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f44a265 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
import pandas as pd
def flatten(img : np.array) -> list[int] :
new : list[int] = []
for row in img:
for item in row:
new.append(int(item))
return new
class Hopfield:
def __init__(self,patts):
self.E : list[int] = []
self.patts = patts
self.size = (4,4)
self.Px :int = len(patts)
self.Py :int = len(patts[0])
self.W : np.array = np.zeros((self.Py,self.Py),dtype=np.float16)
def train(self):
for i in range(self.Py):
for j in range(self.Py):
if i == j:
self.W[i][j] = 0
continue
self.W[i][j] = (1 / self.Px) * sum([patt[i] * patt[j] for patt in self.patts])
def Energy(self):
return self.E
def update(self,pattern):
pattern_flat = flatten(pattern)
H : list[int] = []
for i in self.W:
H.append((sum([w * s for w,s in zip(i, pattern_flat)])))
H = np.array(H)
H = np.sign(H)
E = 0
for i in range(self.Py):
for j in range(self.Py):
E += float(-0.5 * self.W[i][j] * H[i] * H[j])
self.E.append(E)
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