Added Perceptron Learning and Delta Learning Rule Algorithm

Neural Networks/src/delta_learning.py

@@ -0,0 +1,39 @@
+from math import exp
+
+
+def mul(l1, l2):
+    return round(sum(a*b for a,b in zip(l1, l2)), 3)
+
+def sgn(x):
+    return 1 if x > 0 else -1
+def imul(x, a):
+    return [round(a*xi,3) for xi in x]
+def add(l1, l2):
+    return [round(a+b,3) for a,b in zip(l1, l2)]
+def func(net):
+    # bipolar continuous
+    return 2 / (1 + exp(-net)) - 1
+def funcdash(o):
+    return (1 - o*o)/2
+
+if __name__ == '__main__':
+    c = 0.1
+    n = int(input('Enter no of input:'))
+    xn, dn = [], []
+    for i in range(n):
+        xi = list(map(float, input(f'Enter x{i}: ').strip().split(' ')))
+        di = int(input('Enter desired output:'))
+        xn.append(xi); dn.append(di)
+    w = list(map(float, input('Enter initial weights:').split(' ')))
+    for xi, di in zip(xn, dn):
+        # print(f'Input: {xi}')
+        net = mul(w, xi)
+        # print(f'Expected output: {di}, Actual output: {net}')
+
+        oi = round(func(net), 3)
+        fnetdash = round(funcdash(oi), 3)
+        print(f'oi = {oi}, fnetdash = {fnetdash}')
+
+        xi = imul(xi, c * (di - oi) * fnetdash)
+        w = add(w, xi)
+        print(f'Updated weight: {w}')

Neural Networks/src/perceptron_learning.py

@@ -0,0 +1,39 @@
+import math
+
+
+def sgn(x):
+    return 1 if x>0 else -1
+
+def add(a, b):
+    return [round(ai+bi, 2) for ai, bi in zip(a, b)]
+
+def var_mul(x, a):
+    return [round(x*ai, 2) for ai in a]
+
+def mult(a, b):
+    return sum([ai*bi for ai, bi in zip(a, b)])
+
+if __name__ == '__main__':
+    c = 0.1
+    n = int(input('Enter no of input:'))
+    x, d = [],[]
+    for i in range(n):
+        xi = list(map(float, input(f'Enter x{i}: ').strip().split(' ')))
+        di = int(input('Enter desired output:'))
+        x.append(xi); d.append(di)
+
+    w = list(map(float, input('Enter initial weights:').split(' ')))
+
+
+    for xi, di in zip(x, d):
+        net = mult(xi, w)
+        if(sgn(net) != sgn(di)):
+            print("Correction Needed...")
+            w = add(w, var_mul(c*(di - sgn(net)), xi))
+            print("X: ", xi)
+            print(f"W: {w}\n")
+        else:
+            print("Correction Not Needed..")
+            print("X: ", xi)
+            print(f"W: {w}\n")
+            continue