예제 #1
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def distance_manhattenword(train_fname=traindna,
                           test_fname=testdna,
                           order=3,
                           gap=0,
                           reverse=False):
    from shogun import StringCharFeatures, StringWordFeatures, DNA
    from shogun import SortWordString, ManhattanWordDistance, CSVFile

    charfeat = StringCharFeatures(CSVFile(train_fname), DNA)
    feats_train = StringWordFeatures(charfeat.get_alphabet())
    feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
    preproc = SortWordString()
    preproc.init(feats_train)
    feats_train.add_preprocessor(preproc)
    feats_train.apply_preprocessor()

    charfeat = StringCharFeatures(CSVFile(test_fname), DNA)
    feats_test = StringWordFeatures(charfeat.get_alphabet())
    feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)
    feats_test.add_preprocessor(preproc)
    feats_test.apply_preprocessor()

    distance = ManhattanWordDistance(feats_train, feats_train)

    dm_train = distance.get_distance_matrix()
    distance.init(feats_train, feats_test)
    dm_test = distance.get_distance_matrix()
    return dm_train, dm_test
예제 #2
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def distance_canberraword (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False):
	from shogun import StringCharFeatures, StringWordFeatures, DNA
	from shogun import SortWordString
	from shogun import CanberraWordDistance

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_train_dna)
	feats_train=StringWordFeatures(charfeat.get_alphabet())
	feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse)
	preproc=SortWordString()
	preproc.init(feats_train)
	feats_train.add_preprocessor(preproc)
	feats_train.apply_preprocessor()

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_test_dna)
	feats_test=StringWordFeatures(charfeat.get_alphabet())
	feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse)
	feats_test.add_preprocessor(preproc)
	feats_test.apply_preprocessor()

	distance=CanberraWordDistance(feats_train, feats_train)

	dm_train=distance.get_distance_matrix()
	distance.init(feats_train, feats_test)
	dm_test=distance.get_distance_matrix()
	return distance,dm_train,dm_test
def preprocessor_sortwordstring(fm_train_dna=traindna,
                                fm_test_dna=testdna,
                                order=3,
                                gap=0,
                                reverse=False,
                                use_sign=False):

    from shogun import CommWordStringKernel
    from shogun import StringCharFeatures, StringWordFeatures, DNA
    from shogun import SortWordString

    charfeat = StringCharFeatures(fm_train_dna, DNA)
    feats_train = StringWordFeatures(charfeat.get_alphabet())
    feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
    preproc = SortWordString()
    preproc.init(feats_train)
    feats_train.add_preprocessor(preproc)
    feats_train.apply_preprocessor()

    charfeat = StringCharFeatures(fm_test_dna, DNA)
    feats_test = StringWordFeatures(charfeat.get_alphabet())
    feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)
    feats_test.add_preprocessor(preproc)
    feats_test.apply_preprocessor()

    kernel = CommWordStringKernel(feats_train, feats_train, use_sign)

    km_train = kernel.get_kernel_matrix()
    kernel.init(feats_train, feats_test)
    km_test = kernel.get_kernel_matrix()

    return km_train, km_test, kernel
예제 #4
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    def init_sensor(self, kernel, svs):
        f = StringCharFeatures(svs, DNA)

        kname = kernel['name']
        if  kname == 'spectrum':
            wf = StringWordFeatures(f.get_alphabet())
            wf.obtain_from_char(f, kernel['order'] - 1, kernel['order'], 0, False)

            pre = SortWordString()
            pre.init(wf)
            wf.add_preprocessor(pre)
            wf.apply_preprocessor()
            f = wf

            k = CommWordStringKernel(0, False)
            k.set_use_dict_diagonal_optimization(kernel['order'] < 8)
            self.preproc = pre

        elif kname == 'wdshift':
                k = WeightedDegreePositionStringKernel(0, kernel['order'])
                k.set_normalizer(IdentityKernelNormalizer())
                k.set_shifts(kernel['shift'] *
                        numpy.ones(f.get_max_vector_length(), dtype=numpy.int32))
                k.set_position_weights(1.0 / f.get_max_vector_length() *
                        numpy.ones(f.get_max_vector_length(), dtype=numpy.float64))
        else:
            raise "Currently, only wdshift and spectrum kernels supported"

