kennyballou · July 18, 2014 07:21 · Jul 18, 2014
diff --git a/hopfield.py b/hopfield.py
@@ -0,0 +1,89 @@
+#!/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])
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