深入浅出python机器学习_7.1_支持向量机
2024-06-03 17:34:59
%matplotlib inline# 支持向量机SVM的核函数import numpy as npimport matplotlib.pyplot as pltfrom sklearn import svmfrom sklearn.datasets import make_blobsX,y=make_blobs(n_samples=50,centers=2,random_state=6)print('X:\n',X,'\n')print('y:\n',y,'\n')
X:[[ 6.45519089 -9.46356669][ 8.49142837 -2.54974889][ 6.87151089 -10.18071547][ 9.49649411 -3.7902975 ][ 7.67619643 -2.82620437][ 6.3883927 -9.25691447][ 9.24223825 -3.88003098][ 5.95313618 -6.82945967][ 6.86866543 -10.02289012][ 7.52132141 -2.12266605][ 7.29573215 -4.39392379][ 6.85086785 -9.92422452][ 4.29225906 -8.99220442][ 8.21597398 -2.28672255][ 7.9683312 -3.23125265][ 8.68185687 -4.53683537][ 6.77811308 -9.80940478][ 7.93333064 -3.51553205][ 7.73046665 -4.72901672][ 7.37578372 -8.7241701 ][ 6.95292352 -8.22624269][ 8.07502382 -4.25949569][ 7.39169472 -3.1266933 ][ 6.59823581 -10.20150177][ 7.27059007 -4.84225716][ 8.71445065 -2.41730491][ 5.73005848 -4.19481136][ 9.42169269 -2.6476988 ][ 6.26221548 -8.43925752][ 7.89359985 -7.41655113][ 8.98426675 -4.87449712][ 10.48848359 -2.75858164][ 5.45644482 -8.99900075][ 6.50072722 -3.82403586][ 7.07705089 -2.4047943 ][ 9.07568367 -4.21790533][ 7.92736799 -9.7615272 ][ 7.29885085 -9.90563956][ 6.6008728 -8.07144707][ 5.94709536 -9.05353781][ 5.88397542 -8.37284513][ 5.37042238 -2.44715237][ 8.21201164 -1.54781358][ 6.40500112 -7.50322463][ 6.94752781 -9.75794397][ 6.04907774 -8.76969991][ 8.32932478 -8.47191434][ 6.37734541 -10.61510727][ 4.29810787 -8.41461865][ 7.97164446 -3.38236058]] y:[1 0 1 0 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 11 1 1 1 0 0 1 1 1 1 1 1 0]
clf=svm.SVC(kernel='linear',C=1000)clf.fit(X,y)plt.scatter(X[:,0],X[:,1],c=y,s=30,cmap=plt.cm.Paired)
<matplotlib.collections.PathCollection at 0x17f3a14d0f0>
ax=plt.gca()print('打印ax:\n',ax,'\n')print('ax的类型：\n',type(ax),'\n')xlim=ax.get_xlim()print('xlim:\n',xlim,'\n')print('xlim的类型:\n',type(xlim),'\n')ylim=ax.get_ylim()print('ylim:\n',ylim,'\n')
打印ax:AxesSubplot(0.125,0.125;0.775x0.755) ax的类型：<class 'matplotlib.axes._subplots.AxesSubplot'> xlim:(0.0, 1.0) xlim的类型:<class 'tuple'> ylim:(0.0, 1.0)
xx=np.linspace(xlim[0],xlim[1],30)yy=np.linspace(ylim[0],ylim[1],30)print('打印xx:\n',xx,'\n')print('xx的类型:\n',type(xx),'\n')xx_=np.arange(xlim[0],xlim[1],0.1)print('打印xx_:\n',xx_,'\n')print('打印xx.shape',xx.shape,'\n')print('xx_的类型:\n',type(xx_),'\n')YY,XX=np.meshgrid(yy,xx)print('打印XX:\n',XX,'\n')xy=np.vstack([XX.ravel(),YY.ravel()]).Tprint('打印XX.ravel():\n',XX.ravel(),'\n')print('打印np.vstack([XX.ravel(),YY.ravel()])\n',np.vstack([XX.ravel(),YY.ravel()]),'\n')print('打印xy:\n',xy,'\n')Z=clf.decision_function(xy).reshape(XX.shape)
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0.96551724 0.96551724 0.965517241. 1. 1. 1. 1. 1.1. 1. 1. 1. 1. 1.1. 1. 1. 1. 1. 1.1. 1. 1. 1. 1. 1.1. 1. 1. 1. 1. 1. ] 打印np.vstack([XX.ravel(),YY.ravel()])[[0. 0. 0. ... 1. 1. 1. ][0. 0.03448276 0.06896552 ... 0.93103448 0.96551724 1. ]] 打印xy:[[0. 0. ][0. 0.03448276][0. 0.06896552]...[1. 0.93103448][1. 0.96551724][1. 1. ]]
ax.contour(XX,YY,Z,colors='k',levels=[-1,0,1],alpha=0.5,linestyles=['--','-','--'])ax.scatter(clf.support_vectors_[:,0],clf.support_vectors_[:,1],s=100,linewidths=1,facecolors='none')plt.show()
c:\users\huawei\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:2: UserWarning: No contour levels were found within the data range.
