def test_fixedpoint_cm_9(self):
"""classes with cardinality more than 1 and zero degrees"""
# test Matrix 1
n, seed = (100, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=True, seed=seed)
g = sample.UndirectedGraph(A)
g._solve_problem(
model="cm-new",
method="quasinewton",
initial_guess="random",
max_steps=300,
verbose=False,
linsearch="True",
)
g.solution_error()
# print('degseq = ', np.concatenate((g.dseq_out, g.dseq_in)))
# print('expected degseq = ',g.expected_dseq)
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 9: error = {}'.format(g.error))
# test result
self.assertTrue(g.error < 1e-1)
def test_CReAMa_cm_quasinewton_random_dense_20(self):
network = mg.random_weighted_matrix_generator_dense(n=20,
sup_ext=10,
sym=True,
seed=None)
network_bin = (network > 0).astype(int)
g = sample_und.UndirectedGraph(adjacency=network)
g.solve_tool(
model="CReAMa",
method="newton",
initial_guess="random",
adjacency="cm-new",
max_steps=1000,
verbose=False,
)
g.solution_error()
# test result
self.assertTrue(g.relative_error_strength < 1e-1)
self.assertTrue(g.relative_error_strength < 1e-2)
def test_fixedpoint_dcm_10(self):
# test Matrix 1
n, seed = (40, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
A[0, :] = 0
g = sample.DirectedGraph(A)
g._solve_problem(
model="decm",
method="fixed-point",
max_steps=20000,
verbose=False,
initial_guess="uniform",
linsearch=True,
)
g.solution_error()
# debug
# print("\n test 4, zeros, dimension n = {}, error = {}".format(n, g.error))
# test result
self.assertTrue(g.error < 1)
def test_loglikelihood_hessian_dcm_new_simmetry(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
theta = np.random.rand(2 * n)
x0 = np.exp(-theta)
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
f = loglikelihood_hessian_dcm_new(theta, g.args)
f_t = f.T
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f-f_t)
# test result
self.assertTrue(np.allclose(f - f_t, np.zeros((2 * n, 2 * n))))
def test_3(self):
n, seed = (40, 22)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
a[0, :] = 0
k_out = np.sum(a, 1)
k_in = np.sum(a, 0)
g = sample.DirectedGraph(a)
g._solve_problem(
model="dcm_new",
method="newton",
max_steps=3000,
verbose=False,
initial_guess="uniform",
linsearch="False",
)
g.solution_error()
err = g.error
# debug
# print('\ntest 1: error = {}'.format(err))
# test result
self.assertTrue(err < 1)
def test_iterative_dcm(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
# rd
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "eigenvalues"
g._initialize_problem("dcm", "fixed-point")
x0 = 0.5 * np.ones(4)
f_sample = -g.fun(x0)
g.last_model = "dcm"
g._set_solved_problem(f_sample)
f_full = np.concatenate((g.x, g.y))
f_correct = -np.array([2.5, 2.5, 0, 0, 1, 1.25])
# debug
# print(a)
# print(x0, x)
# print(f_full)
# test result
self.assertTrue(np.allclose(f_full, f_correct))
def test_loglikelihood_hessian_diag_dcm_new(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
theta = np.random.rand(n * 2)
x0 = np.exp(-theta)
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
f_sample = np.zeros(2 * n)
for i in range(2 * n):
f = lambda x: loglikelihood_prime_dcm_new(x, g.args)[i]
f_sample[i] = approx_fprime(theta, f, epsilon=1e-6)[i]
g.last_model = "dcm"
f_new = loglikelihood_hessian_diag_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))
def test_CReAMa_original_Dianati_random_dense_20_dir(self):
network = mg.random_weighted_matrix_generator_dense(
n=20, sup_ext=10, sym=False, seed=None
)
network_bin = (network > 0).astype(int)
g = sample.DirectedGraph(adjacency=network)
g.solve_tool(
model="CReAMa",
method="fixed-point",
initial_guess = "random",
adjacency=network_bin,
max_steps=1000,
verbose=False,
)
g.solution_error()
# test result
self.assertTrue(g.relative_error_strength < 1e-1)