        self.kernel = k
        self.train_features = f

        return (self.kernel, self.train_features)
def kernel_weighted_comm_word_string (fm_train_dna=traindat,fm_test_dna=testdat,order=3,gap=0,reverse=True ):
	from shogun import WeightedCommWordStringKernel
	from shogun import StringWordFeatures, StringCharFeatures, DNA
	from shogun import SortWordString

	charfeat=StringCharFeatures(fm_train_dna, DNA)
	feats_train=StringWordFeatures(charfeat.get_alphabet())
	feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse)
	preproc=SortWordString()
	preproc.init(feats_train)
	feats_train.add_preprocessor(preproc)
	feats_train.apply_preprocessor()

	charfeat=StringCharFeatures(fm_test_dna, DNA)
	feats_test=StringWordFeatures(charfeat.get_alphabet())
	feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse)
	feats_test.add_preprocessor(preproc)
	feats_test.apply_preprocessor()

	use_sign=False
	kernel=WeightedCommWordStringKernel(feats_train, feats_train, use_sign)
	km_train=kernel.get_kernel_matrix()

	kernel.init(feats_train, feats_test)
	km_test=kernel.get_kernel_matrix()
	return km_train,km_test,kernel
예제 #6
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def distance_hammingword (fm_train_dna=traindna,fm_test_dna=testdna,
		fm_test_real=testdat,order=3,gap=0,reverse=False,use_sign=False):

	from shogun import StringCharFeatures, StringWordFeatures, DNA
	from shogun import SortWordString
	from shogun import HammingWordDistance

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_train_dna)
	feats_train=StringWordFeatures(charfeat.get_alphabet())
	feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse)
	preproc=SortWordString()
	preproc.init(feats_train)
	feats_train.add_preprocessor(preproc)
	feats_train.apply_preprocessor()

	charfeat=StringCharFeatures(DNA)
	charfeat.set_features(fm_test_dna)
	feats_test=StringWordFeatures(charfeat.get_alphabet())
	feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse)
	feats_test.add_preprocessor(preproc)
	feats_test.apply_preprocessor()

	distance=HammingWordDistance(feats_train, feats_train, use_sign)

	dm_train=distance.get_distance_matrix()
	distance.init(feats_train, feats_test)
	dm_test=distance.get_distance_matrix()
	return distance,dm_train,dm_test
def get_spectrum_features(data, order=3, gap=0, reverse=True):
    """
    create feature object used by spectrum kernel
    """

    charfeat = StringCharFeatures(data, DNA)
    feat = StringWordFeatures(charfeat.get_alphabet())
    feat.obtain_from_char(charfeat, order - 1, order, gap, reverse)
    preproc = SortWordString()
    preproc.init(feat)
    feat.add_preprocessor(preproc)
    feat.apply_preprocessor()

    return feat
예제 #8
0
def runShogunSVMDNASpectrumKernel(train_xt, train_lt, test_xt):
	"""
	run svm with spectrum kernel
	"""

    ##################################################
    # set up SVM
	charfeat_train = StringCharFeatures(train_xt, DNA)
	feats_train = StringWordFeatures(DNA)
	feats_train.obtain_from_char(charfeat_train, K-1, K, GAP, False)
	preproc=SortWordString()
	preproc.init(feats_train)
	feats_train.add_preprocessor(preproc)
	feats_train.apply_preprocessor()
	
	charfeat_test = StringCharFeatures(test_xt, DNA)
	feats_test=StringWordFeatures(DNA)
	feats_test.obtain_from_char(charfeat_test, K-1, K, GAP, False)
	feats_test.add_preprocessor(preproc)
	feats_test.apply_preprocessor()
	
	kernel=CommWordStringKernel(feats_train, feats_train, False)
	kernel.io.set_loglevel(MSG_DEBUG)

