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Python 利用 sklearn 实现 knn

分类: 文章 • 2023-03-28 16:53:15

Python 利用 sklearn 实现 knn

Python
# 导入数据集生成器 from sklearn.datasets import make_blobs # 导入 <span class="wp_keywordlink_affiliate"><a href="https://www.168seo.cn/tag/knn" title="View all posts in knn" target="_blank">knn</a></span> 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
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# 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
 
Python
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。
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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。
 
Python
datas= make_blobs(n_samples=200,centers=2,random_state=8)
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datas=  make_blobs(n_samples=200,centers=2,random_state=8)
 
Python
from pprint import pprint pprint(datas)
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from pprint import pprint
pprint(datas)
 
Python
(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]))
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(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]))
 
Python
x,y = datas
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x,y =  datas
 
Python
plt.scatter(x[:,0],x[:,1],c=y,cmap=plt.cm.spring,edgecolors='k')
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plt.scatter(x[:,0],x[:,1],c=y,cmap=plt.cm.spring,edgecolors='k')
 
Python 利用 sklearn 实现 knn
Python
x[:,0]
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x[:,0]
 
Python
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])
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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])
 
Python
import numpy as np
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import numpy as np
 
Python
a = np.zeros((2,3))
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a = np.zeros((2,3))
 
Python
a[0]
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a[0]
 
Python
array([0., 0., 0.])
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array([0., 0., 0.])
 
Python
a[0] = range(1,4)
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a[0] = range(1,4)
 
Python
a
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a
 
Python
array([[1., 2., 3.], [0., 0., 0.]])
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array([[1., 2., 3.],
       [0., 0., 0.]])
 
Python
# 分片操作,选取二维数组的第一列,第二列
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# 分片操作,选取二维数组的第一列,第二列
 
Python
a[:,0]
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a[:,0]
 
Python
array([1., 0.])
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array([1., 0.])
 
Python
a[:,1]
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2
a[:,1]
 
Python
array([2., 0.])
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array([2., 0.])
 
Python
# 获取第一行 第二行
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# 获取第一行 第二行
 
Python
a[0]
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2
a[0]
 
Python
array([1., 2., 3.])
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2
array([1., 2., 3.])
 
Python
a[1]
1
2
a[1]
 
Python
array([0., 0., 0.])
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2
array([0., 0., 0.])
 
Python
<br />
1
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Python 利用 sklearn 实现 knn


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