支持向量机(SVM)——python代码实现

数据集

• 数据集：ris鸢尾花数据集，它包含3个不同品种的鸢尾花：[Setosa，Versicolour，and Virginica]数据，特征：[‘sepal length’, ‘sepal width’, ‘petal length’, ‘petal width’],一共150个数据。由于这是2分类问题，所以选择前两类数据进行算法测试。

代码实现

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import  train_test_split
import matplotlib.pyplot as plt

#加载数据
def create_data():
    iris = load_iris()
    #选取前两类作为数据
    Data=np.array(iris["data"])[:100]
    Label=np.array(iris["target"])[:100]
    Label=Label*2-1
    print("dara shape:",Data.shape)
    print("label shape:",Label.shape)
    return Data, Label

class SVM:
    def __init__(self, max_iter=100, kernel='linear'):
        self.max_iter = max_iter
        self._kernel = kernel

    #参数初始化
    def init_args(self, features, labels):
        self.m, self.n = features.shape
        self.X = features
        self.Y = labels
        self.b = 0.0
        self.alpha = np.ones(self.m)
        self.computer_product_matrix()#为了加快训练速度创建一个内积矩阵
        # 松弛变量
        self.C = 1.0
        # 将Ei保存在一个列表里
        self.create_E()

    #KKT条件判断
    def judge_KKT(self, i):
        y_g = self.function_g(i) * self.Y[i]
        if self.alpha[i] == 0:
            return y_g >= 1
        elif 0 < self.alpha[i] < self.C:
            return y_g ==