-New perceptron python program

NN/Exercise3.py

@@ -0,0 +1,64 @@
+import math
+import numpy as np
+import random
+
+training_set = np.array([[1, 0, 1],
+ [0, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0]])
+
+def run():
+ f = open("weights.txt", "w")
+
+ w = np.array([random.random(), random.random(), random.random()])
+
+ learning_rate = 0.2
+
+
+ def calc_weights(weights, input, output, correct_output):
+ #return weights + learning_rate*output*(1- output)*(correct_output-output)*input # sigmoid
+ return weights + (0.5*learning_rate*(correct_output-output)*input)
+
+ epochs = 1000
+ error = math.inf
+ while error > 0.0001:
+ for k in range(epochs):
+ np.random.shuffle(training_set)
+ for i in range(4):
+ u = np.append(training_set[i][:2], -1)
+ z = (w[0]*u[0]) + (w[1]*u[1]) + (w[2]*u[2])
+ #v = 1 / (1+(math.e**(-z)))
+ if z <= 0:
+ v = 0
+ else:
+ v = 1
+ t = training_set[i][2]
+ #error = 0.5*((t-v)**2) # squared error
+ error = abs(t-v)
+ w = calc_weights(w, u, v, t)
+
+
+ f.write(np.array_str(w))
+ f.close()
+
+def testweights(linenum):
+ f = open("weights.txt", "r")
+ lines = f.readlines()
+ line = lines[linenum]
+ line = line.replace("[", "")
+ line = line.replace("]", "")
+ w = np.fromstring(line, dtype=float, sep=" ")
+ np.random.shuffle(training_set)
+ for i in range(4):
+ u = np.append(training_set[i][:2], -1)
+ z = (w[0] * u[0]) + (w[1] * u[1]) + (w[2] * u[2])
+ #v = 1 / (1 + (math.e ** (-z)))
+ if z <= 0:
+ v = 0
+ else:
+ v = 1
+ print("Input:", u, " Output:", v)
+
+
+testweights(0)
+#run()