ai-content-maker/.venv/Lib/site-packages/scipy/optimize/tests/test_differentiable_functio...

759 lines
26 KiB
Python

import pytest
import numpy as np
from numpy.testing import (TestCase, assert_array_almost_equal,
assert_array_equal, assert_, assert_allclose,
assert_equal)
from scipy.sparse import csr_matrix
from scipy.sparse.linalg import LinearOperator
from scipy.optimize._differentiable_functions import (ScalarFunction,
VectorFunction,
LinearVectorFunction,
IdentityVectorFunction)
from scipy.optimize import rosen, rosen_der, rosen_hess
from scipy.optimize._hessian_update_strategy import BFGS
class ExScalarFunction:
def __init__(self):
self.nfev = 0
self.ngev = 0
self.nhev = 0
def fun(self, x):
self.nfev += 1
return 2*(x[0]**2 + x[1]**2 - 1) - x[0]
def grad(self, x):
self.ngev += 1
return np.array([4*x[0]-1, 4*x[1]])
def hess(self, x):
self.nhev += 1
return 4*np.eye(2)
class TestScalarFunction(TestCase):
def test_finite_difference_grad(self):
ex = ExScalarFunction()
nfev = 0
ngev = 0
x0 = [1.0, 0.0]
analit = ScalarFunction(ex.fun, x0, (), ex.grad,
ex.hess, None, (-np.inf, np.inf))
nfev += 1
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev, nfev)
approx = ScalarFunction(ex.fun, x0, (), '2-point',
ex.hess, None, (-np.inf, np.inf))
nfev += 3
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(analit.f, approx.f)
assert_array_almost_equal(analit.g, approx.g)
x = [10, 0.3]
f_analit = analit.fun(x)
g_analit = analit.grad(x)
nfev += 1
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
f_approx = approx.fun(x)
g_approx = approx.grad(x)
nfev += 3
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_almost_equal(f_analit, f_approx)
assert_array_almost_equal(g_analit, g_approx)
x = [2.0, 1.0]
g_analit = analit.grad(x)
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
g_approx = approx.grad(x)
nfev += 3
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_almost_equal(g_analit, g_approx)
x = [2.5, 0.3]
f_analit = analit.fun(x)
g_analit = analit.grad(x)
nfev += 1
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
f_approx = approx.fun(x)
g_approx = approx.grad(x)
nfev += 3
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_almost_equal(f_analit, f_approx)
assert_array_almost_equal(g_analit, g_approx)
x = [2, 0.3]
f_analit = analit.fun(x)
g_analit = analit.grad(x)
nfev += 1
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
f_approx = approx.fun(x)
g_approx = approx.grad(x)
nfev += 3
ngev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_almost_equal(f_analit, f_approx)
assert_array_almost_equal(g_analit, g_approx)
def test_fun_and_grad(self):
ex = ExScalarFunction()
def fg_allclose(x, y):
assert_allclose(x[0], y[0])
assert_allclose(x[1], y[1])
# with analytic gradient
x0 = [2.0, 0.3]
analit = ScalarFunction(ex.fun, x0, (), ex.grad,
ex.hess, None, (-np.inf, np.inf))
fg = ex.fun(x0), ex.grad(x0)
fg_allclose(analit.fun_and_grad(x0), fg)
assert analit.ngev == 1
x0[1] = 1.
fg = ex.fun(x0), ex.grad(x0)
fg_allclose(analit.fun_and_grad(x0), fg)
# with finite difference gradient
x0 = [2.0, 0.3]
sf = ScalarFunction(ex.fun, x0, (), '3-point',
ex.hess, None, (-np.inf, np.inf))
assert sf.ngev == 1
fg = ex.fun(x0), ex.grad(x0)
fg_allclose(sf.fun_and_grad(x0), fg)
assert sf.ngev == 1
x0[1] = 1.
