属性错误：“ParticleSwarmOptimization”对象没有属性“global_best_fitnes”

Question

用于特征选择的执行代码 PSO 出错

def fitness(position):
    selected_features = np.array(position, dtype=bool)
    X_selected = X.iloc[:, selected_features]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    
    classifier = KNeighborsClassifier()
    classifier.fit(X_train, y_train)
    y_pred = classifier.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    return accuracy

import numpy as np

class ParticleSwarmOptimization:
    def __init__(self, n_particles, n_features, n_iterations, fitness_function, w=0.5, c1=1, c2=2):
        self.n_particles = n_particles
        self.n_features = n_features
        self.n_iterations = n_iterations
        self.fitness_function = fitness_function
        self.w = w
        self.c1 = c1
        self.c2 = c2

    def initialize_particles(self):
        return np.random.choice([0, 1], size=(self.n_particles, self.n_features))

    def update_velocity(self, velocity, personal_best, global_best, position):
        cognitive = self.c1 * np.random.rand() * (personal_best - position)
        social = self.c2 * np.random.rand() * (global_best - position)
        return self.w * velocity + cognitive + social

    def update_position(self, position, velocity):
        return np.round(1 / (1 + np.exp(-velocity))).astype(int)

    def optimize(self):
        self.best_fitness_history = []  # Add this line to store fitness history

        # Initialize particles
        position = self.initialize_particles()
        velocity = np.zeros((self.n_particles, self.n_features))

        personal_best = position.copy()
        personal_best_fitness = np.array([self.fitness_function(pos) for pos in personal_best])

        self.global_best = personal_best[np.argmax(personal_best_fitness)]
        self.global_best_fitness = np.max(personal_best_fitness)

        for iteration in range(self.n_iterations):
            for i in range(self.n_particles):
                # Update velocity and position
                velocity[i] = self.update_velocity(velocity[i], personal_best[i], self.global_best, position[i])
                position[i] = self.update_position(position[i], velocity[i])

                # Update personal best
                current_fitness = self.fitness_function(position[i])
                if current_fitness > personal_best_fitness[i]:
                    personal_best[i] = position[i].copy()
                    personal_best_fitness[i] = current_fitness

                    # Update global best
                    if current_fitness > self.global_best_fitness:
                        self.global_best = position[i].copy()
                        self.global_best_fitness = current_fitness
                        
                        self.best_fitness_history.append(self.global_best_fitness)

        return self.global_best, self.global_best_fitnes

Answer 1

看起来

ParticleSwarmOptimization.optimize

的返回语句的最后一部分指的是

self.global_best_fitnes

，但在

optimize

的早期，您已将其定义为

self.global_best_fitness

（其中有两个

s

）。纠正拼写应该可以解决这个问题。