def runTest(self):
n = 2
npt = (n + 1) * (n + 2) // 2
x0 = np.array([1.0, 1.0])
xl = -1e2 * np.ones((n, ))
xu = 1e2 * np.ones((n, ))
model = Model(npt, x0, objfun(x0), xl, xu, 1)
x1 = x0 + np.array([1.0, 0.0])
model.change_point(1, x1 - model.xbase, objfun(x1))
x2 = x0 + np.array([0.1, 0.9])
model.change_point(2, x2 - model.xbase, objfun(x2))
x3 = x0 + np.array([-0.1, 0.0])
model.change_point(3, x3 - model.xbase, objfun(x3))
x4 = x0 + np.array([-0.1, 2.0])
model.change_point(4, x4 - model.xbase, objfun(x4))
x5 = x0 + np.array([-1.1, 1.0])
model.change_point(5, x5 - model.xbase, objfun(x5))
xopt = model.xopt()
for i in range(npt):
c, g, hess = model.lagrange_polynomial(i) # based at xopt
for j in range(npt):
dx = model.xpt(j) - xopt
lag_value = c + model_value(g, hess, dx)
expected_value = 1.0 if i == j else 0.0
self.assertAlmostEqual(
lag_value,
expected_value,
msg="Lagrange for x%g has bad value at x%g" % (i, j))
def runTest(self):
n = 2
npt = n + 1
x0 = np.array([-1.2, 1.0])
xl = -1e2 * np.ones((n, ))
xu = 1e2 * np.ones((n, ))
model = Model(npt, x0, rosenbrock(x0), xl, xu, 1)
x1 = np.array([1.0, 0.9])
model.change_point(1, x1 - model.xbase, rosenbrock(x1))
x2 = np.array([2.0, 0.9])
model.change_point(2, x2 - model.xbase, rosenbrock(x2))
xopt = model.xopt()
for i in range(npt):
c, g, hess = model.lagrange_polynomial(i) # based at xopt
for j in range(npt):
dx = model.xpt(j) - xopt
lag_value = c + model_value(g, hess, dx)
expected_value = 1.0 if i == j else 0.0
self.assertAlmostEqual(
lag_value,
expected_value,
msg="Lagrange for x%g has bad value at x%g" % (i, j))