def testRbf(k1=1.3):
dataArr, labelArr = svm.loadDataSet('testSetRBF.txt')
b, alphas = PlattSMO(dataArr, labelArr, 200, 0.0001, 10000,
('rbf', k1)) # C=200 important
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
svInd = nonzero(alphas.A > 0)[0] # 取非零alphas值的索引，构建支持向量的矩阵
sVs = datMat[svInd] # get matrix of only support vectors
labelSV = labelMat[svInd]
print "there are %d Support Vectors" % shape(sVs)[0]
m, n = shape(datMat)
errorCount = 0
'''
w = Sigma(alpha_i * y_i * phi(x_i)),<Sigma为求和>
g_svm(x)=sign(w.T*phi(x)+b)=sign(Sigma(alpha_i*y_i*phi(x_i)*phi(x))+b)=sign(Sigma(alpha_i*y_i*kernel(x_i,x))+b)
<kernel()为核函数，phi()为高维空间的映射函数，alpha_i>0,对应的x_i为支持向量>
'''
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "the training error rate is: %f" % (float(errorCount) / m)
dataArr, labelArr = svm.loadDataSet('testSetRBF2.txt')
errorCount = 0
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
m, n = shape(datMat)
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "the test error rate is: %f" % (float(errorCount) / m)
import svm
from numpy import *
dataMat,labelMat=svm.loadDataSet('testSet.txt')
#print(labelMat)
b,alphas=svm.smoSimple(dataMat,labelMat,0.6,0.001,40)
print(b)
#coding:utf-8
import svm
dataArr, labelArr = svm.loadDataSet('testSet.txt')
#print labelArr
b, alphas = svm.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
print b
print alphas[alphas > 0]
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 5 15:22:26 2018
@author: weixw
"""
import svm as sm
#通过训练数据计算 b, alphas
dataArr, labelArr = sm.loadDataSet('train.txt')
b, alphas = sm.smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', 0.10))
sm.drawDataMap(dataArr, labelArr, b, alphas)
sm.getTrainingDataResult(dataArr, labelArr, b, alphas, 0.10)
dataArr1, labelArr1 = sm.loadDataSet('test.txt')
#测试结果
sm.getTestDataResult(dataArr1, labelArr1, b, alphas, 0.10)
#!usr/bin/env python3 # -*- coding:utf-8 -*- """ #@author:Benny.Chen #@file: main.py #@time: 2020/6/5 11:50 #@email:[email protected] """ import svm if __name__ == '__main__': dataArr,labelArr = svm.loadDataSet("testSet.txt") b,alphas = svm.smoSimple(dataArr, labelArr, 0.6, 0.001, 40)
import svm
from numpy import *
dataArr, labelArr = svm.loadDataSet("machinelearninginaction/Ch06/testSet.txt")
print(labelArr)
print(dataArr)
def testRbf(k1=1.3):
dataArr, labelArr = svm.loadDataSet('dataset.txt')
alphas, b = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1))
