官网manual详见:sklearn.datasets.make_blobs
klearn.datasets.make_blobs( n_samples=100, #样本总数
n_features=2, #每个样本的特征值总数
centers=3, #有几簇聚类数据
cluster_std=1.0, #每簇的标准差,可以设置成[1.0, 2.0, 4.0]依次依次表示每簇数据的标准差
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None)
一般使用sklearn的make_blobs模块生成聚类算法使用的测试数据样本集,聚类数据样本集。
样本集:
1)n_samples:样本集中的样本总数
2)n_features:如果每个样本有2个特征数,则可以通过设置入参n_features=2即可
3)centers:如果需要有2簇(或者3簇)聚类数据,可以通过设置入参centers=2即可
4)返回的data就是样本集,data是一个n_features列的数组,data的总元素=n_samples。即一个坐标轴代表样本的一个特征
data, data_flag = make_blobs(……)
5)返回的第二个data_flag就是每个样本的标记
data, data_flag = make_blobs(……)
1.代码
# -*- coding: utf-8 -*-
"""
@author: 蔚蓝的天空TOM
Talk is cheap, show me the code
Aim:使用sklearn的make_blobs模块生成聚类算法使用的测试数据,聚类数据样本集
"""
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
if __name__ == '__main__':
#待生成的样本的总数N
N = 400
#每个样本的特征数m
feature_cnt = 2
#要生成的样本中心(类别)数,或者是确定的中心点
centers = 3
#每个类别的方差,例如我们希望生成2类数据,其中一类比另一类具有更大的方差,可以将
stds = [1.0, 2.0, 4.0]
#生成的样本数据集
data = []
#样本数据集的标签
data_flag = []
#生成3簇聚类数据集,每个样本有2个特征数
data, data_flag = make_blobs(n_samples=N, n_features=feature_cnt, centers=centers, cluster_std=stds)
#显示方法1:打印数据
print('feature1:',data[:, 0])
print('feature2:',data[:,1])
print('sample flag:',data_flag)
#显示方法2:显示图形
plt.scatter(data[:, 0], data[:, 1], c=data_flag)
plt.show()
runfile('C:/Users/Administrator/tom_sklearn_make_blobs.py', wdir='C:/Users/Administrator')
feature1: [ 2.54001684 5.22477454 -6.10862356 -9.44626916 -7.19535902
5.2679688 -11.42425981 -9.67999405 -5.75053619 3.41957156
5.09385822 3.63539588 2.39926446 3.99817201 4.19291335
2.73072773 5.76337415 2.4222701 3.15417303 0.94907246
1.36182381 4.96241642 4.86671958 5.20985281 1.64624643
-14.72680244 7.99360547 2.65330678 3.15022979 4.56360555
4.63259477 2.59664438 5.56695351 1.18395658 1.96238448
1.09608662 -7.66847365 3.41224687 0.49211757 1.18152355
-9.17519306 3.87926943 -0.13577271 0.93578799 3.55081517
5.23720729 -0.64761192 4.69662965 7.0559361 -3.96957324
-15.22902701 4.48168543 5.24452705 -7.14471869 5.04623679
3.07387132 -8.27907084 9.67937407 -6.45947198 -1.03013999
-2.95222822 6.08051981 7.47502014 3.87496611 4.26346936
5.28032249 5.36402547 -12.50263226 -9.98922844 3.44383279
5.53567406 2.77681492 2.47596677 3.71157681 6.93141516
4.81154579 4.39246095 3.46001954 7.16033998 -9.36287977
4.42585417 -12.01046108 -5.48842508 -8.09393056 -7.63301015
1.3017978 6.32493182 4.91798834 2.83690423 -12.45202027
6.55735457 0.83800565 -13.92198443 3.55798733 3.47635849
1.11887909 -7.3717065 5.36060022 4.84613756 -5.44547875
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3.16026452 4.03150833 3.90216926 3.77203953 1.45972767
5.01045185 3.68028032 1.6941889 -9.52076656 -8.69411543
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3.63055553 1.29130707 5.48626597 2.25498677 3.82380313
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4.49933284 -4.88631602 4.46179572 2.33630831 4.48273994
-3.3881548 2.63858106 2.06087063 2.45884774 5.69174821
-10.47167177 -8.91294921 3.88988285 5.36876469 -10.97848427
2.08420097 3.63561427 1.65981692 4.72266574 7.50226355
-3.11479009 4.67510804 -9.38099514 -7.51121123 5.06025869
-12.30393289 -18.3023349 5.43396406 4.29042285 4.9474745
2.82259014 3.81591397 2.57205612 1.83831286 8.69160799
-13.38330803 5.36516825 -2.66978701 -8.48009189 4.31809912
2.66554538 2.97743468 -7.4361818 4.12355177 -14.67419105
4.3903146 -8.43656594 -12.82606416 4.88737576 -11.74928273
-7.34046462 -0.29160917 -11.27810822 3.10491547 4.28404647
-5.62831231 4.48705667 -8.94656524 5.97360668 -6.11086158
-6.33392368 -8.6906396 1.30013255 5.24645345 -9.86873502
1.75938389 6.00053569 2.47007016 2.55130305 -7.43933537
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-1.99534127 -1.3469743 4.11144355 5.93526313 -8.05510561
5.62432519 2.82347606 -11.2159931 3.55754881 3.40021177]
feature2: [ 5.00243197 -9.73014489 -5.40541613 -3.35791757 5.65380623
-9.2279083 0.49661355 -4.06477365 -3.28652232 6.52045498
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9.04605494 8.15475066 5.85390605 -7.56229274 7.77637249
-8.45067951 -9.42754394 7.99815742 -6.15420462 9.83919929
-8.07112339 8.58693156 -9.99714922 -8.95085152 -8.74209638
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-8.20117025 -3.75691967 -8.1787106 6.6797308 7.44558349
-4.30765946 -9.1693361 1.16835121 5.03792399 -9.21486755
-9.45776835 -3.48606248 -9.19175617 9.7571073 -0.43668415
-5.6479297 -8.09154068 -7.57378712 6.61440419 8.72150175
-8.11018272 -2.71201037 4.88411041 1.01675309 -2.76660598
-7.92114188 10.26739083 9.44663616 7.77877745 -8.68765553
7.63491524 4.72258202 -9.79000379 0.71140727 -8.89727697
7.57780466 -8.28398667 -6.52169789 -8.29901036 9.94526363
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sample flag: [1 0 2 2 2 0 2 2 2 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 1 2 1 0 0 0 0 0 1 0 1 0 2
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(end)