人工免疫算法聚类python代码

人工免疫算法聚类是一种基于免疫系统的聚类算法，可以通过抗体的密度和激励度来衡量结果的好坏，具有全局搜索的能力。下面是一个使用Python实现的人工免疫算法聚类的代码示例：

import numpy as np
from sklearn.datasets import make_blobs

# 生成随机数据
X, y = make_blobs(n_samples=200, centers=4, random_state=0)

# 计算欧氏距离
def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2) ** 2))

# 计算相似度矩阵
def calculate_similarity_matrix(X):
    n_samples = X.shape[0]
    similarity_matrix = np.zeros((n_samples, n_samples))
    for i in range(n_samples):
        for j in range(n_samples):
            similarity_matrix[i, j] = euclidean_distance(X[i], X[j])
    return similarity_matrix

# 计算浓度
def calculate_density(similarity_matrix, delta):
    density = np.zeros(similarity_matrix.shape[0])
    for i in range(similarity_matrix.shape[0]):
        density[i] = np.sum(similarity_matrix[i] < delta)
    return density / similarity_matrix.shape[0]

# 计算激励度
def calculate_stimulation(density, similarity_matrix):
    stimulation = np.zeros(density.shape[0])
    for i in range(density.shape[0]):
        stimulation[i] = np.sum(density * (similarity_matrix[i] < delta))
    return stimulation

# 初始化抗体个体
def initialize_antibodies(n_antibodies, n_features, delta):
    antibodies = np.random.uniform(low=np.min(X), high=np.max(X), size=(n_antibodies, n_features))
    similarity_matrix = calculate_similarity_matrix(antibodies)
    density = calculate_density(similarity_matrix, delta)
    stimulation = calculate_stimulation(density, similarity_matrix)
    return antibodies, density, stimulation

# 免疫算法聚类
def artificial_immune_clustering(X, n_clusters, n_iterations, n_antibodies, delta, mutation_rate):
    n_samples, n_features = X.shape
    antibodies, density, stimulation = initialize_antibodies(n_antibodies, n_features, delta)
    for iteration in range(n_iterations):
        # 免疫选择
        selected_indices = np.argsort(stimulation)[-n_clusters:]
        selected_antibodies = antibodies[selected_indices]
        # 免疫迁移
        migrated_antibodies = np.zeros_like(selected_antibodies)
        for i in range(n_clusters):
            migrated_antibodies[i] = selected_antibodies[i] + mutation_rate * np.random.randn(n_features)
        # 更新抗体
        antibodies = np.vstack((antibodies, migrated_antibodies))
        similarity_matrix = calculate_similarity_matrix(antibodies)
        density = calculate_density(similarity_matrix, delta)
        stimulation = calculate_stimulation(density, similarity_matrix)
    return antibodies[selected_indices]

# 调用人工免疫算法聚类
n_clusters = 4
n_iterations = 100
n_antibodies = 20
delta = 1.0
mutation_rate = 0.1
result = artificial_immune_clustering(X, n_clusters, n_iterations, n_antibodies, delta, mutation_rate)