self.assertTrue(g.relative_error_strength < 1e-2)
self.assertTrue((g._weighted_realisation() >= 0).all())
def test_loglikelihood_dcm(self):
"""
a = np.array([[0, 1, 1],
[1, 0, 1],
[0, 1, 0]])
"""
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "eigenvalues"
g._initialize_problem("dcm", "quasinewton")
x0 = np.concatenate((g.r_x, g.r_y))
# call loglikelihood function
f_sample = -g.step_fun(x0)
f_correct = 4 * np.log(1 / 2) - 3 * np.log(5 / 4)
# debug
# print(x0)
# print(f_sample)
# print(f_correct)
# test result
self.assertTrue(round(f_sample, 3) == round(f_correct, 3))
def test_fixedpoint_2(self):
"""classes with cardinality 1 and zero degrees"""
# test Matrix 1
n, seed = (40, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
A[0, :] = 0
g = sample.DirectedGraph(A)
g._solve_problem(
model="dcm",
method="fixed-point",
max_steps=300,
verbose=False,
initial_guess="uniform",
linsearch="False",
)
g.solution_error()
# print('degseq = ', np.concatenate((g.dseq_out, g.dseq_in)))
# print('expected degseq = ',g.expected_dseq)
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 7: error = {}'.format(g.error))
# test result
self.assertTrue(g.error < 1e-1)
def test_newton_4(self):
# convergence relies heavily on x0
n, s = (40, 35)
# n, s = (5, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sup_ext=100,
sym=False,
seed=s,
intweights=True)
A[0, :] = 0
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
x0 = 0.1 * np.ones(4 * n)
# x0 = np.concatenate((-1*np.ones(2*n), np.ones(2*n)))
args = (k_out, k_in, s_out, s_in)
x0[np.concatenate(args) == 0] = 1e3
fun = lambda x: -sample.loglikelihood_prime_decm_new(x, args)
fun_jac = lambda x: -sample.loglikelihood_hessian_decm_new(x, args)
step_fun = lambda x: -sample.loglikelihood_decm_new(x, args)
lin_fun = lambda x: sample.linsearch_fun_DECM_new(x, (step_fun, ))
hes_reg = sample.hessian_regulariser_function
sol = sample.solver(
x0,
fun=fun,
step_fun=step_fun,
fun_jac=fun_jac,
linsearch_fun=lin_fun,
tol=1e-6,
eps=1e-5,
max_steps=100,
method="newton",
verbose=False,
regularise=True,
full_return=False,
linsearch=True,
hessian_regulariser=hes_reg,
)
sol = np.exp(-sol)
ek = sample.expected_decm(sol)
k = np.concatenate((k_out, k_in, s_out, s_in))
err = np.max(np.abs(ek - k))
# debug
# print(ek)
# print(k)
# print('\ntest 4: error = {}'.format(err))
# print('method: {}, matrix {}x{} with zeros'.format('newton', n,n))
# test result
self.assertTrue(err < 1e-1)
def test_quasinewton_1(self):
n, s = (4, 25)
A = mg.random_weighted_matrix_generator_dense(n,
sup_ext=10,
sym=False,
seed=s,
intweights=True)
A[0, :] = 0
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
x0 = 0.9 * np.ones(n * 4)
args = (k_out, k_in, s_out, s_in)
fun = lambda x: -sample.loglikelihood_prime_decm_new(x, args)
fun_jac = lambda x: -sample.loglikelihood_hessian_diag_decm_new(
x, args)
step_fun = lambda x: -sample.loglikelihood_decm_new(x, args)
lin_fun = lambda x: sample.linsearch_fun_DECM_new(x, (step_fun, ))
hes_reg = sample.hessian_regulariser_function
sol = sample.solver(
x0,
fun=fun,
step_fun=step_fun,
fun_jac=fun_jac,
linsearch_fun=lin_fun,
tol=1e-6,
eps=1e-10,
max_steps=300,
method="quasinewton",
verbose=False,
regularise=True,
full_return=False,
linsearch=True,
hessian_regulariser=hes_reg,
)
sol = np.exp(-sol)
ek = sample.expected_decm(sol)
k = np.concatenate((k_out, k_in, s_out, s_in))
err = np.max(np.abs(ek - k))
# debug
# print(ek)
# print(k)
# print('\ntest 0: error = {}'.format(err))
# print('method = {}, matrix {}x{}'.format('quasinewton', n, n))
# test result
self.assertTrue(err < 1e-1)
def test_dcm_new_uniform(self):
n, seed = (4, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(A)
g.initial_guess = 'uniform'
g._set_initial_guess('dcm_new')
self.assertTrue(
