def main():
text = t.poemToProse(t.getText('texts/wiki1.txt'))
words = t.textToWords(text)
# print(len(words))
uniq = frequencyVector(m.getNouns(words))
print(uniq[0], uniq[1])
matrix = matchMatrix(uniq[0], t.textToSenteces(text))
#print(np.linalg.det(matrix))
# print(np.linalg.svd(matrix)[2][8])
# print(matrix[2].sum())
#print(np.linalg.det(matrix_shit))
# print(np.linalg.det(matrix - np.identity(matrix.shape[0])))
# print(np.linalg.eigvals(matrix_shit))
hopfield = Hopfield(matrix, uniq[1])
hopfield.train()
# print(hopfield.result)
print(showResults(hopfield.result, uniq[0]))
dim = width * height
for _ in range(count):
f.readline() # skip empty line
x = np.empty((height, width))
#print('x shape ', x.shape)
for r in range(height):
x[r, :] = np.array(list(f.readline().strip())) == '#'
patterns.append(2 * x.flatten() -
1) # flatten to 1D vector, rescale {0,1} -> {-1,+1}
util_setup(width, height)
model = Hopfield(dim)
model.train(patterns)
def flip_positions(input, positions):
flipped = 0
new_noise = input.copy()
for pos in positions:
new_noise[pos] = -new_noise[pos]
flipped += 1
return new_noise
def discrete_noise_correction(input):
all_inputs = []
for noise_amount in [0, 7, 14, 21]:
positions = []
