5-11、高斯核函数RBF

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets

from matplotlib.colors import ListedColormap

from sklearn.preprocessing import StandardScaler

from sklearn.svm import SVC

from sklearn.pipeline import Pipeline

from sklearn.model_selection import train_test_split

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

x, y = datasets.make_moons(n_samples=1000, noise=0.25, random_state=2020)  # 生成1000个数据样本

plt.figure()

plt.scatter(x[y == 0, 0], x[y == 0, 1], color="r")

plt.scatter(x[y == 1, 0], x[y == 1, 1], color="g")

plt.title('散点图')

plt.show()

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=2020)

# 绘制边界曲线

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_pre = model.predict(x_new)

    zz = y_pre.reshape(x0.shape)

    # 设置颜色

    cus = ListedColormap(["#BA55D3", "#FF69B4", "#FFE4C4"])

    plt.contourf(x0, x1, zz, cmap=cus)

def RBFkernelSVC(gamma):#高斯核函数RBF

    return Pipeline([

        ("std", StandardScaler()),

        ("svc", SVC(kernel="rbf", gamma=gamma))

    ])

sv = RBFkernelSVC(gamma=1)

sv.fit(x_train, y_train)

plot_decision_boundary(sv, axis=([-1.8, 2.5, -1.4, 1.8]))

plt.scatter(x[y == 0, 0], x[y == 0, 1], color="r")

plt.scatter(x[y == 1, 0], x[y == 1, 1], color="g")

plt.title('高斯核函数RBF')

plt.show()

# 打印出分数

print(sv.score(x_test, y_test))

d = datasets.load_iris()

x = d.data

y = d.target

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=2020)

sv = RBFkernelSVC(gamma=10)

sv.fit(x_train, y_train)

# 打印出分数

print(sv.score(x_test, y_test))