当然，我可以帮您提供使用麻雀搜索算法优化k-means聚类算法的代码。下面是一个示例的完整代码： ```python import numpy as np class KMeans: def __init__(self, n_clusters, max_iter=100): self.n_clusters=n_clusters self.max_iter=max_iter def fit(self, X): self.centroids=X[np.random.choice(range(len(X)), self.n_clusters, replace=False)] for _ in range(self.max_iter): clusters=self.assign_clusters(X) new_centroids=self.update_centroids(X, clusters) if np.allclose(self.centroids, new_centroids): break self.centroids=new_centroids def assign_clusters(self, X): clusters=[] for x in X: distances=np.linalg.norm(x - self.centroids, axis=1) cluster=np.argmin(distances) clusters.append(cluster) return np.array(clusters) def update_centroids(self, X, clusters): new_centroids=[] for i in range(self.n_clusters): cluster_points=X[clusters==i] centroid=np.mean(cluster_points, axis=0) new_centroids.append(centroid) return np.array(new_centroids) class SparrowSearch: def __init__(self, n_clusters, n_sparrows, max_iter=100, w=0.9, c1=2.0, c2=2.0): self.n_clusters=n_clusters self.n_sparrows=n_sparrows self.max_iter=max_iter self.w=w self.c1=c1 self.c2=c2 def fit(self, X): self.sparrows=X[np.random.choice(range(len(X)), self.n_sparrows, replace=False)] self.best_centroids=None self.best_cost=np.inf for _ in range(self.max_iter): self.update_centroids(X) self.update_positions() cost=self.calculate_cost(X) if cost < self.best_cost: self.best_centroids=self.centroids self.best_cost=cost def update_centroids(self, X): kmeans=KMeans(self.n_clusters) kmeans.fit(self.sparrows) self.centroids=kmeans.centroids def update_positions(self): velocities=np.zeros_like(self.sparrows) for i in range(self.n_sparrows): r1=np.random.random(self.sparrows.shape[1]) r2=np.random.random(self.sparrows.shape[1]) velocities[i]=(self.w * velocities[i] + self.c1 * r1 * (self.centroids[i] - self.sparrows[i]) + self.c2 * r2 * (self.best_centroids[i] - self.sparrows[i])) self.sparrows[i] +=velocities[i] def calculate_cost(self, X): distances=np.linalg.norm(X[:, None] - self.centroids, axis=2) cluster_assignments=np.argmin(distances, axis=1) cost=0 for i in range(self.n_clusters): cluster_points=X[cluster_assignments==i] cluster_distances=np.linalg.norm(cluster_points - self.centroids[i], axis=1) cost +=np.sum(cluster_distances) return cost # 示例使用鸢尾花数据集 from sklearn.datasets import load_iris data=load_iris().data kmeans=KMeans(3) kmeans.fit(data) sparrow_search=SparrowSearch(3, 10) sparrow_search.fit(data) print("K-means centroids:") print(kmeans.centroids) print("SparrowSearch centroids:") print(sparrow_search.best_centroids) ``` 这个代码示例中包含了两个类：`KMeans`和`SparrowSearch`。`KMeans`类实现了普通的k-means聚类算法，而`SparrowSearch`类使用麻雀搜索算法对k-means进行优化。 注意：这只是一个简单的示例代码，实际使用时可能需要根据具体问题进行适当的调整和改进。