统计学习方法第七章作业：SVM非线性支持向量机之SMO序列最小优化算法代码实现

SMO序列最小优化算法

import numpy as np
import math
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_splitclass SVM:def __init__(self,kernal='GKF',C=1):self.keranl=kernalself.b = 0self.X = Noneself.Y = Noneself.a = Noneself.N = Noneself.C = Cself.feature_num = Noneself.Elist = Noneself.K = Nonedef comput_kernal(self,x,y,sita=0.1):x = np.expand_dims(np.array(x),axis=0)y = np.expand_dims(np.array(y),axis=0)if self.keranl == 'GKF':return math.exp(-(x-y).dot((x-y).T)/2*sita**2)elif self.keranl == 'liner':return x.dot(y.T)[0][0]def compute_K(self):self.K = np.zeros((self.N,self.N))for i in range(self.N):for j in range(self.N):self.K[i][j] = self.comput_kernal(self.X[i],self.X[j])def conput_gx(self,index_x):gx = 0for j in range(self.N):gx += self.K[index_x][j] * self.Y[j] *self.a[j]gx += self.breturn gxdef compute_E_list(self):self.Elist = [self.conput_gx(i) - self.Y[i] for i in range(self.N)]def get_a1_index(self):for i in range(self.N):if self.a[i] < self.C and self.a[i] > 0:gi = self.conput_gx(i)if self.Y[i]*gi != 1:return ifor i in range(self.N):if self.a[i] == 0:if self.conput_gx(i)*self.Y[i] < 1:return ielif self.a[i] == self.C:if self.conput_gx(i)*self.Y[i] > 1:return ielse:return -1def get_a2_index(self,a1_index):E1_E2 = [abs(self.Elist[a1_index] - self.Elist[i]) for i in range(self.N)]a2_index = E1_E2.index(max(E1_E2))return a2_indexdef fit(self,X,Y,max_iter=20):self.N = len(X)self.X = Xself.Y = Yself.feature_num = len(X[0])self.a = np.zeros(self.N) + 0.5self.compute_K()for iter in range(max_iter):print('epoch = ' + str(iter))#更新Eself.compute_E_list()#更新a1 a2a1_index = self.get_a1_index()if a1_index == -1:print('all_is_fit_KTT')breaka2_index = self.get_a2_index(a1_index)a1_old = self.a[a1_index]a2_old = self.a[a2_index]L = max(0, a2_old + a1_old-self.C)H = min(self.C, a2_old + a1_old)n = self.K[a1_index][a1_index] + self.K[a2_index][a2_index] - 2*self.K[a1_index][a2_index]a2_new_unc = a2_old + self.Y[a2_index]*(self.Elist[a1_index]-self.Elist[a2_index])/nif a2_new_unc > H:a2_new = Helif a2_new_unc >= L and a2_new_unc <= H:a2_new = a2_new_uncelif a2_new_unc < L:a2_new = La1_new = a1_old + self.Y[a1_index]*self.Y[a2_index]*(a2_old - a2_new)self.a[a1_index] = a1_newself.a[a2_index] = a2_new#更新bb1_new = - self.Elist[a1_index] - self.Y[a1_index]*self.K[a1_index][a1_index]*(a1_new-a1_old) \- self.Y[a2_index]*self.K[a2_index][a1_index]*(a2_new-a2_old) + self.bb2_new = - self.Elist[a2_index] - self.Y[a1_index]*self.K[a1_index][a2_index]*(a1_new-a1_old) \- self.Y[a2_index]*self.K[a2_index][a2_index]*(a2_new-a2_old) + self.bif 0 < a1_new < self.C and 0 < a2_new < self.C:self.b = (b1_new + b2_new) / 2elif 0 < a1_new < self.C:self.b = b1_newelif 0 < a2_new < self.C:self.b = b2_newdef predict_single(self,x):result_1 = [self.a[i]*self.Y[i]*self.comput_kernal(x,self.X[i]) for i in range(self.N)]return np.sign(sum(result_1) + self.b)def predict(self,X):return [self.predict_single(x) for x in X]def main():# X = [[1,2],#      [2,3],#      [3,3],#      [2,1],#      [3,2]]# Y = [1,1,1,-1,-1]X = []Y = []with open('./iris.data', 'r') as f:for i in f:data = i.split(',')X.append([float(j) for j in data[:4]])Y.append(data[4])Y = [1 if i == 'Iris-setosa\n' else -1 for i in Y]train_X, test_X, train_y, test_y = train_test_split(X,Y,test_size=0.2,random_state=9999)svm_trainer = SVM(C=30,kernal='GKF')svm_trainer.fit(train_X,train_y,max_iter=10)result = svm_trainer.predict(test_X)print(result)print(accuracy_score(test_y,result))if __name__ == '__main__':main()#####result##############
/usr/bin/python3 /Users/zhengyanzhao/PycharmProjects/tongjixuexi/SVM_SMO.py
epoch = 0
epoch = 1
epoch = 2
epoch = 3
epoch = 4
epoch = 5
epoch = 6
epoch = 7
epoch = 8
epoch = 9
[1.0, 1.0, 1.0, -1.0, 1.0, 1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
0.8
Process finished with exit code 0

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统计学习方法第七章作业：SVM非线性支持向量机之SMO序列最小优化算法代码实现

SMO序列最小优化算法

统计学习方法第七章作业：SVM非线性支持向量机之SMO序列最小优化算法代码实现相关推荐

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