Source code for numdifftools.tests.test_numdifftools

"""Test functions for numdifftools module"""
from __future__ import print_function
import unittest
import numdifftools.core as nd
import numpy as np
from numpy.testing import assert_array_almost_equal


[docs]class TestGlobalFunctions(unittest.TestCase):
[docs] def testdea3(self): def linfun(k): return np.linspace(0, np.pi / 2., 2. ** (k + 5) + 1) Ei = np.zeros(3) for k in np.arange(3): x = linfun(k) Ei[k] = np.trapz(np.sin(x), x) [En, err] = nd.dea3(Ei[0], Ei[1], Ei[2]) self.assertTrue(np.abs(En - 1) < err) assert_array_almost_equal(En, 1.0, decimal=8)
[docs]class TestRichardson(unittest.TestCase):
[docs] def test_central(self): method = 'central' true_vals = {(1, 1): [-0.33333333, 1.33333333], (1, 2): [-0.33333333, 1.33333333], (1, 3): [-0.33333333, 1.33333333], (1, 4): [-0.06666667, 1.06666667], (1, 5): [-0.06666667, 1.06666667], (1, 6): [-0.01587302, 1.01587302], (2, 1): [0.02222222, -0.44444444, 1.42222222], (2, 2): [0.02222222, -0.44444444, 1.42222222], (2, 3): [0.02222222, -0.44444444, 1.42222222], (2, 4): [1.05820106e-03, -8.46560847e-02, 1.08359788e+00], (2, 5): [1.05820106e-03, -8.46560847e-02, 1.08359788e+00], (2, 6): [6.22471211e-05, -1.99190787e-02, 1.01985683e+00]} for num_terms in [1, 2]: for order in range(1, 7): d = nd.Derivative(np.exp, method=method, order=order) d._set_richardson_rule(step_ratio=2.0, num_terms=num_terms) rule = d._richardson_extrapolate._get_richardson_rule() assert_array_almost_equal(rule, true_vals[(num_terms, order)])
[docs] def test_complex(self): truth = { (1, 2, 8): [9.576480164718605e-07, -0.004167684167715291, 1.004166726519699], (4, 2, 2): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (1, 2, 4): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (4, 1, 8): [-0.0039215686274510775, 1.0039215686274505], (2, 2, 4): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (4, 2, 8): [9.576480164718605e-07, -0.004167684167715291, 1.004166726519699], (3, 1, 8): [-0.0039215686274510775, 1.0039215686274505], (4, 1, 2): [-0.06666666666666654, 1.0666666666666664], (3, 1, 6): [-0.06666666666666654, 1.0666666666666664], (1, 1, 8): [-0.0039215686274510775, 1.0039215686274505], (2, 1, 8): [-0.0039215686274510775, 1.0039215686274505], (4, 1, 4): [-0.06666666666666654, 1.0666666666666664], (3, 1, 4): [-0.06666666666666654, 1.0666666666666664], (2, 1, 4): [-0.06666666666666654, 1.0666666666666664], (3, 2, 2): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (2, 2, 8): [9.576480164718605e-07, -0.004167684167715291, 1.004166726519699], (2, 1, 6): [-0.06666666666666654, 1.0666666666666664], (3, 1, 2): [-0.06666666666666654, 1.0666666666666664], (4, 1, 6): [-0.06666666666666654, 1.0666666666666664], (1, 1, 6): [-0.06666666666666654, 1.0666666666666664], (1, 2, 2): [0.022222222222222185, -0.444444444444444, 1.4222222222222216], (3, 2, 6): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (1, 1, 4): [-0.06666666666666654, 1.0666666666666664], (2, 1, 2): [-0.06666666666666654, 1.0666666666666664], (4, 2, 4): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (3, 2, 4): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (2, 2, 2): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (1, 2, 6): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (4, 2, 6): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616], (1, 1, 2): [-0.33333333333333304, 1.333333333333333], (3, 2, 8): [9.576480164718605e-07, -0.004167684167715291, 1.004166726519699], (2, 2, 6): [0.0002614379084968331, -0.07111111111111235, 1.070849673202616]} # t = dict() for n in [1, 2, 3, 4]: for num_terms in [1, 2]: for order in range(2, 9, 2): d = nd.Derivative(np.exp, n=n, method='complex', order=order) d._set_richardson_rule(step_ratio=2.0, num_terms=num_terms) rule = d._richardson_extrapolate._get_richardson_rule() # t[(n, num_terms, order)] = rule.tolist() assert_array_almost_equal(rule, truth[(n, num_terms, order)]) # print(t) # self.assert_(False)
