def test_loglikelihood_hessian_dcm(self):
# n,s =(3, 1)
n, s = (30, 1)
a = mg.random_binary_matrix_generator_custom_density(n=n,
p=0.15,
sym=False,
seed=s)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "eigenvalues"
g._initialize_problem("dcm", "newton")
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
n_rd = len(k_out)
theta = np.random.rand(2 * n_rd)
f_sample = np.zeros((n_rd * 2, n_rd * 2))
for i in range(n_rd * 2):
f = lambda x: sample.loglikelihood_prime_dcm(x, g.args)[i]
f_sample[i, :] = approx_fprime(theta, f, epsilon=1e-6)
f_new = sample.loglikelihood_hessian_dcm(theta, g.args)
"""
for i in range(2*n_rd):
for j in range(2*n_rd):
if np.allclose(f_new[i,j], f_sample[i,j], atol=1e-1) == False:
print(i,j)
print(f_new[i,j])
print(f_sample[i,j])
print(f_sample[i,j]/f_new[i,j])
"""
# debug
# print(theta, x0)
# print(g.args)
# print(n_rd/n)
# print(f_sample)
# print(f_new)
# test result
self.assertTrue(np.allclose(f_sample, f_new, atol=1))
def test_loglikelihood_hessian_dcm_new_emi_simmetry(self):
n, s = (50, 1)
# n,s =(2, 1)
a = mg.random_binary_matrix_generator_custom_density(n=n,
p=0.15,
sym=False,
seed=s)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
n_rd = len(k_out)
# print(n_rd/n)
theta = np.random.rand(2 * n_rd)
f_new = loglikelihood_hessian_dcm_new(theta, g.args)
for i in range(2 * n_rd):
for j in range(2 * n_rd):
if f_new[i, j] - f_new[j, i] != 0:
print(i, j)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_new)
# test result
self.assertTrue(
np.allclose(f_new - f_new.T, np.zeros((2 * n_rd, 2 * n_rd))))
def test_loglikelihood_hessian_dcm_new_emi(self):
n, s = (50, 1)
a = mg.random_binary_matrix_generator_custom_density(n=n,
p=0.15,
sym=False,
seed=s)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
n_rd = len(k_out)
theta = np.random.rand(2 * n_rd)
f_sample = np.zeros((n_rd * 2, n_rd * 2))
for i in range(n_rd * 2):
f = lambda x: loglikelihood_prime_dcm_new(x, g.args)[i]
f_sample[i, :] = approx_fprime(theta, f, epsilon=1e-6)
f_new = loglikelihood_hessian_dcm_new(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_new)
# test result
self.assertTrue(np.allclose(f_sample, f_new))