Python-based radial basis function (RBF) neural network example, pythonrbf

Python-based radial basis function (RBF) neural network example, pythonrbf

This article describes the radial basis function (RBF) neural network implemented by Python. We will share this with you for your reference. The details are as follows:

from numpy import array, append, vstack, transpose, reshape, \         dot, true_divide, mean, exp, sqrt, log, \         loadtxt, savetxt, zeros, frombufferfrom numpy.linalg import norm, lstsqfrom multiprocessing import Process, Arrayfrom random import samplefrom time import timefrom sys import stdoutfrom ctypes import c_doublefrom h5py import Filedef 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