在Python中使用K均值聚类

Question

我有代码，但是我试图找到一种绘制结果的方法

import numpy as np
import random


def cluster_points(X, mu):
    clusters = {}
    for x in X:
        bestmukey = min([(i[0], np.linalg.norm(x - mu[i[0]])) \
                         for i in enumerate(mu)], key=lambda t: t[1])[0]
        try:
            clusters[bestmukey].append(x)
        except KeyError:
            clusters[bestmukey] = [x]
    return clusters


def reevaluate_centers(mu, clusters):
    newmu = []
    keys = sorted(clusters.keys())
    for k in keys:
        newmu.append(np.mean(clusters[k], axis=0))
    return newmu


#def has_converged(mu, oldmu):


def find_centers(x, k):
    # Initialize to K random centers
    oldmu = random.sample(x, k)
    mu = random.sample(x, k)
    while not (set([tuple(a) for a in mu]) == set([tuple(a) for a in oldmu])):
        oldmu = mu
        # Assign all points in X to clusters
        clusters = cluster_points(x, mu)
        # Reevaluate centers
        mu = reevaluate_centers(oldmu, clusters)
    return (mu, clusters)

def init_board(N):
    X = np.array([(random.uniform(-1, 1), random.uniform(-1, 1)) for i in range(N)])
    return X

def init_board_gauss(N, k):
    n = float(N)/k
    X = []
    for i in range(k):
        c = (random.uniform(-1, 1), random.uniform(-1, 1))
        s = random.uniform(0.05,0.5)
        x = []
        while len(x) < n:
            a, b = np.array([np.random.normal(c[0], s), np.random.normal(c[1], s)])
            # Continue drawing points from the distribution in the range [-1,1]
            if abs(a) < 1 and abs(b) < 1:
                x.append([a,b])
        X.extend(x)
    X = np.array(X)[:N]
    return X