def testRbf(k1=1.3):
dataArr, labelArr = SMO.loadDataSet('testSetRBF.txt')
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1))
dataMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
svInd = nonzero(alphas.A > 0)[0]
sVs = dataMat[svInd]
labelLSV = labelMat[svInd]
print("there are %d Support Vectors" % shape(sVs)[0])
m, n = shape(dataMat)
errorCount = 0
for i in range(m):
kernelEval = kernelTrans(sVs, dataMat[i, :], ('rbf', k1))
predict = kernelEval.T * multiply(labelLSV, alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]): errorCount += 1
print("the training error rate is: %f" % (float(errorCount) / m))
dataArr, labelArr = SMO.loadDataSet('testSetRBF2.txt')
errorCount = 0
dataMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
m, n = shape(dataMat)
for i in range(m):
kernelEval = kernelTrans(sVs, dataMat[i, :], ('rbf', k1))
predict = kernelEval.T * multiply(labelLSV, alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]): errorCount += 1
print("the test error rate is: %f" % (float(errorCount) / m))
def testRbf(k1=1.3):
#load data
dataArr, labelArr = smo.loadDataSet('testSetRBF.txt')
#training
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf',k1))
datMat = mat(dataArr)
labelMat = mat(labelArr).transpose()
#build support vector
svInd = nonzero(alphas.A > 0)[0]
sVs = datMat[svInd]
labelSV = labelMat[svInd]
print "there are %d Support Vectors" % shape(sVs)[0]
#training error rate
m,n = shape(datMat)
errorCount = 0
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)
#test error rate
dataArr, labelArr = smo.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)
def main():
print '-----------------------load data and training-----------'
dataArr, labelArr = smo.loadDataSet('testSet.txt')
b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
print "b", b
print "alphas > 0", alphas[alphas > 0]
#calculate weights
print '-----------------------calculate weights----------------'
ws = calcWs(alphas, dataArr, labelArr)
print "ws", ws
print '-----------------------test classify result----------------'
#process first node
datMat = mat(dataArr)
print "first node classify to: ", datMat[0] * mat(ws) + b
print "first node true label:", labelArr[0]
#process third node
print "third node classify to: ", datMat[2] * mat(ws) + b
print "third node true label: ", labelArr[2]
def main():
print '-----------------------load data and training-----------'
dataArr, labelArr = smo.loadDataSet('testSet.txt')
b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
print "b", b
print "alphas > 0", alphas[alphas > 0]
#calculate weights
print '-----------------------calculate weights----------------'
ws = calcWs(alphas, dataArr, labelArr)
print "ws", ws
print '-----------------------test classify result----------------'
#process first node
datMat = mat(dataArr)
print "first node classify to: ", datMat[0]*mat(ws) + b
print "first node true label:", labelArr[0]
#process third node
print "third node classify to: ", datMat[2]*mat(ws) + b
print "third node true label: ", labelArr[2]