print('xx.shape:',xx.shape,'\n')print('yy.shape:',yy.shape,'\n')print('XX.shape:',XX.shape,'\n')print('YY.shape:',YY.shape,'\n')print('XX.ravel().shape:',XX.ravel().shape,'\n')print('np.vstack([XX.ravel(),YY.ravel()]).shape:',np.vstack([XX.ravel(),YY.ravel()]).shape,'\n')print('np.vstack([XX.ravel(),YY.ravel()]).T.shape:',np.vstack([XX.ravel(),YY.ravel()]).T.shape,'\n')print('clf.decision_function(xy).shape:',clf.decision_function(xy).shape,'\n')xx.shape: (30,) yy.shape: (30,) XX.shape: (30, 30) YY.shape: (30, 30) XX.ravel().shape: (900,) np.vstack([XX.ravel(),YY.ravel()]).shape: (2, 900) np.vstack([XX.ravel(),YY.ravel()]).T.shape: (900, 2) clf.decision_function(xy).shape: (900,)
print('Z:\n',Z,'\n')Z:[[-3.2115599 -3.24045912 -3.26935835 -3.29825757 -3.3271568 -3.35605602-3.38495524 -3.41385447 -3.44275369 -3.47165292 -3.50055214 -3.52945137-3.55835059 -3.58724982 -3.61614904 -3.64504827 -3.67394749 -3.70284672-3.73174594 -3.76064516 -3.78954439 -3.81844361 -3.84734284 -3.87624206-3.90514129 -3.93404051 -3.96293974 -3.99183896 -4.02073819 -4.04963741][-3.22031683 -3.24921606 -3.27811528 -3.30701451 -3.33591373 -3.36481296-3.39371218 -3.4226114 -3.45151063 -3.48040985 -3.50930908 -3.5382083-3.56710753 -3.59600675 -3.62490598 -3.6538052 -3.68270443 -3.71160365-3.74050288 -3.7694021 -3.79830132 -3.82720055 -3.85609977 -3.884999-3.91389822 -3.94279745 -3.97169667 -4.0005959 -4.02949512 -4.05839435][-3.22907377 -3.25797299 -3.28687222 -3.31577144 -3.34467067 -3.37356989-3.40246912 -3.43136834 -3.46026756 -3.48916679 -3.51806601 -3.54696524-3.57586446 -3.60476369 -3.63366291 -3.66256214 -3.69146136 -3.72036059-3.74925981 -3.77815904 -3.80705826 -3.83595748 -3.86485671 -3.89375593-3.92265516 -3.95155438 -3.98045361 -4.00935283 -4.03825206 -4.06715128][-3.2378307 -3.26672993 -3.29562915 -3.32452838 -3.3534276 -3.38232683-3.41122605 -3.44012528 -3.4690245 -3.49792372 -3.52682295 -3.55572217-3.5846214 -3.61352062 -3.64241985 -3.67131907 -3.7002183 -3.72911752-3.75801675 -3.78691597 -3.8158152 -3.84471442 -3.87361364 -3.90251287-3.93141209 -3.96031132 -3.98921054 -4.01810977 -4.04700899 -4.07590822][-3.24658764 -3.27548686 -3.30438609 -3.33328531 -3.36218454 -3.39108376-3.41998299 -3.44888221 -3.47778144 -3.50668066 -3.53557988 -3.56447911-3.59337833 -3.62227756 -3.65117678 -3.68007601 -3.70897523 -3.73787446-3.76677368 -3.79567291 -3.82457213 -3.85347136 -3.88237058 -3.9112698-3.94016903 -3.96906825 -3.99796748 -4.0268667 -4.05576593 -4.08466515][-3.25534457 -3.2842438 -3.31314302 -3.34204225 -3.37094147 -3.3998407-3.42873992 -3.45763915 -3.48653837 -3.5154376 -3.54433682 -3.57323604-3.60213527 -3.63103449 -3.65993372 -3.68883294 -3.71773217 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plt.scatter(XX,YY,c=Z,s=30,cmap=plt.cm.Paired)plt.show()
# 测试pp=np.linspace(0,10,900)tt=np.linspace(0,10,900)uu=np.vstack([pp,tt]).TZ_=clf.decision_function(uu).reshape(XX.shape)plt.scatter(XX,YY,c=Z_,s=30,cmap=plt.cm.Paired)plt.show()
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