    # init kernel
	labels = BinaryLabels(train_lt)
	
	# run svm model
	print "Ready to train!"
	svm=LibSVM(SVMC, kernel, labels)
	svm.io.set_loglevel(MSG_DEBUG)
	svm.train()

	# predictions
	print "Making predictions!"
	out1DecisionValues = svm.apply(feats_train)
	out1=out1DecisionValues.get_labels()
	kernel.init(feats_train, feats_test)
	out2DecisionValues = svm.apply(feats_test)
	out2=out2DecisionValues.get_labels()

	return out1,out2,out1DecisionValues,out2DecisionValues
def distance_manhattenword (train_fname=traindna,test_fname=testdna,order=3,gap=0,reverse=False):
	from shogun import StringCharFeatures, StringWordFeatures, DNA
	from shogun import SortWordString, ManhattanWordDistance, CSVFile

	charfeat=StringCharFeatures(CSVFile(train_fname), DNA)
	feats_train=StringWordFeatures(charfeat.get_alphabet())
	feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse)
	preproc=SortWordString()
	preproc.init(feats_train)
	feats_train.add_preprocessor(preproc)
	feats_train.apply_preprocessor()

	charfeat=StringCharFeatures(CSVFile(test_fname), DNA)
	feats_test=StringWordFeatures(charfeat.get_alphabet())
	feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse)
	feats_test.add_preprocessor(preproc)
	feats_test.apply_preprocessor()

	distance=ManhattanWordDistance(feats_train, feats_train)

	dm_train=distance.get_distance_matrix()
	distance.init(feats_train, feats_test)
	dm_test=distance.get_distance_matrix()
	return dm_train,dm_test
def tests_check_commwordkernel_memleak (num, order, gap, reverse):
	import gc
	from shogun import Alphabet,StringCharFeatures,StringWordFeatures,DNA
	from shogun import SortWordString, MSG_DEBUG
	from shogun import CommWordStringKernel, IdentityKernelNormalizer
	from numpy import mat

	POS=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT',
	num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT',
	num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT',
	num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT']
	NEG=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT',
	num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT',
	num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT',
	num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT',
	num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT']

	for i in range(10):
		alpha=Alphabet(DNA)
		traindat=StringCharFeatures(alpha)
		traindat.set_features(POS+NEG)
		trainudat=StringWordFeatures(traindat.get_alphabet());
		trainudat.obtain_from_char(traindat, order-1, order, gap, reverse)
		#trainudat.io.set_loglevel(MSG_DEBUG)
		pre = SortWordString()
		#pre.io.set_loglevel(MSG_DEBUG)
		pre.init(trainudat)
		trainudat.add_preprocessor(pre)
		trainudat.apply_preprocessor()
		spec = CommWordStringKernel(10, False)
		spec.set_normalizer(IdentityKernelNormalizer())
		spec.init(trainudat, trainudat)
		K=spec.get_kernel_matrix()

	del POS
	del NEG
	del order
	del gap
	del reverse
	return K
예제 #11
0
def tests_check_commwordkernel_memleak(num, order, gap, reverse):
    import gc
    from shogun import Alphabet, StringCharFeatures, StringWordFeatures, DNA
    from shogun import SortWordString, MSG_DEBUG
    from shogun import CommWordStringKernel, IdentityKernelNormalizer
    from numpy import mat

    POS = [
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT'
    ]
    NEG = [
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
        num * 'TTGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
        num * 'ACGT'
    ]

    for i in range(10):
        alpha = Alphabet(DNA)
        traindat = StringCharFeatures(alpha)
        traindat.set_features(POS + NEG)
        trainudat = StringWordFeatures(traindat.get_alphabet())
        trainudat.obtain_from_char(traindat, order - 1, order, gap, reverse)
        #trainudat.io.set_loglevel(MSG_DEBUG)
        pre = SortWordString()
        #pre.io.set_loglevel(MSG_DEBUG)
        pre.init(trainudat)
        trainudat.add_preprocessor(pre)
        trainudat.apply_preprocessor()
        spec = CommWordStringKernel(10, False)
        spec.set_normalizer(IdentityKernelNormalizer())
        spec.init(trainudat, trainudat)
        K = spec.get_kernel_matrix()

    del POS
    del NEG
    del order
    del gap
    del reverse
    return K