(bad) Pythonic translation of a Java hopfield neural network

hopfield.py

#!/usr/bin/env python

#   Hopfield Neural Network Simulation
#   Copyright (C) 2014  Kenny Ballou

#   This program is free software: you can redistribute it and/or modify
#   it under the terms of the GNU General Public License as published by
#   the Free Software Foundation, either version 3 of the License, or
#   (at your option) any later version.

#   This program is distributed in the hope that it will be useful,
#   but WITHOUT ANY WARRANTY; without even the implied warranty of
#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#   GNU General Public License for more details.

#   You should have received a copy of the GNU General Public License
#   along with this program.  If not, see <http://www.gnu.org/licenses/>.

import math

NETWORK_SIZE  = 4

def createEmptyList():
    return list(list(0 for i in range(4)) for i in range(4))

def convertToBipolar(bi):
    for i in bi:
        yield -1 if i == 0 else 1

class Neuron(object):
    def __init__(self, w):
        self.activation = -1
        self.w = w
    def act(self, pattern):
        a = 0
        for i in range(4):
            if pattern[i]:
                a += self.w[i]
        return a

class Layer(object):
    def __init__(self, wt):
        self.neurons = []
        self.wt = wt
        for w in self.wt:
            self.neurons.append(Neuron(w))
        self.output = list(False for x in range(4))

    def activation(self, pattern):
        for i in range(4):
            self.neurons[i].activation = self.neurons[i].act(pattern)
            self.output[i] = self.threshold(self.neurons[i].activation)

    def threshold(self, k):
        return math.tanh(k) >= 0

class Hopfield(object):
    def __init__(self):
        self.__initValues()

    def __initValues(self):
        self.wt = createEmptyList()

    def run(self, pattern):
        net = Layer(self.wt)
        net.activation(pattern)
        print(net.output)

    def clear(self):
        self.__initValues()

    def train(self, bi):
        work = createEmptyList()
        self.bi = list(convertToBipolar(bi))
        for i in range(4):
            for j in range(4):
                work[i][j] = self.bi[i] * self.bi[j]
        for i in range(4):
            work[i][i] -= 1
        for i in range(4):
            for j in range(4):
                self.wt[i][j] += work[i][j]

if __name__ == "__main__":
    hfNet = Hopfield()
    hfNet.train([0, 1, 0, 1])
    print(hfNet.wt)
    hfNet.run([False, True, False, True])
    hfNet.run([True, False, True, False])