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Removed PubSub based communication.

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Rowan Cockett
2013-11-21 15:12:28 -08:00
parent 5f64ea8399
commit 0bd971c22f
1 changed files with 0 additions and 36 deletions
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SimPEG/inverse/Optimize.py
-36
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@@ -5,12 +5,6 @@ norm = np.linalg.norm
import scipy.sparse as sp
from SimPEG import Solver
try:
from pubsub import pub
doPub = True
except Exception, e:
print 'Warning: you may not have the required pubsub installed, use pypubsub. You will not be able to listen to events.'
doPub = False
class StoppingCriteria(object):
"""docstring for StoppingCriteria"""
@@ -142,27 +136,6 @@ class Minimize(object):
return out if len(out) > 1 else out[0]
Events are fired with the following inputs via pypubsub::
Minimize.printInit (minimize)
Minimize.evalFunction (minimize, f, g, H)
Minimize.printIter (minimize)
Minimize.searchDirection (minimize, p)
Minimize.scaleSearchDirection (minimize, p)
Minimize.modifySearchDirection (minimize, xt, passLS)
Minimize.endIteration (minimize, xt)
Minimize.printDone (minimize)
To hook into one of these events (must have pypubsub installed)::
from pubsub import pub
def listener(minimize,p):
print 'The search direction is: ', p
pub.subscribe(listener, 'Minimize.searchDirection')
You can use pubsub communication to debug your code, it is not used internally.
The algorithm for general minimization is as follows::
startup(x0)
@@ -189,20 +162,15 @@ class Minimize(object):
while True:
self.f, self.g, self.H = evalFunction(self.xc, return_g=True, return_H=True)
if doPub: pub.sendMessage('Minimize.evalFunction', minimize=self, f=self.f, g=self.g, H=self.H)
self.printIter()
if self.stoppingCriteria(): break
p = self.findSearchDirection()
if doPub: pub.sendMessage('Minimize.searchDirection', minimize=self, p=p)
p = self.scaleSearchDirection(p)
if doPub: pub.sendMessage('Minimize.scaleSearchDirection', minimize=self, p=p)
xt, passLS = self.modifySearchDirection(p)
if doPub: pub.sendMessage('Minimize.modifySearchDirection', minimize=self, xt=xt, passLS=passLS)
if not passLS:
xt, caught = self.modifySearchDirectionBreak(p)
if not caught: return self.xc
self.doEndIteration(xt)
if doPub: pub.sendMessage('Minimize.endIteration', minimize=self, xt=xt)
self.printDone()
@@ -857,10 +825,6 @@ if __name__ == '__main__':
x0 = np.array([2.6, 3.7])
checkDerivative(Rosenbrock, x0, plotIt=False)
# def listener1(minimize,p):
# print 'hi: ', p
# if doPub: pub.subscribe(listener1, 'Minimize.searchDirection')
xOpt = GaussNewton(maxIter=20,tolF=1e-10,tolX=1e-10,tolG=1e-10).minimize(Rosenbrock,x0)
print "xOpt=[%f, %f]" % (xOpt[0], xOpt[1])
xOpt = SteepestDescent(maxIter=30, maxIterLS=15,tolF=1e-10,tolX=1e-10,tolG=1e-10).minimize(Rosenbrock, x0)