[docs] def test_forward_backward(self): truth = {(1, 1): [-1., 2.], (1, 2): [-0.33333333, 1.33333333], (1, 3): [-0.14285714, 1.14285714], (1, 4): [-0.06666667, 1.06666667], (1, 5): [-0.03225806, 1.03225806], (1, 6): [-0.01587302, 1.01587302], (2, 1): [0.33333333, -2., 2.66666667], (2, 2): [0.04761905, -0.57142857, 1.52380952], (2, 3): [0.00952381, -0.22857143, 1.21904762], (2, 4): [0.00215054, -0.10322581, 1.10107527], (2, 5): [5.12032770e-04, -4.91551459e-02, 1.04864311e+00], (2, 6): [1.24984377e-04, -2.39970004e-02, 1.02387202e+00]} for method in ['forward', 'backward']: for num_terms in [1, 2]: for order in range(1, 7): d = nd.Derivative(np.exp, method=method, order=order) d._set_richardson_rule(step_ratio=2.0, num_terms=num_terms) rule = d._richardson_extrapolate._get_richardson_rule() assert_array_almost_equal(rule, truth[(num_terms, order)]) # def _example_(self): # def f(x, h): # return (np.exp(x + h) - np.exp(x - h)) / (2.) # # f = lambda x, h: (np.exp(x+h)-np.exp(x)) # steps = [h for h in 2.0**-np.arange(10)] # df = [f(1, h) for h in steps] # print([dfi / hi for dfi, hi in zip(df, steps)]) # step = nd.MaxStepGenerator(step_ratio=2.0) # for method in ['central']: # d = nd.Derivative(np.exp, step=step, method=method) # for order in [2, 6]: # d.order = order # r_extrap = nd.Richardson(step_ratio=2.0, method=method, # num_terms=2, order=order) # # fd_rule = d._get_finite_difference_rule(step_ratio=2.0) # print(fd_rule) # df1, stepsi, _shape = d._apply_fd_rule(fd_rule, df, steps) # # rule = r_extrap._get_richardson_rule() # df2, error, hi = r_extrap(df1, stepsi) # # print(rule) # print(np.hstack((df2, error))) # # self.assert_(False)
[docs]class TestStepGenerator(unittest.TestCase):
[docs] def test_default_generator(self): step_gen = nd.MinStepGenerator(base_step=None, num_steps=10, step_ratio=4, offset=-1) h = np.array([h for h in step_gen(0)]) desired = np.array([3.58968236e-02, 8.97420590e-03, 2.24355147e-03, 5.60887869e-04, 1.40221967e-04, 3.50554918e-05, 8.76387295e-06, 2.19096824e-06, 5.47742059e-07, 1.36935515e-07]) assert_array_almost_equal((h - desired) / desired, 0)
[docs] def test_default_base_step(self): step_gen = nd.MinStepGenerator(num_steps=1, offset=0) h = [h for h in step_gen(0)] desired = nd.EPS ** (1. / 2.5) assert_array_almost_equal((h[0] - desired) / desired, 0)
[docs] def test_fixed_base_step(self): desired = 0.1 step_gen = nd.MinStepGenerator(base_step=desired, num_steps=1, scale=2, offset=0) h = [h for h in step_gen(0)] assert_array_almost_equal((h[0] - desired) / desired, 0)
[docs]class TestFornbergWeights(unittest.TestCase):
[docs] def test_weights(self): x = np.r_[-1, 0, 1] xbar = 0 k = 1 weights = nd.fornberg_weights(x, xbar, k) np.testing.assert_allclose(weights, [-.5, 0, .5])
[docs]class TestDerivative(unittest.TestCase):
[docs] def test_directional_diff(self): v = [1, -1] x0 = [2, 3] def rosen(x): return (1-x[0])**2 + 105.*(x[1]-x[0]**2)**2 directional_diff = nd.directionaldiff(rosen, x0, v) assert_array_almost_equal(directional_diff, 743.87633380824832)
