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from __future__ import division, print_function, absolute_import
import math
import numpy as np
from numpy.testing import assert_allclose, TestCase, run_module_suite, \
assert_
from scipy.optimize import fmin_cobyla, minimize
class TestCobyla(TestCase):
def setUp(self):
self.x0 = [4.95,0.66]
self.solution = [math.sqrt(25 - (2.0/3)**2), 2.0/3]
self.opts = {'disp': False, 'rhobeg': 1, 'tol': 1e-5,
'maxiter': 100}
def fun(self, x):
return x[0]**2 + abs(x[1])**3
def con1(self, x):
return x[0]**2 + x[1]**2 - 25
def con2(self, x):
return -self.con1(x)
def test_simple(self):
""" fmin_cobyla """
x = fmin_cobyla(self.fun, self.x0, [self.con1, self.con2], rhobeg=1,
rhoend=1e-5, iprint=0, maxfun=100)
assert_allclose(x, self.solution, atol=1e-4)
def test_minimize_simple(self):
""" Minimize with method='COBYLA' """
cons = ({'type': 'ineq', 'fun': self.con1},
{'type': 'ineq', 'fun': self.con2})
sol = minimize(self.fun, self.x0, method='cobyla', constraints=cons,
options=self.opts)
assert_allclose(sol.x, self.solution, atol=1e-4)
assert_(sol.success, sol.message)
assert_(sol.maxcv < 1e-5, sol)
assert_(sol.nfev < 70, sol)
assert_(sol.fun < self.fun(self.solution) + 1e-3, sol)
def test_minimize_constraint_violation(self):
np.random.seed(1234)
pb = np.random.rand(10, 10)
spread = np.random.rand(10)
def p(w):
return pb.dot(w)
def f(w):
return -(w * spread).sum()
def c1(w):
return 500 - abs(p(w)).sum()
def c2(w):
return 5 - abs(p(w).sum())
def c3(w):
return 5 - abs(p(w)).max()
cons = ({'type': 'ineq', 'fun': c1},
{'type': 'ineq', 'fun': c2},
{'type': 'ineq', 'fun': c3})
w0 = np.zeros((10, 1))
sol = minimize(f, w0, method='cobyla', constraints=cons,
options={'catol': 1e-6})
assert_(sol.maxcv > 1e-6)
assert_(not sol.success)
if __name__ == "__main__":
run_module_suite()
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