Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/optimize/tests/test_cobyla.py

@@ -0,0 +1,113 @@
+import math
+import numpy as np
+
+from numpy.testing import assert_allclose, assert_
+
+from scipy.optimize import fmin_cobyla, minimize
+
+
+class TestCobyla(object):
+    def setup_method(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):
+        # use disp=True as smoke test for gh-8118
+        x = fmin_cobyla(self.fun, self.x0, [self.con1, self.con2], rhobeg=1,
+                        rhoend=1e-5, maxfun=100, disp=True)
+        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)
+
+
+def test_vector_constraints():
+    # test that fmin_cobyla and minimize can take a combination
+    # of constraints, some returning a number and others an array
+    def fun(x):
+        return (x[0] - 1)**2 + (x[1] - 2.5)**2
+
+    def fmin(x):
+        return fun(x) - 1
+
+    def cons1(x):
+        a = np.array([[1, -2, 2], [-1, -2, 6], [-1, 2, 2]])
+        return np.array([a[i, 0] * x[0] + a[i, 1] * x[1] +
+                         a[i, 2] for i in range(len(a))])
+
+    def cons2(x):
+        return x     # identity, acts as bounds x > 0
+
+    x0 = np.array([2, 0])
+    cons_list = [fun, cons1, cons2]
+
+    xsol = [1.4, 1.7]
+    fsol = 0.8
+
+    # testing fmin_cobyla
+    sol = fmin_cobyla(fun, x0, cons_list, rhoend=1e-5)
+    assert_allclose(sol, xsol, atol=1e-4)
+
+    sol = fmin_cobyla(fun, x0, fmin, rhoend=1e-5)
+    assert_allclose(fun(sol), 1, atol=1e-4)
+
+    # testing minimize
+    constraints = [{'type': 'ineq', 'fun': cons} for cons in cons_list]
+    sol = minimize(fun, x0, constraints=constraints, tol=1e-5)
+    assert_allclose(sol.x, xsol, atol=1e-4)
+    assert_(sol.success, sol.message)
+    assert_allclose(sol.fun, fsol, atol=1e-4)
+
+    constraints = {'type': 'ineq', 'fun': fmin}
+    sol = minimize(fun, x0, constraints=constraints, tol=1e-5)
+    assert_allclose(sol.fun, 1, atol=1e-4)
+