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Python实现的径向基（实现的径向基（RBF）神经网络示例）神经网络示例

主要介绍了Python实现的径向基（RBF）神经网络,结合完整实例形式分析了Python径向基（RBF）神经网络定

义与实现技巧,需要的朋友可以参考下

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：

from numpy import array, append, vstack, transpose, reshape, \

dot, true_divide, mean, exp, sqrt, log, \

loadtxt, savetxt, zeros, frombuffer

from numpy.linalg import norm, lstsq

from multiprocessing import Process, Array

from random import sample

from time import time

from sys import stdout

from ctypes import c_double

from h5py import File

def metrics(a, b):

return norm(a - b)

def gaussian (x, mu, sigma):

return exp(- metrics(mu, x)**2 / (2 * sigma**2))

def multiQuadric (x, mu, sigma):

return pow(metrics(mu,x)**2 + sigma**2, 0.5)

def invMultiQuadric (x, mu, sigma):

return pow(metrics(mu,x)**2 + sigma**2, -0.5)

def plateSpine (x,mu):

r = metrics(mu,x)

return (r**2) * log(r)

class Rbf:

def __init__(self, prefix = 'rbf', workers = 4, extra_neurons = 0, from_files = None):

self.prefix = prefix

self.workers = workers

self.extra_neurons = extra_neurons

# Import partial model

if from_files is not None:

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: Make reusable

mu_clusters = loadtxt('clusters100.txt', delimiter=' ')

mu_indices = sample(range(n), extra_neurons)

mu_new = x[mu_indices, :]

mu = vstack((mu_clusters, mu_new))

return mu

def _calculate_sigmas(self):

neurons = self.neurons

mu = self.mu

sigmas = zeros((neurons, ))

for i in xrange(neurons):

dists = [0 for _ in xrange(neurons)]

for j in xrange(neurons):

if i != j:

dists[j] = metrics(mu[i], mu[j])

sigmas[i] = mean(dists)* 2

# max(dists) / sqrt(neurons * 2))

return sigmas

def _calculate_phi(self, x):

C = self.workers

neurons = self.neurons

mu = self.mu

sigmas = self.sigmas

phi = self.phi = None

n = self.n

def heavy_lifting(c, phi):

s = jobs[c][1] - jobs[c][0]

for k, i in enumerate(xrange(jobs[c][0], jobs[c][1])):

for j in xrange(neurons):

# phi[i, j] = metrics(x[i,:], mu[j])**3)

# phi[i, j] = plateSpine(x[i,:], mu[j]))

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