fg = ex.fun(x0), ex.grad(x0)
fg_allclose(sf.fun_and_grad(x0), fg)
def test_finite_difference_hess_linear_operator(self):
ex = ExScalarFunction()
nfev = 0
ngev = 0
nhev = 0
x0 = [1.0, 0.0]
analit = ScalarFunction(ex.fun, x0, (), ex.grad,
ex.hess, None, (-np.inf, np.inf))
nfev += 1
ngev += 1
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev, nhev)
approx = ScalarFunction(ex.fun, x0, (), ex.grad,
'2-point', None, (-np.inf, np.inf))
assert_(isinstance(approx.H, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_equal(analit.f, approx.f)
assert_array_almost_equal(analit.g, approx.g)
assert_array_almost_equal(analit.H.dot(v), approx.H.dot(v))
nfev += 1
ngev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [2.0, 1.0]
H_analit = analit.hess(x)
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
H_approx = approx.hess(x)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v))
ngev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [2.1, 1.2]
H_analit = analit.hess(x)
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
H_approx = approx.hess(x)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v))
ngev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [2.5, 0.3]
_ = analit.grad(x)
H_analit = analit.hess(x)
ngev += 1
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
_ = approx.grad(x)
H_approx = approx.hess(x)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v))
ngev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [5.2, 2.3]
_ = analit.grad(x)
H_analit = analit.hess(x)
ngev += 1
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
_ = approx.grad(x)
H_approx = approx.hess(x)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v))
ngev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.ngev, ngev)
assert_array_equal(analit.ngev+approx.ngev, ngev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
def test_x_storage_overlap(self):
# Scalar_Function should not store references to arrays, it should
# store copies - this checks that updating an array in-place causes
# Scalar_Function.x to be updated.
def f(x):
return np.sum(np.asarray(x) ** 2)
x = np.array([1., 2., 3.])
sf = ScalarFunction(f, x, (), '3-point', lambda x: x, None, (-np.inf, np.inf))
assert x is not sf.x
assert_equal(sf.fun(x), 14.0)
assert x is not sf.x
x[0] = 0.
f1 = sf.fun(x)
assert_equal(f1, 13.0)
x[0] = 1
f2 = sf.fun(x)
assert_equal(f2, 14.0)
assert x is not sf.x
# now test with a HessianUpdate strategy specified
hess = BFGS()
x = np.array([1., 2., 3.])
sf = ScalarFunction(f, x, (), '3-point', hess, None, (-np.inf, np.inf))
assert x is not sf.x
assert_equal(sf.fun(x), 14.0)
assert x is not sf.x
x[0] = 0.
f1 = sf.fun(x)
assert_equal(f1, 13.0)
x[0] = 1
f2 = sf.fun(x)
assert_equal(f2, 14.0)
assert x is not sf.x
# gh13740 x is changed in user function
def ff(x):
x *= x # overwrite x
return np.sum(x)
x = np.array([1., 2., 3.])
sf = ScalarFunction(
ff, x, (), '3-point', lambda x: x, None, (-np.inf, np.inf)
)
assert x is not sf.x
assert_equal(sf.fun(x), 14.0)
assert_equal(sf.x, np.array([1., 2., 3.]))
assert x is not sf.x
def test_lowest_x(self):
# ScalarFunction should remember the lowest func(x) visited.
x0 = np.array([2, 3, 4])
sf = ScalarFunction(rosen, x0, (), rosen_der, rosen_hess,
None, None)
sf.fun([1, 1, 1])
sf.fun(x0)
sf.fun([1.01, 1, 1.0])
sf.grad([1.01, 1, 1.0])
assert_equal(sf._lowest_f, 0.0)
assert_equal(sf._lowest_x, [1.0, 1.0, 1.0])
sf = ScalarFunction(rosen, x0, (), '2-point', rosen_hess,
None, (-np.inf, np.inf))
sf.fun([1, 1, 1])
sf.fun(x0)
sf.fun([1.01, 1, 1.0])
sf.grad([1.01, 1, 1.0])
assert_equal(sf._lowest_f, 0.0)
assert_equal(sf._lowest_x, [1.0, 1.0, 1.0])
def test_float_size(self):
x0 = np.array([2, 3, 4]).astype(np.float32)
# check that ScalarFunction/approx_derivative always send the correct
# float width
def rosen_(x):
assert x.dtype == np.float32
return rosen(x)
sf = ScalarFunction(rosen_, x0, (), '2-point', rosen_hess,
None, (-np.inf, np.inf))
res = sf.fun(x0)
assert res.dtype == np.float32
class ExVectorialFunction:
def __init__(self):
self.nfev = 0
self.njev = 0
self.nhev = 0
def fun(self, x):
self.nfev += 1
return np.array([2*(x[0]**2 + x[1]**2 - 1) - x[0],
4*(x[0]**3 + x[1]**2 - 4) - 3*x[0]], dtype=x.dtype)
def jac(self, x):
self.njev += 1
return np.array([[4*x[0]-1, 4*x[1]],
[12*x[0]**2-3, 8*x[1]]], dtype=x.dtype)
def hess(self, x, v):
self.nhev += 1
return v[0]*4*np.eye(2) + v[1]*np.array([[24*x[0], 0],
