みのほど - 経済統計練習帳

import numpy
import math
import random
from matplotlib import pyplot
 
class Neural:
 
    # constructor
    def __init__(self, n_input, n_hidden, n_output):
        self.hidden_weight = numpy.random.random_sample((n_hidden, n_input + 1))
        self.output_weight = numpy.random.random_sample((n_output, n_hidden + 1))
        self.hidden_momentum = numpy.zeros((n_hidden, n_input + 1))
        self.output_momentum = numpy.zeros((n_output, n_hidden + 1))
 
 
# public method
    def train(self, X, T, epsilon, mu, epoch):
        self.error = numpy.zeros(epoch)
        N = X.shape[0]
        for epo in range(epoch):
            for i in range(N):
                x = X[i, :]
                t = T[i, :]
 
                self.__update_weight(x, t, epsilon, mu)
 
            self.error[epo] = self.__calc_error(X, T)
 
 
    def predict(self, X):
        N = X.shape[0]
        C = numpy.zeros(N).astype('int')
        Y = numpy.zeros((N, X.shape[1]))
        for i in range(N):
            x = X[i, :]
            z, y = self.__forward(x)
 
            Y[i] = y
            C[i] = y.argmax()
 
        return (C, Y)
 
 
    def error_graph(self):
        pyplot.ylim(0.0, 2.0)
        pyplot.plot(numpy.arange(0, self.error.shape[0]), self.error)
        pyplot.show()
 
 
# private method
    def __sigmoid(self, arr):
        return numpy.vectorize(lambda x: 1.0 / (1.0 + math.exp(-x)))(arr)
 
 
    def __forward(self, x):
        # z: output in hidden layer, y: output in output layer
        z = self.__sigmoid(self.hidden_weight.dot(numpy.r_[numpy.array([1]), x]))
        y = self.__sigmoid(self.output_weight.dot(numpy.r_[numpy.array([1]), z]))
 
        return (z, y)
 
    def __update_weight(self, x, t, epsilon, mu):
        z, y = self.__forward(x)
 
        # update output_weight
        output_delta = (y - t) * y * (1.0 - y)
        _output_weight = self.output_weight
        self.output_weight -= epsilon * output_delta.reshape((-1, 1)) * numpy.r_[numpy.array([1]), z] - mu * self.output_momentum
        self.output_momentum = self.output_weight - _output_weight
 
        # update hidden_weight
        hidden_delta = (self.output_weight[:, 1:].T.dot(output_delta)) * z * (1.0 - z)
        _hidden_weight = self.hidden_weight
        self.hidden_weight -= epsilon * hidden_delta.reshape((-1, 1)) * numpy.r_[numpy.array([1]), x]
        self.hidden_momentum = self.hidden_weight - _hidden_weight
 
 
    def __calc_error(self, X, T):
        N = X.shape[0]
        err = 0.0
        for i in range(N):
            x = X[i, :]
            t = T[i, :]
 
            z, y = self.__forward(x)
            err += (y - t).dot((y - t).reshape((-1, 1))) / 2.0
 
        return err