/
hopfield_network.py
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/
hopfield_network.py
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from config import HNConfig as Config
import numpy as np # type: ignore
from matplotlib import pyplot as plt # type: ignore
from matplotlib import cm # type: ignore
from matplotlib import colors # type: ignore
import pandas as pd # type: ignore
import util
window_size = 5
dpi = 100
iter_lim = 1000
record_moment = np.arange(0, iter_lim, 10)
record = True
delta_t = 0.01
noise = 0.001
u_0 = 0.02
param_a = 1.0
param_b = 1.0
param_c = 2.0
param_d = 1.0
@np.vectorize
def sigmoid(input: float) -> float:
sigmoid_range = 34.538776394910684
if input <= -sigmoid_range:
return 1e-15
if input >= sigmoid_range:
return 1.0 - 1e-15
return 1.0 / (1.0 + np.exp(-input / u_0))
def kronecker_delta(i: int, j: int) -> float:
if i == j:
return 1.0
return 0.0
def calc_weight_matrix(city_array: np.array) -> np.array:
city_num: int = city_array.shape[0]
n: int = city_num ** 2
tmp: np.array = np.zeros((n, n))
for s0 in range(n):
x: int = int(s0 / city_num)
i: int = s0 % city_num
for s1 in range(n):
y: int = int(s1 / city_num)
j: int = s1 % city_num
dxy: float = util.dist(city_array[x, :], city_array[y, :])
tmp[s0, s1] = (
-param_a * kronecker_delta(x, y) * (1.0 - kronecker_delta(i, j))
- param_b * kronecker_delta(i, j) * (1.0 - kronecker_delta(x, y))
- param_c
- param_d
* dxy
* (
kronecker_delta(j, (i - 1) % city_num)
+ kronecker_delta(j, (i + 1) % city_num)
)
)
return tmp
def calc_bias(city_array: np.array) -> np.array:
city_num: int = city_array.shape[0]
n: int = city_num ** 2
tmp: np.array = param_c * city_num * np.ones(n)
return tmp
def update_inner_vals(
nodes_array: np.matrix,
inner_vals: np.matrix,
weight_matrix: np.matrix,
biases: np.matrix,
) -> np.matrix:
tau = 1.0
asdf: np.matrix = np.matmul(weight_matrix, nodes_array)
delta: np.matrix = (-inner_vals / tau + asdf + biases) * delta_t
return inner_vals + delta
def hp_begin(
inner_vals_array: np.matrix,
nodes_array: np.matrix,
weights_matrix: np.matrix,
biases_array: np.matrix,
) -> None:
if record:
dir_name: str = util.make_directory(Config)
for i in range(iter_lim):
if i in record_moment:
filename: str = "iteration-" + str(i) + ".png"
file_path: str = dir_name + filename
plt.savefig(file_path)
inner_vals_array = update_inner_vals(
nodes_array, inner_vals_array, weights_matrix, biases_array
)
nodes_array = sigmoid(inner_vals_array)
plt.title("iteration=" + str(i + 1))
mat_visual.set_data(np.reshape(nodes_array, (city_num, city_num)))
plt.pause(0.0001)
else:
i = 1
# while plt.get_fignums():
# inner_vals_array = update_inner_vals(nodes_array, inner_vals_array, weights_matrix, biases_array)
# nodes_array = sigmoid(inner_vals_array)
# plt.title("iteration=" + str(i))
# mat_visual.set_data(np.reshape(nodes_array, (city_num, city_num)))
# i += 1
# plt.pause(.01)
while plt.get_fignums():
# print(nodes_array.shape, inner_vals_array.shape, weights_matrix.shape, biases_array.shape)
inner_vals_array = update_inner_vals(
nodes_array, inner_vals_array, weights_matrix, biases_array
)
nodes_array = sigmoid(inner_vals_array)
plt.title("iteration=" + str(i))
mat_visual.set_data(np.reshape(nodes_array, (city_num, city_num)))
i += 1
plt.pause(0.0001)
if __name__ == "__main__":
if Config.read_file:
np_cities = np.genfromtxt(Config.file_path + Config.city_file, delimiter=",")
city_num = np_cities.shape[0]
# width_x = (np.max(np_cities[:, 0]) - np.min(np_cities[:, 0]))
# width_y = (np.max(np_cities[:, 1]) - np.min(np_cities[:, 1]))
# width = np.amax([width_x, width_y])
# np_cities[:, 0] -= np.min(np_cities[:, 0])
# np_cities[:, 0] /= width
# np_cities[:, 1] -= np.min(np_cities[:, 1])
# np_cities[:, 1] /= width
# center_x = np.average(np_cities[:, 0])
# center_y = np.average(np_cities[:, 1])
figsize = (window_size, window_size)
else:
city_num = Config.city_num
# “continuous uniform” distribution random
np_cities = np.random.random((city_num, 2))
center_x = 0.5
center_y = 0.5
figsize = (window_size, window_size)
inner_vals = np.matrix((np.random.random((city_num ** 2)) - 0.5) * noise).T
nodes = np.matrix(sigmoid(inner_vals))
weights = np.matrix(calc_weight_matrix(np_cities))
df = pd.DataFrame(weights)
df.to_csv("weigths.csv", header=False, index=False)
biases = np.matrix(calc_bias(np_cities)).T
fig = plt.figure(figsize=figsize, dpi=dpi)
mat_visual = plt.matshow(
np.reshape(nodes, (city_num, city_num)),
fignum=0,
cmap=cm.Greys,
norm=colors.Normalize(vmin=0.0, vmax=1.0),
)
fig.colorbar(mat_visual)
plt.title("iteration=" + str(0))
plt.pause(0.0001)
hp_begin(inner_vals, nodes, weights, biases)