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LNS22.py
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LNS22.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
import random
digits = load_digits()
X, y = digits.data, digits.target
X /= X.max()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
class AttractorNetwork:
def __init__(self, num_cells, num_classes):
self.num_cells = num_cells
self.num_classes = num_classes
self.weights = np.random.rand(num_cells, num_classes)
self.adjacency_matrix = self.create_adjacency_matrix()
self.best_loss = np.inf
self.epochs_completed = 0
def create_adjacency_matrix(self):
matrix = np.zeros((self.num_cells, self.num_cells))
for i in range(int(np.sqrt(self.num_cells))):
for j in range(int(np.sqrt(self.num_cells))):
index = i * int(np.sqrt(self.num_cells)) + j
if i > 0:
matrix[index, index - int(np.sqrt(self.num_cells))] = 1
if i < np.sqrt(self.num_cells) - 1:
matrix[index, index + int(np.sqrt(self.num_cells))] = 1
if j > 0:
matrix[index, index - 1] = 1
if j < np.sqrt(self.num_cells) - 1:
matrix[index, index + 1] = 1
return matrix
def load_weights(self, file_path):
self.weights = np.load(file_path)
def softmax(self, x):
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum(axis=1, keepdims=True)
def update_cell_states(self, x):
return np.dot(self.adjacency_matrix, x) / 4
def train(self, X_train, y_train, learning_rate, epochs, stop_loss=0.5):
while self.epochs_completed < epochs:
best_loss = np.inf
epochs_without_improvement = 0
for epoch in range(epochs):
total_loss = 0
for i in range(len(X_train)):
x = self.update_cell_states(X_train[i])
label = y_train[i]
label_one_hot = np.zeros(self.num_classes)
label_one_hot[label] = 1
cell_output = np.dot(x, self.weights)
cell_output_softmax = self.softmax(cell_output.reshape(1, -1))
loss = -np.sum(label_one_hot * np.log(cell_output_softmax + 1e-7))
total_loss += loss
delta_weights = learning_rate * np.outer(x, (cell_output_softmax - label_one_hot).flatten())
self.weights -= delta_weights
avg_loss = total_loss / len(X_train)
print(f"Epoch {epoch + 1}/{epochs}, Loss: {avg_loss:.4f}")
self.epochs_completed += 1
if avg_loss <= stop_loss:
print(f"Training stopped because the loss reached the specified threshold: {stop_loss}")
np.save("model_weights.npy", self.weights)
return True
return False
def predict(self, X_test):
predictions = []
for x in X_test:
x = self.update_cell_states(x)
cell_output = np.dot(x, self.weights)
cell_output_softmax = self.softmax(cell_output.reshape(1, -1))
predicted_class = np.argmax(cell_output_softmax)
predictions.append(predicted_class)
return predictions
# 定义遗传算法中的辅助函数
def fitness_function(individual, nn, X_train, y_train, learning_rate, epochs):
nn.train(X_train, y_train, learning_rate, epochs)
y_pred = nn.predict(X_train)
return accuracy_score(y_train, y_pred)
def genetic_algorithm(nn, X_train, y_train, learning_rate, epochs, population_size, num_generations, stop_loss=0.05):
max_fitness_per_generation = []
avg_fitness_per_generation = []
num_features = nn.num_cells * nn.num_classes
crossover_rate = 0.7
mutation_rate = 0.1
population = [np.random.choice([0, 1], size=(num_features,)) for _ in range(population_size)]
for generation in range(num_generations):
print(f"Generation {generation + 1}/{num_generations} in progress...")
fitness_scores = [fitness_function(individual, nn, X_train, y_train, learning_rate, epochs) for individual in population]
max_fitness = max(fitness_scores)
avg_fitness = sum(fitness_scores) / len(fitness_scores)
max_fitness_per_generation.append(max_fitness)
avg_fitness_per_generation.append(avg_fitness)
selected_individuals = random.choices(population, weights=fitness_scores, k=population_size)
next_population = []
for i in range(0, population_size, 2):
parent1, parent2 = selected_individuals[i], selected_individuals[i+1]
if random.random() < crossover_rate:
crossover_point = random.randint(1, num_features - 1)
offspring1 = np.concatenate([parent1[:crossover_point], parent2[crossover_point:]])
offspring2 = np.concatenate([parent2[:crossover_point], parent1[crossover_point:]])
else:
offspring1, offspring2 = parent1, parent2
next_population.extend([offspring1, offspring2])
for individual in next_population:
if random.random() < mutation_rate:
mutation_point = random.randint(0, num_features - 1)
individual[mutation_point] = 1 - individual[mutation_point]
population = next_population
if nn.train(X_train, y_train, learning_rate, epochs, stop_loss):
print(f"Training stopped early at generation {generation + 1} due to loss threshold.")
break
print("Max Fitness per Generation:", max_fitness_per_generation)
print("Average Fitness per Generation:", avg_fitness_per_generation)
plt.plot(max_fitness_per_generation, label='Max Fitness')
plt.plot(avg_fitness_per_generation, label='Average Fitness')
plt.legend()
plt.show()
best_individual = max(population, key=lambda ind: fitness_function(ind, nn, X_train, y_train, learning_rate, epochs))
return best_individual
if __name__ == "__main__":
num_cells = 64
num_classes = 10
learning_rate = 0.01
epochs = 100
nn = NeuralNetwork(num_cells, num_classes)
best_rule = genetic_algorithm(nn, X_train, y_train, learning_rate, epochs, population_size=20, num_generations=50, stop_loss=0.05)
nn.load_weights("model_weights.npy")
y_pred = nn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.4f}")
conf_matrix = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(10, 7))
sns.heatmap(conf_matrix, annot=True, fmt='g')
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()