D:\Anaconda2\python.exe D:/PyCharm/start/ML/SVM/SVM_CircleBorder.py
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.6625
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='poly',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.7
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.9375
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='sigmoid',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.6625
本圖中無分界線z,測試結果：
后加：
answer = clf.predict(np.c_[xx.ravel(), yy.ravel()])
z = answer.reshape(xx.shape)
plt.contourf(xx,yy,z,cmap=plt.cm.Paired,alpha=0.8)
代碼：
====================================================
#coding:utf-8
importnumpyasnp
fromsklearnimportsvm
fromsklearn.cross_validationimporttrain_test_split
importmatplotlib.pyplotasplt
'''數據生成'''
h =0.1
x_min, x_max = -1,1
y_min, y_max = -1,1
xx, yy = np.meshgrid( np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
n = xx.shape[0] * xx.shape[1]
x = np.array( [xx.T.reshape(n).T, xx.reshape(n)]).T# [? 8.00000000e-01? -6.00000000e-01]
#print x? [ 20*20 ，2 ]
y = (x[:,0]*x[:,0] + x[:,1]*x[:,1] <0.8)#使Y分為兩類，圈內圈外
#print? y? bool型 false/true
y.reshape(xx.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.2)
clf_linear = svm.SVC(kernel='linear').fit(x_train,y_train)
clf_poly = svm.SVC(kernel='poly',degree=3).fit(x_train,y_train)
clf_rbf = svm.SVC().fit(x_train,y_train)#radial basis function
clf_sigmoid = svm.SVC(kernel='sigmoid').fit(x_train,y_train)
titles = [
'LinearSVC(linear kernel)',
'SVC with polynomial(degree 3) kernel',
'SVC with RBF kernel',
'SVC with Sigmoid kernel'
]
for i,clf in enumerate( (clf_linear,clf_poly,clf_rbf,clf_sigmoid)):
print clf
answer = clf.predict(x_test)
print(np.mean( answer == y_test ))
print"\n"
plt.subplot(2,2,i+1)
plt.subplots_adjust(wspace=0.4,hspace=0.4)
#Put the result into a color plot
answer = clf.predict(np.c_[xx.ravel(), yy.ravel()])
z = answer.reshape(xx.shape)
plt.contourf(xx,yy,z,cmap=plt.cm.Paired,alpha=0.8)
plt.scatter(x_train[:,0], x_train[:,1],c=y_train,cmap=plt.cm.Paired)
plt.xlim(xx.min(),xx.max())
plt.ylim(yy.min(),yy.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
===================================================
results:
===================================================
D:\Anaconda2\python.exe D:/PyCharm/start/ML/SVM/SVM_CircleBorder.py
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.5875
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='poly',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.65
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.975
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='sigmoid',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
0.5875
