Question

import math, random, os, operator, matplotlib, matplotlib.pyplot

from string import split


def EuDist(vecA, vecB):
    return math.sqrt(sum(map(lambda x: x * x, [i - j for i, j in zip(vecA, vecB)])))

filename = "points.txt"
FILE = open(filename, "w")
for i in range(33):
    line = str(random.uniform(1, 2) + random.uniform(-1, 1)) + "\t" + str(random.uniform(4, 5) + random.uniform(-1, 1)) + "\n"
    FILE.write(line)
for i in range(33):
line = str(random.uniform(4, 6) + random.uniform(-1, 1)) + "\t" + str(random.uniform(4, 6) + random.uniform(-1, 1)) + "\n"
    FILE.write(line)
for i in range(34):
    line = str(random.uniform(2, 3) + random.uniform(-1, 1)) + "\t" + str(random.uniform(2, 3) + random.uniform(-1, 1)) + "\n"
    FILE.write(line)
FILE.close()

dataFile = open("points.txt")
dataset = []
for line in dataFile:
    lineSplit = split(line[: -2], "\t")
    dataset.append([float(value) for value in lineSplit])

maxIters = input("Enter the maximum number of iterations: ")
center = input("Enter a number of clusters: ")

centoids = random.sample(dataset, center)
m = len(dataset)
cluster = [[] for i in range(len(centoids))]
for i in range(maxIters):
    cluster = [[] for v in range(len(centoids))]
    for j in range(m):
    minK = 0
    minDis = 100
    for k in range(len(centoids)):
        if operator.le(EuDist(dataset[j], centoids[k]), minDis):
            minDis = EuDist(dataset[j], centoids[k])
            minK = k
    cluster[minK].append(j)
for t in range(len(centoids)):
    x0 = sum([dataset[x][0] for x in cluster[t]])
    y0 = sum([dataset[x][1] for x in cluster[t]])
    centoids[k] = [x0 / len(cluster[t]), y0 / len(cluster[t])]

matplotlib.pyplot.plot(hold = False)
colorarr=["b", "r", "y", "g", "p"]
for k in range(len(cluster)):
    clusterPoint = [dataset[x] for x in cluster[k]]
    x0 = [x[0] for x in clusterPoint]
    y0 = [x[1] for x in clusterPoint]
    center = [(x0, y0) for x in clusterPoint]
    matplotlib.pyplot.show(centoids)
    matplotlib.pyplot.hold(True)
    matplotlib.pyplot.scatter(x0, y0, center, c = colorarr[k])
picname = "picture_number_" + str(i + 1) + ".png"
matplotlib.pyplot.savefig(picname)

代码工作正常，但我有问题。我不知道如何在此图上显示簇的质心。我知道我需要使用变量 centoids ，但我不确切知道如何。请给我一个提示。

Answer 1

我并不是100％确定你想要什么，但我认为你只是想在这些集群的组合散点图上过度绘制集群的质心，所有这些都在一个图中（每个集群都有它自己的颜色。）

这些方面的东西可以起作用：

from matplotlib import pyplot as plt
import numpy as np

data = {
    'x': np.random.rand(4, 100),
    'y': np.random.rand(4, 100),
}
centoids = {
    'x': np.random.rand(4),
    'y': np.random.rand(4),
}
colorarr = ["b", "r", "y", "g"]

for i, cluster in enumerate(zip(data['x'], data['y'])):
    plt.scatter(cluster[0], cluster[1], s=50, c=colorarr[i])
plt.grid(True)
plt.scatter(centoids['x'], centoids['y'], marker='+', color=colorarr, s=330)
plt.savefig("random.png")

只需使用此处显示的几条plt.行;您不需要更多，当然也不需要hold变量或show。基本上，您只是在上一个群集的顶部过度绘制每个群集，最重要的是群集质心。

在上一个scatter中，我已经为colorarr关键字提供了完整的color：这样，每个质心都会获得相应的群集颜色。

在您的代码中，它看起来像这样：

colorarr=["b", "r", "y", "g", "p"]
for k in range(len(cluster)):
    clusterPoint = [dataset[x] for x in cluster[k]]
    x0 = [x[0] for x in clusterPoint]
    y0 = [x[1] for x in clusterPoint]
    center = [(x0, y0) for x in clusterPoint]
    matplotlib.pyplot.scatter(x0, y0, center, c = colorarr[k])
xcentoids, ycentoids = zip(*centoids)
matplotlib.pyplot.scatter(xcentoids, ycentoids, marker='+', color=colorarr, s=330)
picname = "picture_number_" + str(i + 1) + ".png"
matplotlib.pyplot.savefig(picname)

K-Means中的质心

1 个答案: