def test_estimate(self):
print "Testing parameter estimation of Dirichlet distribution ..."
sys.stdout.flush()
myalpha = 10.0 * np.random.rand(10)
p = Distributions.Dirichlet({'alpha': myalpha})
dat = p.sample(50000)
p = Distributions.Dirichlet({'alpha': np.random.rand(10)})
p.estimate(dat)
alpha = p.param['alpha']
self.assertTrue(
np.max(np.abs(alpha - myalpha)) < self.TolParam,
'Difference in alpha parameter for Dirichlet distribution greater than '
+ str(self.TolParam))
def test_loglik(self):
print "Testing log-likelihood of Dirichlet distribution ... "
sys.stdout.flush()
p = Distributions.Dirichlet({'alpha': self.alpha})
l = p.loglik(self.X)
for k in range(len(self.LL)):
self.assertTrue(
np.abs(l[k] - self.LL[k]) < self.Tol,
'Difference in log-likelihood for Dirichlet greater than ' +
str(self.Tol))