np.concatenate((g.r_x, g.r_y)).all() == np.array(
[1e3, 0.5, 0.5, 0.5, 1e3, 0.5]).all())
def test_cm_uniform(self):
n, seed = (4, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=True, seed=seed)
g = sample_u.UndirectedGraph(A)
g.initial_guess = 'uniform'
g.last_model = "cm"
g._set_initial_guess('cm')
self.assertTrue(g.x0.all() == np.array([0.5, 0.5]).all())
def test_dcm_new(self):
n, seed = (4, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
x0 = np.random.rand(2 * n)
g = sample.DirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess('dcm_new')
g._set_solved_problem_dcm(x0)
self.assertTrue(np.concatenate((g.x, g.y)).all() == x0.all())
def test_iterative_2(self):
n, s = (40, 35)
# n, s = (5, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sup_ext=100,
sym=False,
seed=s,
intweights=True)
# A[0,:] = 0
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
x0 = 0.1 * np.ones(n * 4)
args = (k_out, k_in, s_out, s_in)
x0[np.concatenate(args) == 0] = 1e3
fun = lambda x: sample.iterative_decm_new(x, args)
step_fun = lambda x: -sample.loglikelihood_decm_new(x, args)
lin_fun = lambda x: sample.linsearch_fun_DECM_new(x, (step_fun, ))
hes_reg = sample.hessian_regulariser_function
sol = sample.solver(
x0,
fun=fun,
step_fun=step_fun,
linsearch_fun=lin_fun,
tol=1e-6,
eps=1e-10,
max_steps=7000,
method="fixed-point",
verbose=False,
regularise=True,
full_return=False,
linsearch=True,
hessian_regulariser=hes_reg,
)
sol = np.exp(-sol)
ek = sample.expected_decm(sol)
k = np.concatenate((k_out, k_in, s_out, s_in))
err = np.max(np.abs(ek - k))
# debug
# print(ek)
# print(k)
# print('\ntest 5: error = {}'.format(err))
# print('method: {}, matrix {}x{} '.format('iterative', n,n))
# test result
self.assertTrue(err < 1)
def test_cm(self):
n, seed = (4, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=True, seed=seed)
x0 = np.random.rand(n)
g = sample_u.UndirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess_cm()
g.full_return = False
g.last_model = "cm"
g._set_solved_problem_cm(g.x0)
self.assertTrue(g.x.all() == x0.all())
def test_ecm(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
x0 = np.random.rand(n)
g = sample_u.UndirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess_CReAMa()
self.assertTrue(g.x0.all() == x0.all())
def test_CREAMA_uniform(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample_u.UndirectedGraph(A)
g.initial_guess = 'strengths_minor'
g._set_initial_guess('CReAMa')
x = (g.strength_sequence > 0).astype(float) / (g.strength_sequence + 1)
self.assertTrue(g.x0.all() == x.all())
def test_decm_new_uniform(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample.DirectedGraph(A)
g.initial_guess = 'uniform'
g._set_initial_guess('decm_new')
self.assertTrue(
np.concatenate((g.x, g.y, g.out_strength,
g.in_strength)).all() == np.ones(4 * n).all())
def test_decm(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
x0 = np.random.rand(4 * n)
g = sample.DirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess('decm')
g._set_solved_problem_decm(x0)
self.assertTrue(np.concatenate((g.x, g.y)).all() == x0.all())
def test_loglikelihood_hessian_diag_dcm_new_zeros(self):
# convergence relies heavily on x0
n, s = (10, 35)
# n, s = (5, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sup_ext=100,
sym=False,
seed=s,
intweights=True)
A[0, :] = 0
A[:, 5] = 0
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
g = sample.DirectedGraph(A)
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("decm", "newton")
# theta = np.random.rand(6)
theta = 0.5 * np.ones(n * 4)
theta[np.concatenate((k_out, k_in, s_out, s_in)) == 0] = 1e4
x0 = np.exp(-theta)
f_sample = np.zeros(n * 4)
for i in range(n * 4):
f = lambda x: loglikelihood_prime_decm_new(x, g.args)[i]
f_sample[i] = approx_fprime(theta, f, epsilon=1e-6)[i]
