added self organising maps algorithm in the machine learning section.

machine_learning/Self_Organising_Maps.py

@@ -0,0 +1,62 @@
+import math
+
+
+class SOM:
+    # Function here computes the winning vector
+    # by Euclidean distance
+    def winner(self, weights, sample):
+        D0 = 0
+        D1 = 0
+        for i in range(len(sample)):
+            D0 = D0 + math.pow((sample[i] - weights[0][i]), 2)
+            D1 = D1 + math.pow((sample[i] - weights[1][i]), 2)
+            if D0 > D1:
+                return 0
+            else:
+                return 1
+
+    # Function here updates the winning vector
+    def update(self, weights, sample, J, alpha):
+        for i in range(len(weights)):
+            weights[J][i] = weights[J][i] + alpha * (sample[i] - weights[J][i])
+        return weights
+
+
+# Driver code
+def main():
+    # Training Examples ( m, n )
+    T = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
+    m, n = len(T), len(T[0])
+
+    # weight initialization ( n, C )
+    weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
+
+    # training
+    ob = SOM()
+    epochs = 3
+    alpha = 0.5
+
+    for i in range(epochs):
+        for j in range(m):
+
+            # training sample
+            sample = T[j]
+
+            # Compute winner vector
+            J = ob.winner(weights, sample)
+
+            # Update winning vector
+            weights = ob.update(weights, sample, J, alpha)
+
+    # classify test sample
+    s = [0, 0, 0, 1]
+    J = ob.winner(weights, s)
+
+    # results
+    print("Clusters that the test sample belongs to : ", J)
+    print("Weights that have been trained : ", weights)
+
+
+# running the main() function
+if __name__ == "__main__":
+    main()