自己用python写一个线性支持向量机linearSVM
 

前言：要修改linearSVM的代码，想在网上找一个能用的代码，结果要么调用sklearn库，要么都复制粘贴同一款代码，写得太复杂了，而且有bug，在bing国际版上搜到了一个没有用SMO和拉格朗日算子求解的linearSVM代码，复制过来Mark一下。

完整代码：

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
 
 
class LinearSVMUsingSoftMargin:
    def __init__(self, C=1.0):
        self._support_vectors = None
        self.C = C
        self.beta = None
        self.b = None
        self.X = None
        self.y = None
 
        # n is the number of data points
        self.n = 0
 
        # d is the number of dimensions
        self.d = 0
 
    def __decision_function(self, X):
        return X.dot(self.beta) + self.b
 
    def __cost(self, margin):
        return (1 / 2) * self.beta.dot(self.beta) + self.C * np.sum(np.maximum(0, 1 - margin))
 
    def __margin(self, X, y):
        return y * self.__decision_function(X)
 
    def fit(self, X, y, lr=1e-3, epochs=500):
        # Initialize Beta and b
        self.n, self.d = X.shape
        self.beta = np.random.randn(self.d)
        self.b = 0
 
        # Required only for plotting
        self.X = X
        self.y = y
 
        loss_array = []
        for _ in range(epochs):
            margin = self.__margin(X, y)
            loss = self.__cost(margin)
            loss_array.append(loss)
 
            misclassified_pts_idx = np.where(margin < 1)[0]
            d_beta = self.beta - self.C * y[misclassified_pts_idx].dot(X[misclassified_pts_idx])
            self.beta = self.beta - lr * d_beta
 
            d_b = - self.C * np.sum(y[misclassified_pts_idx])
            self.b = self.b - lr * d_b
 
        self._support_vectors = np.where(self.__margin(X, y) <= 1)[0]
 
    def predict(self, X):
        return np.sign(self.__decision_function(X))
 
    def score(self, X, y):
        P = self.predict(X)
        return np