def generate_and_score_samples(self):
sample_list = []
target_list = flex.double()
for ii in range(self.sample_size):
x = random_transform.t_variate(a=max(2, self.n - 1), N=self.n)
x = x * self.sigma + self.mean
t = self.compute_target(x)
sample_list.append(x)
target_list.append(t)
order = flex.sort_permutation(flex.double(target_list))
return sample_list, t, order
def generate_and_score_samples(self):
sample_list = []
target_list = flex.double()
for ii in range(self.sample_size):
x = random_transform.t_variate(a=max(2,self.n-1),N=self.n)
x = x*self.sigma + self.mean
t = self.compute_target(x )
sample_list.append( x )
target_list.append( t )
order = flex.sort_permutation( flex.double(target_list) )
return sample_list, t, order
def exercise_t_variate():
data = rt.t_variate(a=6, mu=0, sigma=1, N=1000000)
mu1 = flex.mean(data)
mu2 = flex.mean(data * data)
assert approx_equal(mu1, 0, eps=0.02)
assert approx_equal(mu2, 1.5, eps=0.04)
def exercise_t_variate(): data = rt.t_variate(a=6, mu=0,sigma=1,N=1000000) mu1 = flex.mean(data) mu2 = flex.mean(data*data) assert approx_equal(mu1,0,eps=0.02) assert approx_equal(mu2,1.5,eps=0.04)
