def test_check_derivative(self):
def jac(x):
return csr_matrix(self.jac(x))
accuracy = check_derivative(self.fun,
jac,
self.x0,
bounds=(self.lb, self.ub),
sparse_diff=True)
assert_(accuracy < 1e-9)
A = self.structure(self.n)
groups = group_columns(A)
accuracy = check_derivative(self.fun,
jac,
self.x0,
bounds=(self.lb, self.ub),
sparse_diff=True,
sparsity=(A, groups))
assert_(accuracy < 1e-9)
accuracy = check_derivative(self.fun,
jac,
self.x0,
bounds=(self.lb, self.ub),
sparse_diff=False)
# Slightly worse accuracy because all elements are computed.
# Floating point issues make true 0 to some smal value, then
# it is divided by small step and as a result we have ~1e-9 element,
# which is actually should be zero.
assert_(accuracy < 1e-8)
def test_check_derivative(self):
def jac(x):
return csr_matrix(self.jac(x))
accuracy = check_derivative(
self.fun, jac, self.x0,
bounds=(self.lb, self.ub), sparse_diff=True)
assert_(accuracy < 1e-9)
A = self.structure(self.n)
groups = group_columns(A)
accuracy = check_derivative(
self.fun, jac, self.x0, bounds=(self.lb, self.ub),
sparse_diff=True, sparsity=(A, groups)
)
assert_(accuracy < 1e-9)
accuracy = check_derivative(
self.fun, jac, self.x0,
bounds=(self.lb, self.ub), sparse_diff=False)
# Slightly worse accuracy because all elements are computed.
# Floating point issues make true 0 to some smal value, then
# it is divided by small step and as a result we have ~1e-9 element,
# which is actually should be zero.
assert_(accuracy < 1e-8)
def test_check_derivative(self):
def jac(x):
return csr_matrix(self.jac(x))
accuracy = check_derivative(self.fun, jac, self.x0,
bounds=(self.lb, self.ub))
assert_(accuracy < 1e-9)
accuracy = check_derivative(self.fun, jac, self.x0,
bounds=(self.lb, self.ub))
assert_(accuracy < 1e-9)
def test_check_derivative(self):
def jac(x):
return csr_matrix(self.jac(x))
accuracy = check_derivative(self.fun, jac, self.x0,
bounds=(self.lb, self.ub))
assert_(accuracy < 1e-9)
accuracy = check_derivative(self.fun, jac, self.x0,
bounds=(self.lb, self.ub))
assert_(accuracy < 1e-9)
def test_check_derivative(self):
x0 = np.array([-10.0, 10])
accuracy = check_derivative(self.fun_vector_vector,
self.jac_vector_vector, x0)
assert_(accuracy < 1e-9)
accuracy = check_derivative(self.fun_vector_vector,
self.jac_vector_vector, x0)
assert_(accuracy < 1e-6)
x0 = np.array([0.0, 0.0])
accuracy = check_derivative(self.fun_zero_jacobian,
self.jac_zero_jacobian, x0)
assert_(accuracy == 0)
accuracy = check_derivative(self.fun_zero_jacobian,
self.jac_zero_jacobian, x0)
assert_(accuracy == 0)
def test_check_derivative(self):
x0 = np.array([-10.0, 10])
accuracy = check_derivative(self.fun_vector_vector,
self.jac_vector_vector, x0)
assert_(accuracy < 1e-9)
accuracy = check_derivative(self.fun_vector_vector,
self.jac_vector_vector, x0)
assert_(accuracy < 1e-6)
x0 = np.array([0.0, 0.0])
accuracy = check_derivative(self.fun_zero_jacobian,
self.jac_zero_jacobian, x0)
assert_(accuracy == 0)
accuracy = check_derivative(self.fun_zero_jacobian,
self.jac_zero_jacobian, x0)
assert_(accuracy == 0)
def check_jacobian(self):
accuracy = []
if self.sparsity is not None:
sparse_diff = True
groups = group_columns(self.sparsity)
sparsity = (self.sparsity, groups)
else:
sparse_diff = False
sparsity = None
for x0, bounds in self.specs:
x = x0 + np.random.randn(self.n)
acc = check_derivative(self.fun, self.jac, x, bounds=bounds,
sparse_diff=sparse_diff, sparsity=sparsity)
accuracy.append(acc)
return accuracy
