def test_finite_difference_gradients(self):
def myfun(x):
return 2 - 5*x[0,0] + 4*x[0,1]
np.random.seed(42)
X = np.random.uniform(-1.0,1.0,size=(10,2))
df = gr.finite_difference_gradients(X, myfun)
M = df.shape[0]
df_test = np.tile(np.array([-5.0, 4.0]), (M,1))
np.testing.assert_array_almost_equal(df, df_test, decimal=6)
def test_finite_difference_gradients(self):
def myfun(x):
return 2 - 5 * x[0, 0] + 4 * x[0, 1]
np.random.seed(42)
X = np.random.uniform(-1.0, 1.0, size=(10, 2))
df = gr.finite_difference_gradients(X, myfun)
M = df.shape[0]
df_test = np.tile(np.array([-5.0, 4.0]), (M, 1))
np.testing.assert_array_almost_equal(df, df_test, decimal=6)
def test_finite_difference_gradients(self):
def myfun(x):
return 2 - 5*x[0,0] + 4*x[0,1]
data = helper.load_test_npz('train_points_10_2.npz')
X = data['X'].copy()
df = gr.finite_difference_gradients(X, myfun)
M = df.shape[0]
df_test = np.tile(np.array([-5.0, 4.0]), (M,1))
np.testing.assert_array_almost_equal(df, df_test, decimal=6)