def printf(*argv):
data, data_type, data_shape = [], [], []
for arg in argv:
try:
data.append(arg)
data_type.append(type(arg))
data_shape.append(arg.shape)
except AttributeError:
data_shape.append(None)
except Exception as e:
print_danger(e)
for i in range(len(data)):
print_danger(data[i])
if (data_shape[i] == () or data_shape[i] == None):
p('{0}'.format(data_type[i]))
else:
p('{0} => shape: {1}'.format(data_type[i], data_shape[i]))
def main(student_cnt: int = 2,
stop_time: int = 2000,
stop_percent: Optional[float] = None,
display: bool = False):
assert student_cnt >= 2 and student_cnt <= 10000
students = [Student() for _ in range(student_cnt)]
students[0].hear()
if display:
console.rule(f'cnt={student_cnt}, stop={stop_time}')
def percent_heard():
return sum([s.heard() for s in students]) / student_cnt
time = 0
while time < stop_time:
shuffle(students)
split = student_cnt // 2
firsts = students[:split]
seconds = students[split:]
for i in range(len(firsts)):
first = firsts[i]
second = seconds[i]
if first.able_to_tell() and second.able_to_tell():
if randint(0, 1):
first.hear()
if randint(0, 1):
second.hear()
elif first.able_to_tell():
first.tell(second)
elif second.able_to_tell():
second.tell(first)
time += 1
if stop_percent is not None:
if percent_heard() >= stop_percent:
break
if display:
p(time, percent_heard)
return percent_heard(), time
def c(self, *argv):
self.m_io.clear()
self.t_io.clear()
self.shape.clear()
for arg in argv:
try:
self.m_io.append(arg)
self.t_io.append(type(arg))
self.shape.append(arg.shape)
except AttributeError:
self.shape.append(None)
except Exception as e:
p(e)
if not self.debug:
for i in range(len(self.m_io)):
self.print_danger(self.m_io[i])
if (self.shape[i]) == None:
p('{0}'.format(self.t_io[i]))
else:
p('{0} => shape: {1}'.format(self.t_io[i], self.shape[i]))
else:
pass
def main(args: Namespace):
trials = args.trials
q_cnt = args.questions
c_cnt = args.choices
exams_shape = (trials, q_cnt)
with console.status('Computing on GPU'):
#! Create exams
if args.answer_type == 'last':
exams = cp.full(exams_shape, c_cnt - 1, dtype=NP_TYPE)
elif args.answer_type == 'random':
exams = cp.random.choice(c_cnt, exams_shape).astype(NP_TYPE)
else:
log("[bold red] Incorrect answer strategy!")
exit(-1)
#! Create attempts
attempts = cp.random.choice(c_cnt, exams_shape).astype(NP_TYPE)
grades = (exams == attempts)
correct = grades.sum(axis=1).astype(NP_TYPE)
scores = (correct / q_cnt) * 100
# displayResults(exam, trial, graded)
# log(f'Score = {score:0.2f}%')
# log(locals())
# Move to CPU and sync
cp.cuda.Stream.null.synchronize()
scores = scores.get()
passing = (scores > 70).sum()
trace = np.random.choice(scores, 10).round(2)
simulation_result = (passing / len(scores))
title('[bold purple]Results')
p(f'[green]Scores (trace 10): [blue][{"] [".join(trace.astype(str))}]')
p(f'[green]Percent of Successes (> 70%): [blue]{toPercent(simulation_result)}'
) # type: ignore
p(f'[green]Average: [blue] {scores.mean()}')
formula = ' (1 / c_cnt) ** np.ceil(q_cnt * 0.7)'
formula_result = eval(formula)
syntax = Syntax(formula, "python")
error = (simulation_result - formula_result) / formula_result
title('[bold yellow]Conclusion')
p('Each question is independent, therefor:')
p(' P(q1,q2,q3)\n = P(q1)P(q2)P(q3)')
p('Each question has the same probability of being answered correctly')
p(' = P(q1)^N\n where N is the number of questions')
p('and in this case where success is > 70%...')
p(syntax, f' = {toPercent(formula_result)}')
p('The simulation results support this conclusion:')
p(f'Experimental Error = {toPercent(error)}')
p(f'[green]Average: [blue] {scores.mean()}')
formula = ' (1 / c_cnt) ** np.ceil(q_cnt * 0.7)'
formula_result = eval(formula)
syntax = Syntax(formula, "python")
error = (simulation_result - formula_result) / formula_result
title('[bold yellow]Conclusion')
p('Each question is independent, therefor:')
p(' P(q1,q2,q3)\n = P(q1)P(q2)P(q3)')
p('Each question has the same probability of being answered correctly')
p(' = P(q1)^N\n where N is the number of questions')
p('and in this case where success is > 70%...')
p(syntax, f' = {toPercent(formula_result)}')
p('The simulation results support this conclusion:')
p(f'Experimental Error = {toPercent(error)}')
if __name__ == '__main__':
args = setupArgParser()
title('[bold cyan]Assumptions')
p('- Interview has 3 questions instead of 20')
p('- Success means a 70% or higher')
p(' - That means 3/3 given the above assumption')
title('[bold blue]Parameters')
inspect(args, title='Simulation Params', docs=False)
main(args)
def print_danger(m):
p('[italic red]{0}[/italic red]'.format(m))