支持向量机之知识汇总

1.支持向量机推导

2.案例

 

import numpy as np
import sklearn.model_selection as ms
import sklearn.svm as svm
import sklearn.metrics as sm
import matplotlib.pyplot as mp
import matplotlib as mpl

#绘制分类图的过程
#1.求出所有的格点，并绘出预测结果。 2.画出测试数据的散点图，
if __name__ == '__main__':
    data=np.loadtxt(open("/home/python/Desktop/test.csv"),dtype=float,delimiter=",",)
    train_x,test_x,train_y,test_y=ms.train_test_split(data[:,:-1],data[:,-1],test_size=0.25)
    svm1=svm.SVC(kernel="linear",C=100,class_weight="balanced")
    svm1.fit(train_x,train_y)
    result=svm1.predict(test_x)
    s=sm.classification_report(test_y,result)#分类准确率相关数据
    #绘制分类图
    x1_min,x1_max=data[:,0].min()-1,data[:,0].max()+1
    x2_min, x2_max = data[:, 1].min()-1, data[:, 1].max() + 1
    x1,x2=np.meshgrid(np.linspace(x1_min,x1_max,num=100),np.linspace(x2_min, x2_max, num=100))
    b=np.column_stack((x1.flatten(),x2.flatten()))
    result=svm1.predict(b)
    mp.pcolormesh(x1,x2,result.reshape(x1.shape),cmap="jet")
    mp.scatter(test_x[:,0],test_x[:,1],s=30,c=test_y,cmap="gray",label="sample")
    mp.show()

 

2.松弛因子

3.低维高维转换内积

4.核函数