Python径向基（RBF）神经网络 Python实现的径向基（RBF）神经网络示例

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