ホップフィールドネットワークとやらを試してみた。hogeがhmgeになった。かなしい。

neural.py

import numpy


def str2matrix(string):
	return numpy.matrix(tuple(int(x)*2-1 for x in ''.join('{0:08b}'.format(ord(x)) for x in string))).T

def matrix2box(matrix):
	return matrix * matrix.T - numpy.identity(matrix.shape[0])

def make_neural(*strings):
	result = matrix2box(str2matrix(strings[0]))
	for x in strings[1:]:
		result += matrix2box(str2matrix(x))
	return result

def remember(neural, key_matrix):
	return numpy.matrix(tuple(int(x>=0)*2-1 for x in neural * key_matrix)).T

def matrix2str(matrix):
	ls = tuple(int(int(x)>=0) for x in matrix)
	result = []
	for i in range(0, len(ls), 8):
		result.append(chr(int(''.join(str(x) for x in ls[i:i+8]), 2)))
	return ''.join(result)


if __name__ == '__main__':
	neural_network = make_neural('hoge', 'huga', 'piyo')
	print'poyo -> ', matrix2str(remember(neural_network, str2matrix('poyo')))  # piyo
	print'hoge -> ', matrix2str(remember(neural_network, str2matrix('hoge')))  # hmge

neural_class.py

import numpy


class BaseBrain:
	def __init__(self, size):
		self.neural = numpy.zeros((size, size), numpy.int8)
		self.count = 0

	def data2matrix(self, data):
		assert isinstance(data, bytes)
		return (numpy.matrix(numpy.unpackbits(numpy.array(tuple(data), numpy.uint8)), dtype=numpy.int8) * 2 - 1).T

	def learn(self, data):
		matrix = self.data2matrix(data)
		self.neural += matrix * matrix.T
		self.count += 1

	def remember(self, key):
		matrix = self.data2matrix(key)
		result = (self.neural - numpy.identity(self.neural.shape[0])*self.count) * matrix
		return numpy.packbits(result >= 0).tobytes()


if __name__ == '__main__':
	brain = BaseBrain(4 * 8)
	brain.learn(b'hoge')
	brain.learn(b'piyo')
	print(brain.remember(b'poyo'))