sk learn1.0. 1版
' '============================================================================polynomialandrbfkernels.' ' importnumpyation # # # # # # # # # # # # # # # # # # # generatesampledatax=NP.sort (5* NP.ran dort ) ) ) ) ) # # # # fitregressionmodelsvr _ RBF=w wdfk (kernel=' RBF '，C=100，gamma=0.1，epsilon=0.1 ) SVR_Lin=wwdfk ) kernel GK gamma='auto '，degree=3，epsilon=0.1， coef0=1) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ' Polynomial']model_color=['m '，' c '，' g']fig，axel Sharey=true(forIX，SVRinenumerate ) svrs ] 3: axer color=mool or label=' { } model '.format (kernel _ label (IX )，) axes (IX ).Scatter ) y[svr.support_]， y [ SVR.support _ ] label=' { } support vectors '.format (kernel _ label ) IX )、(axes ) IX ).scatter (NP.sabel ) svr.support_ ) ]，facecolor='none '，edgecolor='k '，s=50，laacecolor=) axes [ IX ].legend (loc=' upper Shadow=va='center ' ) fig.text (0.06，0.5，' target '，ha='center '，va='center '，rotation='vertical )