# read the inital kernel matrix

    kernelLinesTR = readMatrix( baseKernelNameTR )
    #kernelLinesTR = np.loadtxt( baseKernelNameTR )
    print kernelLinesTR.shape

    #kernelLinesTR0 = readMatrix( os.path.join(kernelPath, 'trainval.jpg.2SsalTotal0.sift.cb.all.trainval.jpg.2SsalTotal
0.sift.cb.all.histInt') )
    #print kernelLinesTR0.shape

    #kernelLinesTR1 = readMatrix(os.path.join(kernelPath, 'trainval.jpg.2SsalTotal1.sift.cb.all.trainval.jpg.2SsalTotal1
.sift.cb.all.histInt') )
    #print kernelLinesTR1.shape

    kernelLinesTE= readMatrix(baseKernelNameTE).T
    print kernelLinesTE.shape

    #kernelLinesTE0 = readMatrix(os.path.join(kernelPath, 'trainval.jpg.2SsalTotal0.sift.cb.all.test.jpg.2SsalTotal0.sif
t.cb.all.histInt') )
    #print kernelLinesTE0.shape

    #kernelLinesTE1 = readMatrix(os.path.join(kernelPath, 'trainval.jpg.2SsalTotal1.sift.cb.all.test.jpg.2SsalTotal1.sif
t.cb.all.histInt') )
    #print kernelLinesTE1.shape

    kernelTR = CombinedKernel()
    kernelTR.append_kernel(CustomKernel(kernelLinesTR))
    #kernelTR.append_kernel(CustomKernel(kernelLinesTR0))
    #kernelTR.append_kernel(CustomKernel(kernelLinesTR1))

    # create combined test features
    kernelTE = CombinedKernel()
    kernelTE.append_kernel(CustomKernel(kernelLinesTE))
    #kernelTE.append_kernel(CustomKernel(kernelLinesTE0))
    #kernelTE.append_kernel(CustomKernel(kernelLinesTE1))


    # get the classes
    indexFileExt = '*_' + featFileTR
    indexFiles = glob.glob( os.path.join(indexFilePath, indexFileExt) )
    print indexFilePath, indexFileExt
    print indexFiles
    for f in indexFiles:
      print 'indexFileTR:', f
      for i in range(5):
        print i
        fTE = f.replace(featFileTR, featFileTE)
        #print 'indexFileTE:', fTE

        # train file
        indexFile = open(f, 'r')
        indexFilelinesTR = indexFile.read().splitlines()
        indexFile.close()
        # create labels,
        labelsTR = np.array([ float(d.split()[1]) for d in indexFilelinesTR])
        labelsTR[labelsTR==0] = -1
        labelsTR = BinaryLabels(labelsTR)

        # test file
        indexFile = open(fTE, 'r')
        indexFilelinesTE = indexFile.read().splitlines()
        indexFile.close()
        # create labels,
        labelsTE = np.array([ float(d.split()[1]) for d in indexFilelinesTE])
        labelsTE[labelsTE==0] = -1
        labelsTE = BinaryLabels(labelsTE)

        # train mkl
        mkl = MKLClassification()

        mkl.io.set_loglevel(MSG_DEBUG)
        # which norm to use for MKL
        mkl.set_mkl_norm(1) #1, 2,3

        # set cost (neg, pos)
        mkl.set_C(1, 1)
        mkl.set_epsilon(1e-5)

        # set kernel and labels
        mkl.set_kernel(kernelTR)
        mkl.set_labels(labelsTR)

        # train
        mkl.train()
        w=kernelTR.get_subkernel_weights()
        print 'kernel Weights',  w
        # test
        # and classify
        mkl.set_kernel(kernelTE)
        print 'apply'
        mkl.apply()
        print 'getLabels'
        predLabels = mkl.apply().get_labels()
        print predLabels