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