[0, 8]])
class TestVectorialFunction(TestCase):
def test_finite_difference_jac(self):
ex = ExVectorialFunction()
nfev = 0
njev = 0
x0 = [1.0, 0.0]
analit = VectorFunction(ex.fun, x0, ex.jac, ex.hess, None, None,
(-np.inf, np.inf), None)
nfev += 1
njev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev, njev)
approx = VectorFunction(ex.fun, x0, '2-point', ex.hess, None, None,
(-np.inf, np.inf), None)
nfev += 3
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(analit.f, approx.f)
assert_array_almost_equal(analit.J, approx.J)
x = [10, 0.3]
f_analit = analit.fun(x)
J_analit = analit.jac(x)
nfev += 1
njev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
f_approx = approx.fun(x)
J_approx = approx.jac(x)
nfev += 3
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_almost_equal(f_analit, f_approx)
assert_array_almost_equal(J_analit, J_approx, decimal=4)
x = [2.0, 1.0]
J_analit = analit.jac(x)
njev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
J_approx = approx.jac(x)
nfev += 3
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_almost_equal(J_analit, J_approx)
x = [2.5, 0.3]
f_analit = analit.fun(x)
J_analit = analit.jac(x)
nfev += 1
njev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
f_approx = approx.fun(x)
J_approx = approx.jac(x)
nfev += 3
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_almost_equal(f_analit, f_approx)
assert_array_almost_equal(J_analit, J_approx)
x = [2, 0.3]
f_analit = analit.fun(x)
J_analit = analit.jac(x)
nfev += 1
njev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
f_approx = approx.fun(x)
J_approx = approx.jac(x)
nfev += 3
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_almost_equal(f_analit, f_approx)
assert_array_almost_equal(J_analit, J_approx)
def test_finite_difference_hess_linear_operator(self):
ex = ExVectorialFunction()
nfev = 0
njev = 0
nhev = 0
x0 = [1.0, 0.0]
v0 = [1.0, 2.0]
analit = VectorFunction(ex.fun, x0, ex.jac, ex.hess, None, None,
(-np.inf, np.inf), None)
nfev += 1
njev += 1
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev, nhev)
approx = VectorFunction(ex.fun, x0, ex.jac, '2-point', None, None,
(-np.inf, np.inf), None)
assert_(isinstance(approx.H, LinearOperator))
for p in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_equal(analit.f, approx.f)
assert_array_almost_equal(analit.J, approx.J)
assert_array_almost_equal(analit.H.dot(p), approx.H.dot(p))
nfev += 1
njev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [2.0, 1.0]
H_analit = analit.hess(x, v0)
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
H_approx = approx.hess(x, v0)
assert_(isinstance(H_approx, LinearOperator))
for p in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(p), H_approx.dot(p),
decimal=5)
njev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [2.1, 1.2]
v = [1.0, 1.0]
H_analit = analit.hess(x, v)
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
H_approx = approx.hess(x, v)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v))
njev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [2.5, 0.3]
_ = analit.jac(x)
H_analit = analit.hess(x, v0)
njev += 1
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
_ = approx.jac(x)
H_approx = approx.hess(x, v0)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v), decimal=4)
njev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
x = [5.2, 2.3]
v = [2.3, 5.2]
_ = analit.jac(x)
H_analit = analit.hess(x, v)
njev += 1
nhev += 1
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
_ = approx.jac(x)
H_approx = approx.hess(x, v)
assert_(isinstance(H_approx, LinearOperator))
for v in ([1.0, 2.0], [3.0, 4.0], [5.0, 2.0]):
assert_array_almost_equal(H_analit.dot(v), H_approx.dot(v), decimal=4)
njev += 4
assert_array_equal(ex.nfev, nfev)
assert_array_equal(analit.nfev+approx.nfev, nfev)
assert_array_equal(ex.njev, njev)
assert_array_equal(analit.njev+approx.njev, njev)
assert_array_equal(ex.nhev, nhev)
assert_array_equal(analit.nhev+approx.nhev, nhev)
def test_x_storage_overlap(self):
# VectorFunction should not store references to arrays, it should
# store copies - this checks that updating an array in-place causes
# Scalar_Function.x to be updated.
ex = ExVectorialFunction()
x0 = np.array([1.0, 0.0])
vf = VectorFunction(ex.fun, x0, '3-point', ex.hess, None, None,
(-np.inf, np.inf), None)
assert x0 is not vf.x
assert_equal(vf.fun(x0), ex.fun(x0))
assert x0 is not vf.x
x0[0] = 2.
