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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='\t') 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])) # 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])) if k % 1000 == 0: percent = true_divide(k, s)*100 print(c, ': {:2.2f}%'.format(percent)) print(c, ': Done') # distributing the work between 4 workers 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 for c in range(C): jobs.append((c*p, (c+1)*p + (m if c == C-1 else 0))) worker = Process(target = heavy_lifting, args = (c, phi)) workers.append(worker) worker.start() for worker in workers: worker.join() return phi def _do_algebra(self, y): phi = self.phi w = lstsq(phi, y)[0] os = dot(w, transpose(phi)) return w, os # Saving to HDF5 os_h5 = os_handle.create_dataset('os', data = os) def train(self, x, y): self.n = x.shape[0] ## Initialize HDF5 caches 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') ## Mu generation mu = self.mu = self._generate_mu(x) self.neurons = mu.shape[0] print('({} neurons)'.format(self.neurons)) # Save to HDF5 mu_h5 = mu_handle.create_dataset('mu', data = mu) ## Sigma calculation print('Calculating Sigma...') sigmas = self.sigmas = self._calculate_sigmas() # Save to HDF5 sigmas_h5 = sigma_handle.create_dataset('sigmas', data = sigmas) print('Done') ## Phi calculation print('Calculating Phi...') phi = self.phi = self._calculate_phi(x) print('Done') # Saving to HDF5 print('Serializing...') phi_h5 = phi_handle.create_dataset('phi', data = phi) del phi self.phi = phi_h5 print('Done') ## Algebra print('Doing final algebra...') w, os = self.w, _ = self._do_algebra(y) # Saving to HDF5 w_h5 = w_handle.create_dataset('w', data = w) os_h5 = os_handle.create_dataset('os', data = os) ## Calculate error self._calculate_error(y) print('Done') def predict(self, test_data): mu = self.mu = self.mu.value sigmas = self.sigmas = self.sigmas.value w = self.w = self.w.value print('Calculating phi for test data...') phi = self._calculate_phi(test_data) os = dot(w, transpose(phi)) savetxt('iok3834.txt', os, delimiter='\n') return os @property def summary(self): return '\n'.join( ['-----------------', 'Training set size: {}'.format(self.n), 'Hidden layer size: {}'.format(self.neurons), '-----------------', 'Absolute error : {:02.2f}'.format(self.error), 'Relative error : {:02.2f}%'.format(self.relative_error * 100)]) def predict(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)) for i in range(n): for j in range(neur): phi[i, j] = multiQuadric(test_data[i,:], mu[j], sigmas[j]) os = dot(w, transpose(phi)) savetxt('iok3834.txt', os, delimiter='\n') return os
更多关于Python相关内容感兴趣的读者可查看本站专题:《Python数据结构与算法教程》、《Python编码操作技巧总结》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》及《Python入门与进阶经典教程》
希望本文所述对大家Python程序设计有所帮助。
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稳了!魔兽国服回归的3条重磅消息!官宣时间再确认!
昨天有一位朋友在大神群里分享,自己亚服账号被封号之后居然弹出了国服的封号信息对话框。
这里面让他访问的是一个国服的战网网址,com.cn和后面的zh都非常明白地表明这就是国服战网。
而他在复制这个网址并且进行登录之后,确实是网易的网址,也就是我们熟悉的停服之后国服发布的暴雪游戏产品运营到期开放退款的说明。这是一件比较奇怪的事情,因为以前都没有出现这样的情况,现在突然提示跳转到国服战网的网址,是不是说明了简体中文客户端已经开始进行更新了呢?
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