def test_logistic_gradient_at_simple_points():
# Tests gradient of logistic data loss function in points near to zero.
# This is a not so hard test, just for detecting big errors
X, y, w, mask = create_graph_net_simulation_data(n_samples=10, size=4)
grad_weight = 1
# Add the intercept
w = np.append(w, 0)
func = lambda w: _logistic_data_loss_and_spatial_grad(
X, y, w, mask, grad_weight)
func_grad = lambda w: _logistic_data_loss_and_spatial_grad_derivative(
X, y, w, mask, grad_weight)
for i in range(0, w.size, 7):
point = np.zeros(*w.shape)
point[i] = 1
assert_almost_equal(sp.optimize.check_grad(func, func_grad, point),
0, decimal=3)
def test_logistic_gradient_at_simple_points():
# Tests gradient of logistic data loss function in points near to zero.
# This is a not so hard test, just for detecting big errors
X, y, w, mask = create_graph_net_simulation_data(n_samples=10, size=4)
grad_weight = 1
# Add the intercept
w = np.append(w, 0)
func = lambda w: _logistic_data_loss_and_spatial_grad(
X, y, w, mask, grad_weight)
func_grad = lambda w: _logistic_data_loss_and_spatial_grad_derivative(
X, y, w, mask, grad_weight)
for i in range(0, w.size, 7):
point = np.zeros(*w.shape)
point[i] = 1
assert_almost_equal(sp.optimize.check_grad(func, func_grad, point),
0, decimal=3)