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 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 __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)