k最近邻算法实现

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from sklearn.metrics import pairwise_distances_argmin
sns.set()


def find_clusters(X, n_clusters, rseed=2):
    rng = np.random.RandomState(rseed)
    # 随机选择簇中心点
    print('rng.permutation(X.shape[0])[:n_clusters]:\n', rng.permutation(X.shape[0])[:n_clusters])
    i = rng.permutation(X.shape[0])[:n_clusters]  # 随机排序X.shape[0](300个点，取出前nclusters个)
    centers = X[i]  # 随机选出这四个点坐标
    print('centers:\n', centers)
    while True:  # 一直执行
        # 基于最近中心指定标签
        labels = pairwise_distances_argmin(X, centers)
        # print('labels= ', labels)  # 是一个表示分类的列表
        # 根据各个分类里的点平均值找到新的中心
        new_centers = np.array([X[labels == i].mean(0) for i in range(n_clusters)])  # .mean(0)跨行求平均
        # print('new_centers:\n', new_centers)
        if np.all(centers == new_centers):
            break
        centers = new_centers
    return centers, labels


X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
centers, labels = find_clusters(X, 4)
plt.scatter(X[:, 0], X[:, 1], c=labels,
            s=50, cmap='viridis')
plt.show()