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_normality_increase_lambert(self):
# Generate random data and check that it is more normal after inference
for i, y in enumerate([np.random.standard_cauchy(size=ns), experimental_data]):
print('Distribution %d' % i)
print('Before')
print(('anderson: %0.3f\tshapiro: %0.3f' % (anderson(y)[0], shapiro(y)[0])).expandtabs(30))
stats.probplot(y, dist="norm", plot=plt)
plt.savefig(os.path.join(self.test_dir, '%d_before.png' % i))
plt.clf()
tau = g.igmm(y)
x = g.w_t(y, tau)
print('After')
print(('anderson: %0.3f\tshapiro: %0.3f' % (anderson(x)[0], shapiro(x)[0])).expandtabs(30))
stats.probplot(x, dist="norm", plot=plt)
plt.savefig(os.path.join(self.test_dir, '%d_after.png' % i))
plt.clf()
def test_normality_increase_lambert():
# Generate random data and check that it is more normal after inference
for i, y in enumerate([np.random.standard_cauchy(size=ns), experimental_data]):
print "Distribution %d" % i
print "Before"
print ("anderson: %0.3f\tshapiro: %0.3f" % (anderson(y)[0], shapiro(y)[0])).expandtabs(30)
stats.probplot(y, dist="norm", plot=pylab)
pylab.savefig("%d_before.png" % i)
pylab.clf()
tau = g.igmm(y)
x = g.w_t(y, tau)
print "After"
print ("anderson: %0.3f\tshapiro: %0.3f" % (anderson(x)[0], shapiro(x)[0])).expandtabs(30)
stats.probplot(x, dist="norm", plot=pylab)
pylab.savefig("%d_after.png" % i)
pylab.clf()