[docs] def test_infinite_functions(self): def finf(x): return np.inf df = nd.Derivative(finf) val = df(0) self.assert_(np.isnan(val)) df.n = 0 self.assertEqual(df(0), np.inf) # def _example_fd_mat(self): # fdmat = nd.Derivative._fd_matrix(step_ratio=2.0, parity=1, nterms=3) # _fd_rules = np.linalg.pinv(fdmat) # self.assert_(False)
[docs] def test_high_order_derivative_cos(self): true_vals = (0.0, -1.0, 0.0, 1.0, 0.0, -1.0, 0.0) methods = ['complex', 'multicomplex', 'central', 'forward', 'backward'] for method in methods: n_max = dict(multicomplex=2, central=6).get(method, 5) for n in range(0, n_max + 1): true_val = true_vals[n] for order in range(2, 9, 2): d3cos = nd.Derivative(np.cos, n=n, order=order, method=method, full_output=True) y, info = d3cos(np.pi / 2.0) error = np.abs(y - true_val) # small = error <= info.error_estimate # if not small: # small = error < 10**(-12 + n) # if not small: # print('method=%s, n=%d, order=%d' % (method, n, order)) # print(error, info.error_estimate) # self.assertTrue(small) assert_array_almost_equal(y, true_val, decimal=4) # self.assert_(False)
[docs] def test_derivative_of_cos_x(self): x = np.r_[0, np.pi / 6.0, np.pi / 2.0] true_vals = (-np.sin(x), -np.cos(x), np.sin(x), np.cos(x), -np.sin(x), -np.cos(x)) for method in ['complex', 'central', 'forward', 'backward']: n_max = dict(complex=2, central=6).get(method, 5) for n in range(1, n_max + 1): true_val = true_vals[n - 1] start, stop, step = dict(central=(2, 7, 2), complex=(2, 3, 1)).get(method, (1, 5, 1)) for order in range(start, stop, step): d3cos = nd.Derivative(np.cos, n=n, order=order, method=method, full_output=True) y, info = d3cos(x) error = np.abs(y - true_val) # small = error <= info.error_estimate # if not small.all(): # small = np.where(small, small, error <= 10**(-11 + n)) # if not small.all(): # print('method=%s, n=%d, order=%d' % (method, n, order)) # print(error, info.error_estimate) assert_array_almost_equal(y, true_val, decimal=4) # self.assertTrue(small.all()) # assert_allclose(y, true_val) # self.assert_(False)
[docs] def test_default_scale(self): for method, scale in zip(['complex', 'central', 'forward', 'backward', 'multicomplex'], [1.35, 2.5, 2.5, 2.5, 1.35]): np.testing.assert_allclose(scale, nd.default_scale(method, n=1))
[docs] def test_fun_with_additional_parameters(self): """Test for issue #9""" def func(x, a, b=1): return b * a * x * x * x methods = ['forward', 'backward', 'central', 'complex', 'multicomplex'] dfuns = [nd.Gradient, nd.Derivative, nd.Jacobian, nd.Hessdiag, nd.Hessian] for dfun in dfuns: for method in methods: df = dfun(func, method=method) val = df(0.0, 1.0, b=2) assert_array_almost_equal(val, 0)
[docs] def test_derivative_cube(self): """Test for Issue 7""" def cube(x): return x * x * x dcube = nd.Derivative(cube) shape = (3, 2) x = np.ones(shape) * 2 dx = dcube(x) assert_array_almost_equal(list(dx.shape), list(shape), decimal=8, err_msg='Shape mismatch') txt = 'First differing element %d\n value = %g,\n true value = %g' for i, (val, tval) in enumerate(zip(dx.ravel(), (3 * x**2).ravel())): assert_array_almost_equal(val, tval, decimal=8, err_msg=txt % (i, val, tval))
[docs] def test_derivative_exp(self): # derivative of exp(x), at x == 0 dexp = nd.Derivative(np.exp) assert_array_almost_equal(dexp(0), np.exp(0), decimal=8)
[docs] def test_derivative_sin(self): # Evaluate the indicated (default = first) # derivative at multiple points dsin = nd.Derivative(np.sin) x = np.linspace(0, 2. * np.pi, 13) y = dsin(x) np.testing.assert_almost_equal(y, np.cos(x), decimal=8)
[docs] def test_backward_derivative_on_sinh(self): # Compute the derivative of a function using a backward difference # scheme. A backward scheme will only look below x0. dsinh = nd.Derivative(np.sinh, method='backward') self.assertAlmostEqual(dsinh(0.0), np.cosh(0.0))
