Python实现的径向基（RBF）神经网络示例
本文实例讲述了Python实现的径向基（RBF）神经网络。分享给大家供大家参考，具体如下：
fromnumpyimportarray,append,vstack,transpose,reshape,\
dot,true_divide,mean,exp,sqrt,log,\
loadtxt,savetxt,zeros,frombuffer
fromnumpy.linalgimportnorm,lstsq
frommultiprocessingimportProcess,Array
fromrandomimportsample
fromtimeimporttime
fromsysimportstdout
fromctypesimportc_double
fromh5pyimportFile
defmetrics(a,b):
returnnorm(a-b)
defgaussian(x,mu,sigma):
returnexp(-metrics(mu,x)**2/(2*sigma**2))
defmultiQuadric(x,mu,sigma):
returnpow(metrics(mu,x)**2+sigma**2,0.5)
definvMultiQuadric(x,mu,sigma):
returnpow(metrics(mu,x)**2+sigma**2,-0.5)
defplateSpine(x,mu):
r=metrics(mu,x)
return(r**2)*log(r)
classRbf:
def__init__(self,prefix='rbf',workers=4,extra_neurons=0,from_files=None):
self.prefix=prefix
self.workers=workers
self.extra_neurons=extra_neurons
#Importpartialmodel
iffrom_filesisnotNone:
w_handle=self.w_handle=File(from_files['w'],'r')
mu_handle=self.mu_handle=File(from_files['mu'],'r')
sigma_handle=self.sigma_handle=File(from_files['sigma'],'r')
self.w=w_handle['w']
self.mu=mu_handle['mu']
self.sigmas=sigma_handle['sigmas']
self.neurons=self.sigmas.shape[0]
def_calculate_error(self,y):
self.error=mean(abs(self.os-y))
self.relative_error=true_divide(self.error,mean(y))
def_generate_mu(self,x):
n=self.n
extra_neurons=self.extra_neurons
#TODO:Makereusable
mu_clusters=loadtxt('clusters100.txt',delimiter='\t')
mu_indices=sample(range(n),extra_neurons)
mu_new=x[mu_indices,:]
mu=vstack((mu_clusters,mu_new))
returnmu
def_calculate_sigmas(self):
neurons=self.neurons
mu=self.mu
sigmas=zeros((neurons,))
foriinxrange(neurons):
dists=[0for_inxrange(neurons)]
forjinxrange(neurons):
ifi!=j:
dists[j]=metrics(mu[i],mu[j])
sigmas[i]=mean(dists)*2
#max(dists)/sqrt(neurons*2))
returnsigmas
def_calculate_phi(self,x):
C=self.workers
neurons=self.neurons
mu=self.mu
sigmas=self.sigmas
phi=self.phi=None
n=self.n
defheavy_lifting(c,phi):
s=jobs[c][1]-jobs[c][0]
fork,iinenumerate(xrange(jobs[c][0],jobs[c][1])):
forjinxrange(neurons):
#phi[i,j]=metrics(x[i,:],mu[j])**3)
#phi[i,j]=plateSpine(x[i,:],mu[j]))
#phi[i,j]=invMultiQuadric(x[i,:],mu[j],sigmas[j]))
phi[i,j]=multiQuadric(x[i,:],mu[j],sigmas[j])
#phi[i,j]=gaussian(x[i,:],mu[j],sigmas[j]))
ifk%1000==0:
percent=true_divide(k,s)*100
print(c,':{:2.2f}%'.format(percent))
print(c,':Done')
#distributingtheworkbetween4workers
shared_array=Array(c_double,n*neurons)
phi=frombuffer(shared_array.get_obj())
phi=phi.reshape((n,neurons))
jobs=[]
workers=[]
p=n/C
m=n%C
forcinrange(C):
jobs.append((c*p,(c+1)*p+(mifc==C-1else0)))
worker=Process(target=heavy_lifting,args=(c,phi))
workers.append(worker)
worker.start()
forworkerinworkers:
worker.join()
returnphi
def_do_algebra(self,y):
phi=self.phi
w=lstsq(phi,y)[0]
os=dot(w,transpose(phi))
returnw,os
#SavingtoHDF5
os_h5=os_handle.create_dataset('os',data=os)
deftrain(self,x,y):
self.n=x.shape[0]
##InitializeHDF5caches
prefix=self.prefix
postfix=str(self.n)+'-'+str(self.extra_neurons)+'.hdf5'
name_template=prefix+'-{}-'+postfix
phi_handle=self.phi_handle=File(name_template.format('phi'),'w')
os_handle=self.w_handle=File(name_template.format('os'),'w')
w_handle=self.w_handle=File(name_template.format('w'),'w')
mu_handle=self.mu_handle=File(name_template.format('mu'),'w')
sigma_handle=self.sigma_handle=File(name_template.format('sigma'),'w')
##Mugeneration
mu=self.mu=self._generate_mu(x)
self.neurons=mu.shape[0]
print('({}neurons)'.format(self.neurons))
#SavetoHDF5
mu_h5=mu_handle.create_dataset('mu',data=mu)
##Sigmacalculation
print('CalculatingSigma...')
sigmas=self.sigmas=self._calculate_sigmas()
#SavetoHDF5
sigmas_h5=sigma_handle.create_dataset('sigmas',data=sigmas)
print('Done')
##Phicalculation
print('CalculatingPhi...')
phi=self.phi=self._calculate_phi(x)
print('Done')
#SavingtoHDF5
print('Serializing...')
phi_h5=phi_handle.create_dataset('phi',data=phi)
delphi
self.phi=phi_h5
print('Done')
##Algebra
print('Doingfinalalgebra...')
w,os=self.w,_=self._do_algebra(y)
#SavingtoHDF5
w_h5=w_handle.create_dataset('w',data=w)
os_h5=os_handle.create_dataset('os',data=os)
##Calculateerror
self._calculate_error(y)
print('Done')
defpredict(self,test_data):
mu=self.mu=self.mu.value
sigmas=self.sigmas=self.sigmas.value
w=self.w=self.w.value
print('Calculatingphifortestdata...')
phi=self._calculate_phi(test_data)
os=dot(w,transpose(phi))
savetxt('iok3834.txt',os,delimiter='\n')
returnos
@property
defsummary(self):
return'\n'.join(\
['-----------------',
'Trainingsetsize:{}'.format(self.n),
'Hiddenlayersize:{}'.format(self.neurons),
'-----------------',
'Absoluteerror:{:02.2f}'.format(self.error),
'Relativeerror:{:02.2f}%'.format(self.relative_error*100)])
defpredict(test_data):
mu=File('rbf-mu-212243-2400.hdf5','r')['mu'].value
sigmas=File('rbf-sigma-212243-2400.hdf5','r')['sigmas'].value
w=File('rbf-w-212243-2400.hdf5','r')['w'].value
n=test_data.shape[0]
neur=mu.shape[0]
mu=transpose(mu)
mu.reshape((n,neur))
phi=zeros((n,neur))
foriinrange(n):
forjinrange(neur):
phi[i,j]=multiQuadric(test_data[i,:],mu[j],sigmas[j])
os=dot(w,transpose(phi))
savetxt('iok3834.txt',os,delimiter='\n')
returnos
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