def test2():
state=PerceptronSharedState(40)
gp1=GPerceptron.from_shared_state(state)
gp2=GPerceptron.from_shared_state(state)
gp3=GPerceptron.from_shared_state(state)
print "GP1"
print gp1.w
print gp1.w_avg
print gp1.w_avg_N
print "-------------------------"
gp1.update({"X":1.0, "Y":1.0},{"X":1.0,"Z":1.0},0.2,0.1,0.0)
print gp1.w
print gp1.w_avg
print gp1.w_avg_N
print
print
print "GP2"
print gp2.w
print gp2.w_avg
print gp2.w_avg_N
print "-------------------------"
gp2.update({"X":1.0, "Y":1.0},{"X":1.0,"Z":1.0},0.2,0.1,0.0)
print gp2.w
print gp2.w_avg
print gp2.w_avg_N
print
print
print "GP3"
print gp3.w
print gp3.w_avg
print gp3.w_avg_N
print "-------------------------"
gp3.update({"X":1.0, "Y":1.0},{"X":1.0,"Z":1.0},0.2,0.1,0.0)
print gp3.w
print gp3.w_avg
print gp3.w_avg_N
print
print
print "************* RELOAD *************"
state.save("delme.s",True)
state=PerceptronSharedState.load("delme.s",True)
gp1=GPerceptron.from_shared_state(state)
print gp1.w
print gp1.w_avg
print gp1.w_avg_N
def test2():
state = PerceptronSharedState(40)
gp1 = GPerceptron.from_shared_state(state)
gp2 = GPerceptron.from_shared_state(state)
gp3 = GPerceptron.from_shared_state(state)
print "GP1"
print gp1.w
print gp1.w_avg
print gp1.w_avg_N
print "-------------------------"
gp1.update({"X": 1.0, "Y": 1.0}, {"X": 1.0, "Z": 1.0}, 0.2, 0.1, 0.0)
print gp1.w
print gp1.w_avg
print gp1.w_avg_N
print
print
print "GP2"
print gp2.w
print gp2.w_avg
print gp2.w_avg_N
print "-------------------------"
gp2.update({"X": 1.0, "Y": 1.0}, {"X": 1.0, "Z": 1.0}, 0.2, 0.1, 0.0)
print gp2.w
print gp2.w_avg
print gp2.w_avg_N
print
print
print "GP3"
print gp3.w
print gp3.w_avg
print gp3.w_avg_N
print "-------------------------"
gp3.update({"X": 1.0, "Y": 1.0}, {"X": 1.0, "Z": 1.0}, 0.2, 0.1, 0.0)
print gp3.w
print gp3.w_avg
print gp3.w_avg_N
print
print
print "************* RELOAD *************"
state.save("delme.s", True)
state = PerceptronSharedState.load("delme.s", True)
gp1 = GPerceptron.from_shared_state(state)
print gp1.w
print gp1.w_avg
print gp1.w_avg_N
def __init__(self,model_file_name,fName=None,gp=None,beam_size=40,test_time=False):
self.test_time=test_time
self.features=Features()
self.beam_size=beam_size
self.model=Model.load(model_file_name)
if gp:
self.perceptron=gp
return
elif fName is not None:
self.perceptron_state=PerceptronSharedState.load(fName,retrainable=True)
else:
self.perceptron_state=PerceptronSharedState(5000000)
self.perceptron=GPerceptron.from_shared_state(self.perceptron_state)
def test1():
state=PerceptronSharedState(10000)
dist1=DataGen(1000)
dist2=DataGen(1000)
PS=[]
for x in range(8):
newGP=GPerceptron.from_shared_state(state)
p=multiprocessing.Process(target=one_process,args=(newGP,dist1,dist2))
p.start()
PS.append(p)
for p in PS:
p.join()
print state.w_avg_N_s.value
state.save("xxx2",True)
def test1():
state = PerceptronSharedState(10000)
dist1 = DataGen(1000)
dist2 = DataGen(1000)
PS = []
for x in range(8):
newGP = GPerceptron.from_shared_state(state)
p = multiprocessing.Process(target=one_process,
args=(newGP, dist1, dist2))
p.start()
PS.append(p)
for p in PS:
p.join()
print state.w_avg_N_s.value
state.save("xxx2", True)
def __init__(self,
model_file_name,
fName=None,
gp=None,
beam_size=40,
test_time=False):
self.test_time = test_time
self.features = Features()
self.beam_size = beam_size
self.model = Model.load(model_file_name)
if gp:
self.perceptron = gp
return
elif fName is not None:
self.perceptron_state = PerceptronSharedState.load(
fName, retrainable=True)
else:
self.perceptron_state = PerceptronSharedState(5000000)
self.perceptron = GPerceptron.from_shared_state(self.perceptron_state)
