sklearn 中的RBF核

import numpy as np
import matplotlib.pyplot as plt
from sklearn import  datasets
X, y = datasets.make_moons(noise=0.15, random_state=666)
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

sklearn 中的RBF核

from sklearn.preprocessing import StandardScaler,PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

def RBFKernelSVC1(gamma):
    return Pipeline([
        ("stand_c",StandardScaler()),
        ("svcc",SVC(kernel="rbf",gamma=gamma))
    ])

def plot_decision_boundary(model, axis):
    
    x0, x1 = np.meshgrid(
        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),
        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),
    )
    X_new = np.c_[x0.ravel(), x1.ravel()]

    y_predict = model.predict(X_new)
    zz = y_predict.reshape(x0.shape)

    from matplotlib.colors import ListedColormap
    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
    
    plt.contourf(x0, x1, zz, cmap=custom_cmap)

svc=RBFKernelSVC1(gamma=1)
svc.fit(X,y)
plot_decision_boundary(svc,axis=[-1.5,2.5,-1,1.5])
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

sklearn 中的RBF核

svc100=RBFKernelSVC1(gamma=100)
svc100.fit(X,y)
plot_decision_boundary(svc100,axis=[-1.5,2.5,-1,1.5])
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

sklearn 中的RBF核

svc10=RBFKernelSVC1(gamma=10)
svc10.fit(X,y)
plot_decision_boundary(svc10,axis=[-1.5,2.5,-1,1.5])
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

sklearn 中的RBF核

svc01=RBFKernelSVC1(gamma=0.1)
svc01.fit(X,y)
plot_decision_boundary(svc01,axis=[-1.5,2.5,-1,1.5])
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

sklearn 中的RBF核