def test_invert_lambert(self):
# Test if we can transform and transform back
mu, sigma, delta = 0.5, 1.7, 0.33
x = np.random.normal(loc=mu, scale=sigma, size=ns)
y = g.inverse(x, (mu, sigma, delta))
x_prime = g.w_t(y, (mu, sigma, delta))
assert np.allclose(x, x_prime)
def test_invert_lambert():
# Test if we can transform and transform back
mu, sigma, delta = 0.5, 1.7, 0.33
x = np.random.normal(loc=mu, scale=sigma, size=ns)
y = g.inverse(x, (mu, sigma, delta))
x_prime = g.w_t(y, (mu, sigma, delta))
assert np.allclose(x, x_prime)
def test_recover_lambert(self):
# Generate data from a normal, then apply the W transform, and check if we can recover parameters
# See Table 2 of Georg's paper
# These results seem a little worse, but we'd have to run many replications to directly compare.
mu, sigma = 0, 1
# print ('del_true\tns\tmu\tsigma\tdelta').expandtabs(10)
for delta in [0, 1./3, 1., 1.5]:
for n in [50, 100, 1000]:
x = np.random.normal(loc=mu, scale=sigma, size=n)
y = g.inverse(x, (mu, sigma, delta))
mu_prime, sigma_prime, delta_prime = g.igmm(y)
print(('%0.3f\t%d\t%0.3f\t%0.3f\t%0.3f' % (delta, n, mu_prime, sigma_prime, delta_prime)).expandtabs(10))
assert np.abs(mu - mu_prime) < 10. / np.sqrt(n)
assert np.abs(sigma - sigma_prime) < 10. / np.sqrt(n)
assert np.abs(delta - delta_prime) < 10. / np.sqrt(n)
def test_recover_lambert():
# Generate data from a normal, then apply the W transform, and check if we can recover parameters
# See Table 2 of Georg's paper
# These results seem a little worse, but we'd have to run many replications to directly compare.
mu, sigma = 0, 1
print ("del_true\tns\tmu\tsigma\tdelta").expandtabs(10)
for delta in [0, 1.0 / 3, 1.0, 1.5]:
for n in [50, 100, 1000]:
x = np.random.normal(loc=mu, scale=sigma, size=n)
y = g.inverse(x, (mu, sigma, delta))
mu_prime, sigma_prime, delta_prime = g.igmm(y)
print ("%0.3f\t%d\t%0.3f\t%0.3f\t%0.3f" % (delta, n, mu_prime, sigma_prime, delta_prime)).expandtabs(10)
assert np.abs(mu - mu_prime) < 10.0 / np.sqrt(n)
assert np.abs(sigma - sigma_prime) < 10.0 / np.sqrt(n)
assert np.abs(delta - delta_prime) < 10.0 / np.sqrt(n)
