SMO算法的实现

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs, make_circles, make_moons
from sklearn.preprocessing import StandardScaler
plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文显示
plt.rcParams['axes.unicode_minus'] = False  # 负号显示

class SMO:
    def __init__(self, X, y, C, kernel, alphas, b,errors, user_linear_optim):
        # model = SMO(X, y, C, linear_kernel, initial_alphas, initial_b, np.zeros(m), user_linear_optim=True)
        self.X = X  # 训练样本
        self.y = y  # 类别 label
        self.C = C  # r  正则化常量，用于调整（过）拟合的程度
        self.kernel = kernel  # kernel function   核函数，实现了两个核函数，线性和高斯（RBF）
        self.alphas = alphas  # lagrange multiplier 拉格朗日乘子，与样本一一相对
        self.b = b# scalar bias term 标量，偏移量# self.b = 0
        self.errors = errors # E cache  用于存储alpha值实际与预测值得差值，与样本数量一一相对
        self.m, self.n = np.shape(self.X)  # 训练样本的个数和每个样本的w维度
        self.user_linear_optim = user_linear_optim  # 判断模型是否使用线性核函数
        self.w = np.zeros(self.n)  # 初始化权重w的值，主要用于线性核函数
        # self.b = 0
def linear_kernel(x, y, b=1):
    end = np.dot(x,y.T)-b
    return end
def gaussian_kernel(x, y, sigma=1):
    # 高斯核函数
    if np.ndim(x) == 1 and np.ndim(y) == 1:
        end = np.exp(-(np.linalg.norm(x - y, 2)) ** 2 / (2 * sigma ** 2))
    elif (np.ndim(x) > 1 and np.ndim(y) == 1) or (np.ndim(x) == 1 and np.ndim(y) > 1):
        end = np.exp(-(np.linalg.norm(x - y, 2, axis=1) ** 2) / (2 * sigma ** 2))
    elif np.ndim(x) > 1 and np.ndim(y) > 1:
        end = np.exp(-(np.linalg.norm(x[:, np.newaxis] - y[np.newaxis, :], 2, axis=2) ** 2) / (2 * sigma ** 2))
    return end
#主要用于绘图
def decision_function_plot(alphas, y, kernel, x1 ,x2, b):

    end = np.dot(alphas * y,kernel(x1, x2))-b # *，@ 两个Operators的区别?

    return end
def decision_f_output(model, i):
    if model.user_linear_optim:
        # Equation (J1)
        f_xi=float(np.dot(model.w.