Machine_Learning/hebbian learning .py at master · DanishSheikh1999/Machine_Learning · GitHub

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import numpy as np
import time

def threshold(x):
	if x >=0:
		return 1
	else:
		return -1


def print_func(loop_var, net, sig_net, w, delta_w):
		print("i: "+ str(loop_var))
		print("net: "+ str(net))
		print("sig_net: "+ str(sig_net))
		print("delta_w: "+ str(delta_w))
		print("w: "+ str(w))
		print("-------------------\n")


def compute():

	try:
		n = int(input("Enter number of input vectors: "))

		x = []
		r = 1 #Learning constant(c)

		for i in range(0,n):
			raw_str1 = str(input("Enter values for vector " + str(i+1) + ": "))
			input_vector = raw_str1.split(' ')
			#print(input_vector)
			ip_list = []
			for ele in input_vector:
				ip_list.append(float(ele))
			#print(ip_list)
			np_list = np.array(ip_list, dtype=np.float64)
			x.append(np_list)

		raw_str3 = str(input("Enter initial weight vector: "))
		w = raw_str3.split(' ')
		w_list = []
		for ele in w:
			w_list.append(float(ele))

		#np_wlist = np.array(w_list, dtype=np.float64)
		#print(np_wlist)
		#if len(np_wlist) != n:
		#	print("Init Weight Vector Error..")

		delta_w = 0
		for i in range(0,n):

			net = np.transpose(np.asarray(w_list)).dot(np.asarray(x[i]))
			#print(net)
			sig_net = threshold(net)
			#print(sig_net)

			delta_w = r * sig_net * x[i]
			#print(delta_w)
			w_list = np.add(np.asarray(w_list),delta_w)

			print_func(i, net, sig_net, w_list, delta_w)

	except Exception as e:
		print("Error.. "+(str(e)))


if __name__ == '__main__':
	compute()