def prepare_next_tree(self):
"""Prepare conditional U matrix for next tree."""
for edge in self.edges:
copula_theta = edge.theta
if self.level == 1:
left_u = self.u_matrix[:, edge.L]
right_u = self.u_matrix[:, edge.R]
else:
left_parent, right_parent = edge.parents
left_u, right_u = Edge.get_conditional_uni(
left_parent, right_parent)
# compute conditional cdfs C(i|j) = dC(i,j)/duj and dC(i,j)/du
left_u = [x for x in left_u if x is not None]
right_u = [x for x in right_u if x is not None]
X_left_right = np.array([[x, y] for x, y in zip(left_u, right_u)])
X_right_left = np.array([[x, y] for x, y in zip(right_u, left_u)])
copula = Bivariate(edge.name)
copula.fit(X_left_right)
left_given_right = copula.partial_derivative(
X_left_right, copula_theta)
right_given_left = copula.partial_derivative(
X_right_left, copula_theta)
# correction of 0 or 1
left_given_right[left_given_right == 0] = EPSILON
right_given_left[right_given_left == 0] = EPSILON
left_given_right[left_given_right == 1] = 1 - EPSILON
right_given_left[right_given_left == 1] = 1 - EPSILON
edge.U = np.array([left_given_right, right_given_left])
def get_likelihood(self, uni_matrix):
"""Compute likelihood given a U matrix.
Args:
uni_matrix (numpy.array):
Matrix to compute the likelihood.
Return:
tuple (np.ndarray, np.ndarray, np.array):
likelihood and conditional values.
"""
if self.parents is None:
left_u = uni_matrix[:, self.L]
right_u = uni_matrix[:, self.R]
else:
left_ing = list(self.D - self.parents[0].D)[0]
right_ing = list(self.D - self.parents[1].D)[0]
left_u = uni_matrix[self.L, left_ing]
right_u = uni_matrix[self.R, right_ing]
copula = Bivariate(copula_type=self.name)
copula.theta = self.theta
X_left_right = np.array([[left_u, right_u]])
X_right_left = np.array([[right_u, left_u]])
value = np.sum(copula.probability_density(X_left_right))
left_given_right = copula.partial_derivative(X_left_right)
right_given_left = copula.partial_derivative(X_right_left)
return value, left_given_right, right_given_left
def get_likelihood(self, uni_matrix):
"""Compute likelihood given a U matrix."""
if self.parents is None:
left_u = uni_matrix[:, self.L]
right_u = uni_matrix[:, self.R]
else:
left_ing = list(self.D - self.parents[0].D)[0]
right_ing = list(self.D - self.parents[1].D)[0]
left_u = uni_matrix[self.L, left_ing]
right_u = uni_matrix[self.R, right_ing]
copula = Bivariate(self.name)
copula.theta = self.theta
X_left_right = np.array([[left_u, right_u]])
X_right_left = np.array([[right_u, left_u]])
value = np.sum(copula.probability_density(X_left_right))
left_given_right = copula.partial_derivative(X_left_right)
right_given_left = copula.partial_derivative(X_right_left)
return value, left_given_right, right_given_left
