def testRosen(self):
N = 20
f = rosen
df = rosen_der
opt = CGD()
restarts = 10
for _ in range(restarts):
try:
x0 = (numpy.random.randn(N) * .5) + numpy.ones(N)
res = opt.opt(f, df, x0, messages=0,
maxiter=1e3, gtol=1e-12)
assert numpy.allclose(res[0], 1, atol=.1)
break
except:
# RESTART
pass
else:
raise AssertionError("Test failed for {} restarts".format(restarts))
def testMinimizeSquare(self):
N = 100
A = numpy.random.rand(N) * numpy.eye(N)
b = numpy.random.rand(N) * 0
f = lambda x: numpy.dot(x.T.dot(A), x) - numpy.dot(x.T, b)
df = lambda x: numpy.dot(A, x) - b
opt = CGD()
restarts = 10
for _ in range(restarts):
try:
x0 = numpy.random.randn(N) * 10
res = opt.opt(f, df, x0, messages=0, maxiter=1000, gtol=1e-15)
assert numpy.allclose(res[0], 0, atol=1e-5)
break
except AssertionError:
import pdb;pdb.set_trace()
# RESTART
pass
else:
raise AssertionError("Test failed for {} restarts".format(restarts))
# 'Test.testMinimizeSquare',
# 'Test.testRosen',
# ]
# unittest.main()
N = 2
A = numpy.random.rand(N) * numpy.eye(N)
b = numpy.random.rand(N) * 0
f = lambda x: numpy.dot(x.T.dot(A), x) - numpy.dot(x.T, b)
df = lambda x: numpy.dot(A, x) - b
# f = rosen
# df = rosen_der
x0 = (numpy.random.randn(N) * .5) + numpy.ones(N)
print x0
opt = CGD()
pylab.ion()
fig = pylab.figure("cgd optimize")
if fig.axes:
ax = fig.axes[0]
ax.cla()
else:
ax = fig.add_subplot(111, projection='3d')
interpolation = 40
# x, y = numpy.linspace(.5, 1.5, interpolation)[:, None], numpy.linspace(.5, 1.5, interpolation)[:, None]
x, y = numpy.linspace(-1, 1, interpolation)[:, None], numpy.linspace(-1, 1, interpolation)[:, None]
X, Y = numpy.meshgrid(x, y)
fXY = numpy.array([f(numpy.array([x, y])) for x, y in zip(X.flatten(), Y.flatten())]).reshape(interpolation, interpolation)