def test_1_call():
import example1
ported_eq(example1.f(3), 3)
ported_eq("description" in example1.f.__doc__, True)
ported_eq(example1.f.__name__, "f")
delta = 0.025
x = np.arange(min([x0[0],x1[0],x2[0],x_star[0]])-0.5, \
max([x0[0],x1[0],x2[0],x_star[0]])+0.5, \
delta)
y = np.arange(min([x0[1],x1[1],x2[1],x_star[1]])-0.5, \
max([x0[1],x1[1],x2[1],x_star[1]])+0.5, \
delta)
X, Y = np.meshgrid(x, y)
dim = np.shape(X)
# Compute function values
Xv = X.flatten()
Yv = Y.flatten()
Input = zip(Xv, Yv)
Zv = []
for x in Input:
Zv.append(f(x))
# Restore arrays to proper dimensions
Z = np.array(Zv).reshape(dim)
# Open figure
plt.figure()
# Plot contour
CS = plt.contour(X, Y, Z)
plt.clabel(CS, inline=1, fontsize=10)
plt.title('Sequence of Iterates, Example 1')
# plt.title('Sequence of Iterates, Example 2')
plt.plot(x_star[0], x_star[1], 'ok') # optimum
plt.plot(x0[0], x0[1], 'ob') # starting point
plt.plot(x1[0], x1[1], 'or')
plt.plot(x2[0], x2[1], 'og')
if __name__ == "__main__":
# # Test ad on an external objective
# from ad import gh
# from example1 import g
# s = 1e-1
# G0 = lambda x: ext_obj(g(adnumber(x)),s)
# dG0, _ = gh(G0)
# # Return ordinary values
# G = lambda x: G0(x).x
# dG= lambda x: dG0(x).T
# # Evaluate
# x0 = np.array([2.5,2.5])
# val = G0(x0)
# dif = dG0(x0)
# # Run BFGS
# from scipy.optimize import minimize
# res = minimize(G, x0, method='BFGS', jac=dG0)
# Test ad on interior objective
from ad import gh
from example1 import g, f
s = 1e-1
fcn = lambda x: f(x) + log_barrier(g(x)) / 5.0
dfcn, _ = gh(fcn)
# Run BFGS
from scipy.optimize import minimize
x0 = [0, 0]
res = minimize(fcn, x0, method='BFGS', jac=dfcn)
return res
if __name__ == "__main__":
# # Test ad on an external objective
# from ad import gh
# from example1 import g
# s = 1e-1
# G0 = lambda x: ext_obj(g(adnumber(x)),s)
# dG0, _ = gh(G0)
# # Return ordinary values
# G = lambda x: G0(x).x
# dG= lambda x: dG0(x).T
# # Evaluate
# x0 = np.array([2.5,2.5])
# val = G0(x0)
# dif = dG0(x0)
# # Run BFGS
# from scipy.optimize import minimize
# res = minimize(G, x0, method='BFGS', jac=dG0)
# Test ad on interior objective
from ad import gh
from example1 import g,f
s = 1e-1
fcn = lambda x: f(x) + log_barrier(g(x))/5.0
dfcn, _ = gh(fcn)
# Run BFGS
from scipy.optimize import minimize
x0 = [0,0]
res = minimize(fcn, x0, method='BFGS', jac=dfcn)
def test_1_call(self):
import example1
eq_(example1.f(3), 3)
eq_('description' in example1.f.__doc__, True)
eq_(example1.f.__name__, 'f')
def test_1_call():
import example1
assert example1.f(3) == 3
assert "description" in example1.f.__doc__
assert example1.f.__name__ == "f"