Hopfield net small implementation

hopfield

#!/usr/bin/python

import numpy as np
np.set_printoptions(precision=3, suppress = True)

def mprint(n,v):
	print "%s = \n\t" %n , str(v).replace("\n", "\n\t")
	print 
	
	
SIZE = 7

np.random.seed(4213213)
T = np.random.randn(SIZE,SIZE)
U = np.random.randint(0,2,SIZE)


for i in range(SIZE):
	for j in range(i):
		T[i,j] = T[j,i]
	T[i,i] = 0

print "symmtric:" , (T == T.T).all()
	
mprint("T", T)
mprint("U", U)



def combine_pattern(A):
	m,n = A.shape
	T = np.zeros((n,n))
	
	for i in range(n):
		for j in range(i):
			x = 0
			for p in range(m):
				x += (2*A[p,i]-1)*(2*A[p,j]-1)				
			T[j,i] = x
			T[i,j] = x		
		T[i,i] = 0
	return T
	
def energy(T, U):
	s = 0
	for i in range(SIZE):
		for j in range(SIZE):
			if i!=j:
				s += U[i]*U[j]*T[j,i]		
				
	return -0.5* s

def step(T, U):
	Unext = U.copy()
	
	for i in range(SIZE):
		W = T[:,i]		
		x = sum(W * U)
		u = 1 if x >= 0 else 0		
		Unext[i] = u
		
	return Unext

for m in range(0):#2**SIZE):
	mprint("E", energy(T,U))
	Un = step(T,U)
	b = (Un == U).all()
	if b: break	
	U = Un


#mprint("b",b)
#mprint("m",m)
#mprint("U", U)

	
A = readImages(["1.jpg", "2.jpg", "3.jpg"], 32*32)
T = combine_pattern(A)

a1 = A[1,:].copy()
a1[:250] = 0
a1[600:] = 0

for m in range(10):
	mprint("E", energy(T,a1))
	a1n = step(T,a1)
	b = (a1n == a1).all()
	if b: break
	a1 = a1n
	
I = a1.reshape((32,32))
cv2.imshow("abc" , I*255)
cv2.waitKey()