效果图
操作过程
1. Python源代码Kmeans
import csv
import matplotlib.pyplot as plt
import numpy as np
import xlrd2
from sklearn import preprocessing
from mpl_toolkits.mplot3d import Axes3D
# 标准化数据集 X
from xlsxwriter import worksheet
def normalize(X, axis=-1, p=2):
lp_norm = np.atleast_1d(np.linalg.norm(X, p, axis))
lp_norm[lp_norm == 0] = 1
return X / np.expand_dims(lp_norm, axis)
# 计算一个样本与数据集中所有样本的欧氏距离的平方
def euclidean_distance(one_sample, X):
one_sample = one_sample.reshape(1, -1)
X = X.reshape(X.shape[0], -1)
distances = np.power(np.tile(one_sample, (X.shape[0], 1)) - X, 2).sum(axis=1)
return distances
class Kmeans():
"""Kmeans聚类算法.
Parameters:
-----------
k: int
聚类的数目.
max_iterations: int
最大迭代次数.
varepsilon: float
判断是否收敛, 如果上一次的所有k个聚类中心与本次的所有k个聚类中心的差都小于varepsilon,
则说明算法已经收敛
"""
def __init__(self, k=4, max_iterations=500, varepsilon=0.001):
self.k = k
self.max_iterations = max_iterations
self.varepsilon = varepsilon
# 从所有样本中随机选取self.k样本作为初始的聚类中心
def init_random_centroids(self, X):
n_samples, n_features = np.shape(X)
centroids = np.zeros((self.k, n_features))
for i in range(self.k):
centroid = X[np.random.choice(range(n_samples))]
centroids[i] = centroid
return centroids
# 返回距离该样本最近的一个中心索引[0, self.k)
def _closest_centroid(self, sample, centroids):
distances = euclidean_distance(sample, centroids)
closest_i = np.argmin(distances)
return closest_i
# 将所有样本进行归类，归类规则就是将该样本归类到与其最近的中心
def create_clusters(self, centroids, X):
n_samples = np.shape(X)[0]
clusters = [[] for _ in range(self.k)]
for sample_i, sample in enumerate(X):
centroid_i = self._closest_centroid(sample, centroids)
clusters[centroid_i].append(sample_i)
return clusters
# 对中心进行更新
def update_centroids(self, clusters, X):
n_features = np.shape(X)[1]
centroids = np.zeros((self.k, n_features))
for i, cluster in enumerate(clusters):
centroid = np.mean(X[cluster], axis=0)
centroids[i] = centroid
return centroids
# 将所有样本进行归类，其所在的类别的索引就是其类别标签
def get_cluster_labels(self, clusters, X):
y_pred = np.zeros(np.shape(X)[0])
for cluster_i, cluster in enumerate(clusters):
for sample_i in cluster:
y_pred[sample_i] = cluster_i
return y_pred
# 对整个数据集X进行Kmeans聚类，返回其聚类的标签
def predict(self, X):
# 从所有样本中随机选取self.k样本作为初始的聚类中心
centroids = self.init_random_centroids(X)
# 迭代，直到算法收敛(上一次的聚类中心和这一次的聚类中心几乎重合)或者达到最大迭代次数
for _ in range(self.max_iterations):
# 将所有进行归类，归类规则就是将该样本归类到与其最近的中心
clusters = self.create_clusters(centroids, X)
former_centroids = centroids
# 计算新的聚类中心
centroids = self.update_centroids(clusters, X)
# 如果聚类中心几乎没有变化，说明算法已经收敛，退出迭代
diff = centroids - former_centroids
if diff.any() < self.varepsilon:
break
return self.get_cluster_labels(clusters, X)
data = []
# 读取数据
# 待计算样本数据的文件名称是means-input.xls,与python代码放在同一个目录下
wk = xlrd2.open_workbook('means-input.xls')
# Excel sheet页取名为sheet1
sheets = wk.sheet_by_name('sheet1')
ws = wk.sheet_by_index(0)
# 获取总行数
nrows = ws.nrows
for i in range(1, nrows):
row = sheets.cell_value(i, 0)
row1 = sheets.cell_value(i, 1)
row2 = sheets.cell_value(i, 2)
data.append([ float(row)*100, float(row1)*100, float(row2)*100])
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))
A = np.array(data)
print(A[0])
X=min_max_scaler.fit_transform(A)
num, dim = X.shape
clf = Kmeans(k=2)
y_pred = clf.predict(X)
color = ['r', 'g', 'b', 'c', 'y', 'm', 'k']
ax = plt.subplot(111, projection='3d')
# kmeans计算结果保存的文件名means-output.csv;输出图像资源需用户自定义命名
f = open('means-output.csv', 'w', encoding='utf-8', newline='')
csv_writer = csv.writer(f)
for p in range(0,num):
y=y_pred[p]
csv_writer.writerow([y])
ax.scatter((A[p, 0]), (A[p, 1]), (A[p, 2]), c=color[(int(y))])
f.close()
plt.show()
2. 输入数据Excel格式
- 文件名：means-input.xls；且与代码放在同一个目录下
- sheet页名：sheet1
- 三个坐标放在紧邻的三个单元格中
- 没有表头，数据从第一行开始填充
3.txt空格分割坐标转化为Excel数据Python代码
# -*- encoding: utf-8 -*-
import xlwt #需要的模块
def txt_xls(filename,xlsname):
"""
:文本转换成xls的函数
:param filename txt文本文件名称、
:param xlsname 表示转换后的excel文件名
"""
try:
f = open(filename)
xls=xlwt.Workbook()
#生成excel的方法，声明excel
sheet = xls.add_sheet('sheet1',cell_overwrite_ok=True)
x = 0
while True:
#按行循环，读取文本文件
line = f.readline()
if not line:
break #如果没有内容，则退出循环
for i in range(len(line.split(' '))):
item=line.split(' ')[i]
sheet.write(x,i,item) #x单元格经度，i 单元格纬度
x += 1 #excel另起一行
f.close()
xls.save(xlsname) #保存xls文件
except:
raise
if __name__ == "__main__" :
# txt文档名是input.txt
filename = "input.txt"
# 输出文件名是output.xls
xlsname = "output.xls"
txt_xls(filename,xlsname)
4.QA
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