Python实现的径向基（RBF）神经网络示例
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2020-09-20
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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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