Function testRbf

src/python/6.SVM/svm-complete.py:371–401

(k1=1.3)

def testRbf(k1=1.3):
    dataArr, labelArr = loadDataSet('input/6.SVM/testSetRBF.txt')
    b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1))  # C=200 important
    datMat = mat(dataArr)
    labelMat = mat(labelArr).transpose()
    svInd = nonzero(alphas.A > 0)[0]
    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
    for i in range(m):
        kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))

        # 和这个svm-simple类似： fXi = float(multiply(alphas, labelMat).T*(dataMatrix*dataMatrix[i, :].T)) + b
        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 = loadDataSet('input/6.SVM/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 img2vector(filename):