assert_equal(vf.fun(x0), ex.fun(x0))
assert x0 is not vf.x
x0[0] = 1.
assert_equal(vf.fun(x0), ex.fun(x0))
assert x0 is not vf.x
# now test with a HessianUpdate strategy specified
hess = BFGS()
x0 = np.array([1.0, 0.0])
vf = VectorFunction(ex.fun, x0, '3-point', hess, None, None,
(-np.inf, np.inf), None)
with pytest.warns(UserWarning):
# filter UserWarning because ExVectorialFunction is linear and
# a quasi-Newton approximation is used for the Hessian.
assert x0 is not vf.x
assert_equal(vf.fun(x0), ex.fun(x0))
assert x0 is not vf.x
x0[0] = 2.
assert_equal(vf.fun(x0), ex.fun(x0))
assert x0 is not vf.x
x0[0] = 1.
assert_equal(vf.fun(x0), ex.fun(x0))
assert x0 is not vf.x
def test_float_size(self):
ex = ExVectorialFunction()
x0 = np.array([1.0, 0.0]).astype(np.float32)
vf = VectorFunction(ex.fun, x0, ex.jac, ex.hess, None, None,
(-np.inf, np.inf), None)
res = vf.fun(x0)
assert res.dtype == np.float32
res = vf.jac(x0)
assert res.dtype == np.float32
def test_LinearVectorFunction():
A_dense = np.array([
[-1, 2, 0],
[0, 4, 2]
])
x0 = np.zeros(3)
A_sparse = csr_matrix(A_dense)
x = np.array([1, -1, 0])
v = np.array([-1, 1])
Ax = np.array([-3, -4])
f1 = LinearVectorFunction(A_dense, x0, None)
assert_(not f1.sparse_jacobian)
f2 = LinearVectorFunction(A_dense, x0, True)
assert_(f2.sparse_jacobian)
f3 = LinearVectorFunction(A_dense, x0, False)
assert_(not f3.sparse_jacobian)
f4 = LinearVectorFunction(A_sparse, x0, None)
assert_(f4.sparse_jacobian)
f5 = LinearVectorFunction(A_sparse, x0, True)
assert_(f5.sparse_jacobian)
f6 = LinearVectorFunction(A_sparse, x0, False)
assert_(not f6.sparse_jacobian)
assert_array_equal(f1.fun(x), Ax)
assert_array_equal(f2.fun(x), Ax)
assert_array_equal(f1.jac(x), A_dense)
assert_array_equal(f2.jac(x).toarray(), A_sparse.toarray())
assert_array_equal(f1.hess(x, v).toarray(), np.zeros((3, 3)))
def test_LinearVectorFunction_memoization():
A = np.array([[-1, 2, 0], [0, 4, 2]])
x0 = np.array([1, 2, -1])
fun = LinearVectorFunction(A, x0, False)
assert_array_equal(x0, fun.x)
assert_array_equal(A.dot(x0), fun.f)
x1 = np.array([-1, 3, 10])
assert_array_equal(A, fun.jac(x1))
assert_array_equal(x1, fun.x)
assert_array_equal(A.dot(x0), fun.f)
assert_array_equal(A.dot(x1), fun.fun(x1))
assert_array_equal(A.dot(x1), fun.f)
def test_IdentityVectorFunction():
x0 = np.zeros(3)
f1 = IdentityVectorFunction(x0, None)
f2 = IdentityVectorFunction(x0, False)
f3 = IdentityVectorFunction(x0, True)
assert_(f1.sparse_jacobian)
assert_(not f2.sparse_jacobian)
assert_(f3.sparse_jacobian)
x = np.array([-1, 2, 1])
v = np.array([-2, 3, 0])
assert_array_equal(f1.fun(x), x)
assert_array_equal(f2.fun(x), x)
assert_array_equal(f1.jac(x).toarray(), np.eye(3))
assert_array_equal(f2.jac(x), np.eye(3))
assert_array_equal(f1.hess(x, v).toarray(), np.zeros((3, 3)))