kmeans聚类算法核心代码python

以下是KMeans聚类算法的核心代码示例，使用Python的scikit-learn库实现：

from sklearn.cluster import KMeans

# 创建KMeans对象，设置聚类数为3
kmeans = KMeans(n_clusters=3)

# 训练模型并进行聚类
X = [[1, 2], [1, 4], [1, 0],
     [4, 2], [4, 4], [4, 0]]
kmeans.fit(X)

# 输出聚类结果
print(kmeans.labels_)

上述代码中，首先导入了scikit-learn库中的KMeans类。然后创建了一个KMeans对象，将聚类数设置为3。接着，使用fit()方法对数据进行训练，并使用labels_属性获取聚类结果。

相关问题

kmeans聚类算法python代码

当然可以回答，以下是Kmeans聚类算法的Python代码示例：

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# 生成数据集
X, y = make_blobs(n_samples=1000, centers=4, random_state=0)

# KMeans 聚类
kmeans = KMeans(n_clusters=4, random_state=0).fit(X)

# 输出聚类结果
print(kmeans.labels_)

这段代码演示了如何使用`sklearn`库中的`KMeans`类进行聚类。在这个例子中，我们生成了一个包含 1000 个样本和 4 个簇的数据集，并使用 KMeans 算法将数据分为 4 个簇。

pso优化kmeans聚类算法python代码

PSO优化KMeans聚类算法是一种基于粒子群优化算法改进的聚类算法，它通过优化KMeans聚类算法中的初始质心来提高聚类效果。以下是Python实现代码：

import numpy as np
from sklearn.cluster import KMeans

class PSO_KMeans():
    def __init__(self, n_clusters, n_particles, n_iterations):
        self.n_clusters = n_clusters
        self.n_particles = n_particles
        self.n_iterations = n_iterations

    def fit(self, X):
        self.swarm_pos = np.random.uniform(low=X.min(axis=0), high=X.max(axis=0), size=(self.n_particles, X.shape, self.n_clusters))
        self.swarm_vel = np.zeros_like(self.swarm_pos)

        for i in range(self.n_iterations):
            # 计算每个粒子的适应度值
            fitness = np.zeros((self.n_particles,))
            for j in range(self.n_particles):
                kmeans = KMeans(n_clusters=self.n_clusters, init=self.swarm_pos[j])
                kmeans.fit(X)
                fitness[j] = -kmeans.score(X)

            # 更新全局最优解和局部最优解
            if i == 0:
                self.global_best_pos = self.swarm_pos[np.argmin(fitness)]
                self.global_best_fitness = np.min(fitness)
                self.local_best_pos = self.swarm_pos.copy()
                self.local_best_fitness = fitness.copy()
            else:
                mask = fitness < self.local_best_fitness
                self.local_best_pos[mask] = self.swarm_pos[mask]
                self.local_best_fitness[mask] = fitness[mask]
                if np.min(fitness) < self.global_best_fitness:
                    self.global_best_pos = self.swarm_pos[np.argmin(fitness)]
                    self.global_best_fitness = np.min(fitness)

            # 更新粒子速度和位置
            r1, r2 = np.random.rand(self.n_particles, X.shape, self.n_clusters), np.random.rand(self.n_particles, X.shape, self.n_clusters)
            self.swarm_vel = 0.5 * self.swarm_vel + 1 * r1 * (self.local_best_pos - self.swarm_pos) + 1 * r2 * (self.global_best_pos - self.swarm_pos)
            self.swarm_pos += self.swarm_vel

        # 训练完毕后，使用全局最优解进行聚类
        kmeans = KMeans(n_clusters=self.n_clusters, init=self.global_best_pos)
        kmeans.fit(X)
        self.labels_ = kmeans.labels_
        self.cluster_centers_ = kmeans.cluster_centers_

pso_kmeans = PSO_KMeans(n_clusters=3, n_particles=50, n_iterations=100)
pso_kmeans.fit(X)
labels = pso_kmeans.labels_
cluster_centers = pso_kmeans.cluster_centers_