""" Unit tests for optimization routines from _root.py. """ from numpy.testing import assert_ from pytest import raises as assert_raises import numpy as np from scipy.optimize import root class TestRoot(object): def test_tol_parameter(self): # Check that the minimize() tol= argument does something def func(z): x, y = z return np.array([x**3 - 1, y**3 - 1]) def dfunc(z): x, y = z return np.array([[3*x**2, 0], [0, 3*y**2]]) for method in ['hybr', 'lm', 'broyden1', 'broyden2', 'anderson', 'diagbroyden', 'krylov']: if method in ('linearmixing', 'excitingmixing'): # doesn't converge continue if method in ('hybr', 'lm'): jac = dfunc else: jac = None sol1 = root(func, [1.1,1.1], jac=jac, tol=1e-4, method=method) sol2 = root(func, [1.1,1.1], jac=jac, tol=0.5, method=method) msg = "%s: %s vs. %s" % (method, func(sol1.x), func(sol2.x)) assert_(sol1.success, msg) assert_(sol2.success, msg) assert_(abs(func(sol1.x)).max() < abs(func(sol2.x)).max(), msg) def test_tol_norm(self): def norm(x): return abs(x[0]) for method in ['excitingmixing', 'diagbroyden', 'linearmixing', 'anderson', 'broyden1', 'broyden2', 'krylov']: root(np.zeros_like, np.zeros(2), method=method, options={"tol_norm": norm}) def test_minimize_scalar_coerce_args_param(self): # github issue #3503 def func(z, f=1): x, y = z return np.array([x**3 - 1, y**3 - f]) root(func, [1.1, 1.1], args=1.5) def test_f_size(self): # gh8320 # check that decreasing the size of the returned array raises an error # and doesn't segfault class fun(object): def __init__(self): self.count = 0 def __call__(self, x): self.count += 1 if not (self.count % 5): ret = x[0] + 0.5 * (x[0] - x[1]) ** 3 - 1.0 else: ret = ([x[0] + 0.5 * (x[0] - x[1]) ** 3 - 1.0, 0.5 * (x[1] - x[0]) ** 3 + x[1]]) return ret F = fun() with assert_raises(ValueError): root(F, [0.1, 0.0], method='lm')