def test_version1(n = 50000, silent= False):
"""
test the speed of rtnorm by Christoph Lassner
test for both fixed limits a,b and variable limits
n - number of rep
"""
times = np.zeros(3)
start = time.time()
x = rntorm_v1(amin +0.1, 1,size=n) # @UnusedVariable
times[0] = time.time() - start
start = time.time()
x = rntorm_v1(amax -0.1, 4,size=n)
times[1] = time.time() - start
a_vec = 5*np.random.randn(n)
b_vec = a_vec + np.abs(5*np.random.randn(n))
start = time.time()
for i, (a, b) in enumerate(zip(a_vec, b_vec)):
x[i] =rntorm_v1(a, b)
times[2] = time.time() - start
if not silent:
print('for rntorm_v1:')
for i, time_ in enumerate(times):
print('test_{i} = {time:.5f}'.format(i = i, time = time_))
return times
def test_version1(n=50000, silent=False):
"""
test the speed of rtnorm by Christoph Lassner
test for both fixed limits a,b and variable limits
n - number of rep
"""
times = np.zeros(3)
start = time.time()
x = rntorm_v1(amin + 0.1, 1, size=n) # @UnusedVariable
times[0] = time.time() - start
start = time.time()
x = rntorm_v1(amax - 0.1, 4, size=n)
times[1] = time.time() - start
a_vec = 5 * np.random.randn(n)
b_vec = a_vec + np.abs(5 * np.random.randn(n))
start = time.time()
for i, (a, b) in enumerate(zip(a_vec, b_vec)):
x[i] = rntorm_v1(a, b)
times[2] = time.time() - start
if not silent:
print('for rntorm_v1:')
for i, time_ in enumerate(times):
print('test_{i} = {time:.5f}'.format(i=i, time=time_))
return times
def KS_test_Rtrunc(a, b, n = 1000, p_lim = 0.01):
"""
a - limit below
b - limit above
n - number of samples
p_lims - limit on which to reject KS
"""
f = lambda x: stats.truncnorm.cdf(x, a, b)
p_val = stats.kstest(rntorm_v1(a, b,size=n), f)[1]
if p_val < p_lim:
print('p_val = {pval}'.format(pval = p_val))
return False
return True
def KS_test_Rtrunc(a, b, n=1000, p_lim=0.01):
"""
a - limit below
b - limit above
n - number of samples
p_lims - limit on which to reject KS
"""
f = lambda x: stats.truncnorm.cdf(x, a, b)
p_val = stats.kstest(rntorm_v1(a, b, size=n), f)[1]
if p_val < p_lim:
print('p_val = {pval}'.format(pval=p_val))
return False
return True
def test_unconstrained(self):
"""
if the limits are -inf, inf does it generate samples from
"""
p_val = stats.kstest(rntorm_v1(-np.inf,np.inf,size=1000), 'norm')[1]
self.assertTrue(p_val > 0.01, msg='KS failed')
def test_unconstrained(self):
"""
if the limits are -inf, inf does it generate samples from
"""
p_val = stats.kstest(rntorm_v1(-np.inf, np.inf, size=1000), 'norm')[1]
self.assertTrue(p_val > 0.01, msg='KS failed')
