class MHMultivariateLognormalTargetMock (MetropolisHastings):
def __init__ (self, theta):
super ().__init__ (theta, verbose=False)
n = theta.get_size ()
mu = np.ones (n)
Sigma = np.eye (n) / 10
self.target_distribution = MultivariateLognormal (mu,
Sigma)
def _calc_log_likelihood (self, t):
t_values = t.get_values ()
l = self.target_distribution.pdf (t_values)
return np.log (l)
def _create_jump_dist (self, theta_t):
n = theta_t.get_size ()
S = np.eye (n) / 100
variances = S.diagonal ()
theta_values = np.array (theta_t.get_values ())
mu = np.log (theta_values) - variances / 2
return MultivariateLognormal (mu, S)
def _calc_mh_ratio (self, new_t, new_l, old_t, old_l):
J_gv_new = self._create_jump_dist (new_t)
J_gv_old = self._create_jump_dist (old_t)
p_old_gv_new = J_gv_new.pdf (old_t.get_values ())
p_new_gv_old = J_gv_old.pdf (new_t.get_values ())
l_ratio = np.exp (new_l - old_l)
r = (l_ratio) * (p_old_gv_new / p_new_gv_old)
return r
def test_get_pdf_of_zero_prob_point (self):
""" Tests if we can get the pdf of a point with pdf equal to
zero. """
mu = [0, 0, 0]
S = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
X = MultivariateLognormal (mu, S)
x = [1, 0, 1]
analytic = 0
assert (abs (X.pdf (x) - analytic) < 1e-4)
def test_get_pdf_of_small_prob_point (self):
""" Tests if we can get the pdf of a point with small pdf value.
"""
mu = [0, 0, 0]
S = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
X = MultivariateLognormal (mu, S)
x = [1e-230, 1e-120, 1e-130]
analytic = 0
assert (abs (X.pdf (x) - analytic) < 1e-4)
def test_get_pdf (self):
""" Tests if we can get a point of the probability density
function of this random variable. """
mu = [0, 0, 0]
S = [[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
X = MultivariateLognormal (mu, S)
x = [1, 1, 1]
analytic = 1 / np.sqrt ((2 * np.pi) ** 3 )
assert (abs (X.pdf (x) - analytic) < 1e-4)