Coursera NG 机器学习第六周 SVM分类 Spam Classifier Python 实现

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VIP免费原创最新推荐文章于 2026-02-23 00:51:31 发布 740 阅读 AI 写同款· GEO 优化 ›

基于Andrew ng课后作业6，matlab实现svm算法的垃圾邮件分类器（spam classifier）垃圾邮件分类器构造步骤：获得数据集 ---> 构造字典 ---> 获得特征向量X,y ---> 训练模型 ---> 预测数据。重在字典建造。阅读详情

SVM 分类

ex6.py

import numpy as np
from scipy.io import loadmat
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from ex6modules import *
 
#Exmaple 1
data=loadmat('ex6data1.mat')
X=data['X']
y=data['y'].ravel()
plotData(X,y)
plt.show()
 
#Plot C=1's boundary
clf=SVC(C=1,kernel='linear')
clf.fit(X,y)
visualizeBoundaryLinear(X,y,clf)
 
#Plot C=100's boundary
clf=SVC(C=100,kernel='linear')
clf.fit(X,y)
visualizeBoundaryLinear(X,y,clf)
 
#Examing Gaussian Kernel
sim=gaussianKernel(np.array([1,2,1]),np.array([0,4,-1]),sigma=2)
print("similarity :",sim)
 
#Example 2
data=loadmat('ex6data2.mat')
X=data['X']
y=data['y'].ravel()
plotData(X,y)
plt.show()
 
clf=SVC(C=1,kernel='rbf',gamma=50) #gamma=1/(2*sigma**2)
clf.fit(X,y)
visualizeBoundary(X,y,clf)
 
#Exmaple 3
data=loadmat('ex6data3.mat')
X=data['X']
y=data['y'].ravel()
Xval=data['Xval']
yval=data['yval']
plotData(X,y)
plt.show()
 
Csteps=np.array([.01,.03,.1,.3,1,3,10,30])
gammasteps=np.array([1/(2*.01**2),1/(2*.03**2),1/(2*.1**2),1/(2*.3**2),\
                     1/(2*1**2),1/(2*3**2),1/(2*10**2),1/(2*30**2)])
 
Cmin,gammamin=findBest(Csteps,gammasteps,X,y,Xval,yval)
 
clf=SVC(C=Cmin,kernel='rbf',gamma=gammamin)
clf.fit(X,y)
visualizeBoundary(X,y,clf)

ex6modules.py

import matplotlib.pyplot as plt
import numpy as np
from sklearn.svm import SVC
 
def plotData(X,y):
    pos = np.where(y == 1)
    neg = np.where(y == 0)
    plt.scatter(X[pos, 0]