f_new = loglikelihood_hessian_diag_decm_new(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print('approx',f_sample)
# print('my',f_new)
# print('gradient', loglikelihood_prime_decm_new(theta, g.args))
# print('diff',f_sample - f_new)
# print('max',np.max(np.abs(f_sample - f_new)))
# test result
self.assertTrue(np.allclose(f_sample, f_new))
def test_CREAMA(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
x0 = np.random.rand(n)
g = sample_u.UndirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess_CReAMa()
g.full_return = False
g._set_solved_problem_CReAMa(g.x0)
self.assertTrue(g.beta.all() == x0.all())
def test_CREAMA_uniform(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample.DirectedGraph(A)
g.initial_guess = 'strengths_minor'
g._set_initial_guess_CReAMa()
x = np.concatenate(
(sample.out_strength(A) / (sample.out_strength(A) + 1),
sample.in_strength(A) / (sample.in_strength(A) + 1)))
self.assertTrue(g.x0.all() == x.all())
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(self):
# problem initialisation
A = mg.random_weighted_matrix_generator_dense(n=10, sym=False)
prova = sample.DirectedGraph(A)
prova.initial_guess = "random"
prova._initialize_problem("decm", "quasinewton")
sol = np.concatenate((prova.x, prova.y, prova.b_out, prova.b_in))
# casadi functions initialization
x, f = casadi_loglikelihood_decm(A)
casadi_fun = Function("f", [x], [f])
f_og = sample.loglikelihood_decm(sol, prova.args)
f_casadi = casadi_fun(sol)
err_loglikelihood = abs(f_og - f_casadi)
# print('loglikelihood og-casadi = {}'.format(err_loglikelihood))
self.assertTrue(err_loglikelihood < 1e-10)
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_prime_dcm(self):
"""
a = np.array([[0, 1, 1],
[1, 0, 1],
[0, 1, 0]])
"""
n, seed = (30, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
# rd
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)
f = lambda x: sample.loglikelihood_dcm(x, g.args)
f_sample = approx_fprime(theta, f, epsilon=1e-6)
f_new = sample.loglikelihood_prime_dcm(theta, g.args)
# debug
# print(a)
# print(x0, x)
# print(f_sample)
# print(f_new)
# for i in range(2*n_rd):
# if not np.allclose(f_new[i], f_sample[i],atol=1e-1):
# print(i)
# test result
self.assertTrue(np.allclose(f_sample, f_new, atol=1e-1))
def test_loglikelihood_prime(self):
# problem initialisation
A = mg.random_weighted_matrix_generator_dense(n=10, sym=False)
prova = sample.DirectedGraph(A)
prova.initial_guess = "random"
prova._initialize_problem("decm", "quasinewton")
sol = np.concatenate((prova.x, prova.y, prova.b_out, prova.b_in))
# casadi functions initialization
x, f = casadi_loglikelihood_decm(A)
casadi_fun = Function("f", [x], [f])
fj = jacobian(f, x)
casadi_fun_gradient = Function("j", [x], [fj])
fj_og = sample.loglikelihood_prime_decm(sol, prova.args)
fj_casadi = casadi_fun_gradient(sol)
err_ll_prime = max(abs(fj_og - fj_casadi.__array__()[0]))
# print('loglikelihood prime og-casadi = {}'.format(err_ll_prime ))
self.assertTrue(err_ll_prime < 1e-10)
def test_iterative_dcm_new(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
# rd
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "fixed-point")
# theta = np.random.rand(6)
theta = np.array([np.infty, 0.5, 0.5, 0.5, np.infty, 0.5])
x0 = np.exp(-theta)
f_sample = g.fun(x0)
g.last_model = "dcm"
g._set_solved_problem(f_sample)
f_full = np.concatenate((g.x, g.y))
f_full = -np.log(f_full)
f_new = iterative_dcm_new(theta, g.args)
g._set_solved_problem_dcm(f_new)
f_new_full = np.concatenate((g.x, g.y))
# f_new_bis = iterative_dcm_new_bis(theta, g.args)
# print('normale ',f_new)
# print('bis',f_new_bis)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_full)
# print(f_new)
# print(f_new_full)
# test result
self.assertTrue(np.allclose(f_full, f_new_full))