[docs] def test_central_and_forward_derivative_on_log(self): # Although a central rule may put some samples in the wrong places, it # may still succeed epsilon = nd.MinStepGenerator(num_steps=15, offset=0, step_ratio=2) dlog = nd.Derivative(np.log, method='central', step=epsilon) x = 0.001 self.assertAlmostEqual(dlog(x), 1.0 / x) # But forcing the use of a one-sided rule may be smart anyway dlog = nd.Derivative(np.log, method='forward', step=epsilon) self.assertAlmostEqual(dlog(x), 1 / x)
[docs]class TestJacobian(unittest.TestCase):
[docs] def testjacobian(self): xdata = np.reshape(np.arange(0, 1, 0.1), (-1, 1)) ydata = 1 + 2 * np.exp(0.75 * xdata) def fun(c): return (c[0] + c[1] * np.exp(c[2] * xdata) - ydata) ** 2 for method in ['complex', 'central', 'forward', 'backward']: for order in [2, 4]: Jfun = nd.Jacobian(fun, method=method, order=order) J = Jfun([1, 2, 0.75]) # should be numerically zero assert_array_almost_equal(J, np.zeros(J.shape))
[docs]class TestGradient(unittest.TestCase):
[docs] def test_directional_diff(self): v = np.r_[1, -1] v = v/np.linalg.norm(v) x0 = [2, 3] def rosen(x): return (1-x[0])**2 + 105.*(x[1]-x[0]**2)**2 directional_diff = np.dot(nd.Gradient(rosen)(x0), v) assert_array_almost_equal(directional_diff, 743.87633380824832)
[docs] def testgradient(self): def fun(x): return np.sum(x ** 2) dtrue = [2., 4., 6.] for method in ['complex', 'central', 'backward', 'forward']: for order in [2, 4]: dfun = nd.Gradient(fun, method=method, order=order) d = dfun([1, 2, 3]) assert_array_almost_equal(d, dtrue) # self.assert_(False)
[docs]class TestHessdiag(unittest.TestCase):
[docs] def test_complex(self): def fun(x): return x[0] + x[1] ** 2 + x[2] ** 3 htrue = np.array([0., 2., 18.]) method = 'complex' for num_steps in range(3, 7, 1): steps = nd.MinStepGenerator(num_steps=num_steps, use_exact_steps=True, step_ratio=2.0, offset=4) Hfun = nd.Hessdiag(fun, step=steps, method=method, full_output=True) hd, _info = Hfun([1, 2, 3]) _error = hd - htrue assert_array_almost_equal(hd, htrue)
[docs] def test_fixed_step(self): def fun(x): return x[0] + x[1] ** 2 + x[2] ** 3 htrue = np.array([0., 2., 18.]) methods = ['multicomplex', 'complex', 'central', 'forward', 'backward'] for order in range(2, 7, 2): steps = nd.MinStepGenerator(num_steps=order + 1, use_exact_steps=True, step_ratio=3., offset=0) for method in methods: Hfun = nd.Hessdiag(fun, step=steps, method=method, order=order, full_output=True) hd, _info = Hfun([1, 2, 3]) _error = hd - htrue assert_array_almost_equal(hd, htrue)
[docs] def test_default_step(self): def fun(x): return x[0] + x[1] ** 2 + x[2] ** 3 htrue = np.array([0., 2., 18.]) methods = ['central2', 'central', 'multicomplex', 'complex', 'forward', 'backward'] for order in range(2, 7, 2): for method in methods: Hfun = nd.Hessdiag(fun, method=method, order=order, full_output=True) hd, _info = Hfun([1, 2, 3]) _error = hd - htrue assert_array_almost_equal(hd, htrue)
[docs]class TestHessian(unittest.TestCase):
[docs] def test_hessian_cosIx_yI_at_I0_0I(self): # cos(x-y), at (0,0) def fun(xy): return np.cos(xy[0] - xy[1]) htrue = [[-1., 1.], [1., -1.]] methods = ['multicomplex', 'complex', 'central', 'central2', 'forward', 'backward'] for num_steps in [10, 1]: step = nd.MinStepGenerator(num_steps=num_steps) for method in methods: Hfun2 = nd.Hessian(fun, method=method, step=step, full_output=True) h2, _info = Hfun2([0, 0]) # print(method, (h2-np.array(htrue))) assert_array_almost_equal(h2, htrue)
if __name__ == '__main__': unittest.main()