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conjugate gradient optimizer without callback (no c.join)

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Max Zwiessele 2013-05-08 15:36:38 +01:00
parent bc0bd59874
commit 2d9ecaa042
2 changed files with 18 additions and 16 deletions
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9
GPy/inference/conjugate_gradient_descent.py
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@ -3,7 +3,8 @@ Created on 24 Apr 2013
@author: maxz
'''
from GPy.inference.gradient_descent_update_rules import FletcherReeves
from GPy.inference.gradient_descent_update_rules import FletcherReeves, \
PolakRibiere
from Queue import Empty
from multiprocessing import Value
from multiprocessing.queues import Queue
@ -173,7 +174,7 @@ class Async_Optimize(object):
except Empty:
pass
def opt_async(self, f, df, x0, callback, update_rule=FletcherReeves,
def opt_async(self, f, df, x0, callback, update_rule=PolakRibiere,
messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
report_every=10, *args, **kwargs):
self.runsignal.set()
@ -199,12 +200,12 @@ class Async_Optimize(object):
while self.runsignal.is_set():
try:
p.join(1)
c.join(1)
if c: c.join(1)
except KeyboardInterrupt:
# print "^C"
self.runsignal.clear()
p.join()
c.join()
if c: c.join()
if c and c.is_alive():
# self.runsignal.set()
# while self.runsignal.is_set():

25
GPy/testing/cgd_tests.py
View file

@ -26,12 +26,12 @@ class Test(unittest.TestCase):
restarts = 10
for _ in range(restarts):
try:
x0 = numpy.random.randn(N) * 300
res = opt.opt(f, df, x0, messages=0,
maxiter=1000, gtol=1e-10)
assert numpy.allclose(res[0], 0, atol=1e-3)
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:
except AssertionError:
import ipdb;ipdb.set_trace()
# RESTART
pass
else:
@ -47,9 +47,9 @@ class Test(unittest.TestCase):
restarts = 10
for _ in range(restarts):
try:
x0 = numpy.random.randn(N) * .5
x0 = (numpy.random.randn(N) * .5) + numpy.ones(N)
res = opt.opt(f, df, x0, messages=0,
maxiter=5e2, gtol=1e-2)
maxiter=1e3, gtol=1e-12)
assert numpy.allclose(res[0], 1, atol=.1)
break
except:
@ -68,10 +68,10 @@ if __name__ == "__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
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
@ -86,7 +86,8 @@ if __name__ == "__main__":
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(.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)