1):
schedule.add_lesson( WeeklyLesson(start_time=i, day_of_week=j))
self.bins = Bins(schedule=schedule).bins()
def bins(self):
return Bins(schedule=self._schedule).bins()
def by_default_should_be_6(self):
schedule = Schedule()
schedule.add_lesson(RandomWeeklyLesson())
b = Bins(schedule=schedule)
expect(b.num_bins()).to.equal(6)
def it_should_aggregate_the_lesson_requests_per_bin(self):
schedule = Schedule()
schedule.add_lesson(RandomWeeklyLesson())
b = Bins(schedule=schedule)
expect(b.sum().sum()).to.equal(1)
def it_should_aggregate_the_lesson_requests_per_bin(self):
schedule = Schedule()
schedule.add_lesson(RandomWeeklyLesson())
b = Bins(schedule=schedule)
expect(b.sum().sum()).to.equal(1)
def by_default_should_be_6(self):
schedule = Schedule()
schedule.add_lesson(RandomWeeklyLesson())
b = Bins(schedule=schedule)
expect(b.num_bins()).to.equal(6)