Python 利用 sklearn 实现 knn

# 导入数据集生成器
from sklearn.datasets import make_blobs
# 导入 knn 分类器
from sklearn.neighbors import KNeighborsClassifier
# 导入画图工具
import matplotlib.pyplot as plt
# 导入数据集拆分
from sklearn.model_selection import train_test_split
make_blobs??

# 在学习机器学习的过程中,常常遇到random_state这个参数,下面来简单叙述一下它的作用。
# 作用:控制随机状态。
datas=  make_blobs(n_samples=200,centers=2,random_state=8)
from pprint import pprint
pprint(datas)
(array([[ 6.75445054,  9.74531933],
       [ 6.80526026, -0.2909292 ],
       [ 7.07978644,  7.81427747],
       [ 6.87472003, -0.16069949],

       [ 8.20316159, 12.01375618],
       [ 6.97321804,  2.576281  ],
       [ 6.42049196,  0.26683712],
       [ 7.40783871,  6.93633083],
       [ 6.54464509,  0.89987351],
       [ 7.58423725, 10.70124388],
       [ 8.80002143,  8.54323521],
       [ 7.1847723 ,  2.22950427],
       [ 7.80361128,  9.74561264],
       [ 7.96481592,  8.03914659],
       [ 6.6571269 ,  7.72756233],
       [ 7.29433984,  9.79486468],
       [ 7.237824  ,  1.70291874],
       [ 8.37153676,  0.98810496],
       [ 6.49932355,  0.24955722],
       [ 9.02255525, 10.06777901],
       [ 7.61227907,  9.4463627 ],
       [ 8.89464606, 10.29806397],
       [ 7.01747287, -1.22016798],
       [ 8.10434971,  1.83659293],
       [ 7.68373899,  1.5632695 ],
       [ 9.43042008,  0.68726533],
       [ 6.26211747,  1.577057  ],
       [ 9.59017028,  0.58441955],
       [ 7.82182216,  0.52633087],
       [ 7.6025272 ,  8.98962387],
       [ 8.48011698,  0.69122126],
       [ 7.63890536, -0.06731493],
       [ 5.84965451,  0.72241791],
       [ 7.46996922,  8.44935323],
       [ 6.8117005 , 10.8840413 ],
       [ 8.67502392,  0.37561206],
       [ 8.12519495,  1.67159478],
       [ 5.07337492, 10.52482973],
       [ 7.48665378,  0.21345453],
       [ 8.11950967,  0.56120493],
       [ 6.15895483,  8.70208685],
       [ 7.94310647,  8.20622208],
       [ 7.95311372,  8.36897664],
       [ 4.96938735,  1.32531048],
       [ 8.8583269 , -0.34648253],
       [10.01367527, 10.52089453],
       [ 8.99334153,  9.7313491 ],
       [ 8.22871505,  1.23014656],
       [ 6.19407512, -0.03183561],
       [ 7.26697254,  9.87045836],
       [ 7.94970781, -0.37340645],
       [ 5.62803952,  9.77585443],
       [ 8.50049461,  9.12147855],
       [ 7.31054144,  0.39102866],
       [ 7.49814373,  9.29677019],
       [ 8.32245091,  9.67819196],
       [ 8.32813617,  9.14002426],
       [ 7.56475962, 11.24762868],
       [ 7.92129785,  0.78018447],
       [ 8.00236864, 10.1691733 ],
       [ 4.33366829, 10.51034676],
       [ 6.02937898, 10.31974057],
       [ 6.88953097,  0.80526874],
       [ 7.51239046,  2.06597042],
       [ 9.17061801, 10.37690696],
       [ 7.63027116,  8.69797933],
       [ 8.35312192,  0.20325714],
       [ 8.72578696, 10.34691678],
       [ 5.44099009,  1.59585563],
       [ 7.56093115, -0.51702689],
       [ 6.02376341, -0.52025947],
     ),
 array([0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,
       1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,
       1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,
       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
       0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1,
       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,
       0, 1]))
x,y =  datas
