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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():