plt.scatter(x[:,0],x[:,1],c=y,cmap=plt.cm.spring,edgecolors='k')
Python 利用 sklearn 实现 knn-Python 技术分享 Java技术分享 Python 爬虫技术_微信公众号:zeropython—昊天博客
x[:,0]
array([ 6.75445054,  6.80526026,  7.07978644,  6.87472003,  8.06164078,
        7.4934131 ,  4.69777002,  9.19642422,  8.80996213,  7.5952749 ,
        8.20330317,  8.59258191,  6.89228905,  8.00405631,  8.14715032,
        7.06363179,  6.34526126,  5.28435774,  6.62257531,  7.40314915,
        7.27423265,  8.77188508,  6.39995999,  7.44636985,  7.74488453,
        9.10088858,  8.10044749,  8.73747674,  6.51876894,  7.16251356,
        6.57119411,  7.1354011 ,  7.31294296,  7.52733204,  6.0160163 ,
        6.73117031,  6.11962018,  7.88579276,  7.32112244,  7.62051584,
        6.96767867,  8.51730001,  7.92672195,  5.52161775,  6.93568163,
        7.89765814,  7.40292703,  8.28827095,  7.33912656,  5.27801757,
        5.57550594,  8.67425268,  7.55303352,  6.84661976,  6.26977193,
        7.09962807,  5.5987887 ,  8.0060449 ,  6.85769503,  6.19399963,
        8.68173394,  5.82259795,  5.30528133,  6.89703841,  5.9389756 ,
        7.13760133,  7.51718983,  8.08034605,  6.89078889,  6.95802459,
        8.91111219,  7.57818277,  6.24007751,  7.79924692,  7.49985237,
        9.94109903,  7.07232613,  7.50126258,  6.63110319,  6.6060513 ,
        8.81545663,  6.5688005 ,  9.15668309,  7.45637594,  7.29548244,
        8.20316159,  6.97321804,  6.42049196,  7.40783871,  6.54464509,
        7.58423725,  8.80002143,  7.1847723 ,  7.80361128,  7.96481592,
        6.6571269 ,  7.29433984,  7.237824  ,  8.37153676,  6.49932355,
        9.02255525,  7.61227907,  8.89464606,  7.01747287,  8.10434971,
        7.68373899,  9.43042008,  6.26211747,  9.59017028,  7.82182216,
        7.6025272 ,  8.48011698,  7.63890536,  5.84965451,  7.46996922,
        6.8117005 ,  8.67502392,  8.12519495,  5.07337492,  7.48665378,
        8.11950967,  6.15895483,  7.94310647,  7.95311372,  4.96938735,
        8.8583269 , 10.01367527,  8.99334153,  8.22871505,  6.19407512,
        7.26697254,  7.94970781,  5.62803952,  8.50049461,  7.31054144,
        7.49814373,  8.32245091,  8.32813617,  7.56475962,  7.92129785,
        8.00236864,  4.33366829,  6.02937898,  6.88953097,  7.51239046,
        9.17061801,  7.63027116,  8.35312192,  8.72578696,  5.44099009,
        7.56093115,  6.02376341,  7.15013321,  7.56833386,  7.09022949,
        5.94356564,  6.25817082,  5.94205586,  7.82510107,  5.88994248,
        6.40269472,  7.64534862,  6.8830708 ,  7.24044576,  9.4035308 ,
        6.55819206,  6.58341965,  7.83939881,  7.22095192,  7.8440213 ,
        7.39634594,  9.10772988,  6.93540782,  7.9465776 ,  7.92430026,
        6.79156708,  6.28516091,  7.54257819,  7.40565933,  7.51463404,
        6.40863862,  6.5342397 ,  5.17209648,  5.49953213,  9.86936252,
        7.84725158,  8.14330144,  7.28724996,  6.0888764 ,  7.59635095,
        6.71388804,  7.3307687 ,  8.18240421,  8.53178848,  6.91511696,
        7.82944816,  6.09382282,  7.24211001,  8.2634157 ,  8.39800148])
import numpy as np
a = np.zeros((2,3))
a[0]
array([0., 0., 0.])
a[0] = range(1,4)
a
array([[1., 2., 3.],
       [0., 0., 0.]])
# 分片操作,选取二维数组的第一列,第二列
a[:,0]
array([1., 0.])
a[:,1]
array([2., 0.])
# 获取第一行 第二行
a[0]
array([1., 2., 3.])
a[1]
array([0., 0., 0.])

HTTPX 基础教程-新乡seo|网站优化,网站建设_微信公众号:zeropython—昊天博客