Python CommWordStringKernel.set_normalizer 예제들

예제 #1

파일: signal_sensor.py 프로젝트: Anshul-Bansal/gsoc

    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)

예제 #2

파일: signal_sensor.py 프로젝트: nuoanunu/shogun-olaf

    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)

예제 #3

파일: tests_check_commwordkernel_memleak_modular.py 프로젝트: alesis/shogun

def tests_check_commwordkernel_memleak_modular(num, order, gap, reverse):
	import gc
	from shogun.Features import Alphabet,StringCharFeatures,StringWordFeatures,DNA
	from shogun.Preprocessor import SortWordString, MSG_DEBUG
	from shogun.Kernel 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 xrange(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_preproc(pre)
		trainudat.apply_preproc()
		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 #4

파일: tests_check_commwordkernel_memleak_modular.py 프로젝트: theoryno3/shogun

def tests_check_commwordkernel_memleak_modular (num, order, gap, reverse):
	import gc
	from shogun.Features import Alphabet,StringCharFeatures,StringWordFeatures,DNA
	from shogun.Preprocessor import SortWordString, MSG_DEBUG
	from shogun.Kernel 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 #5

파일: tests_check_commwordkernel_memleak_modular.py 프로젝트: memimo/shogun-liblinear

100*'ACGT',100*'ACGT', 100*'ACGT', 100*'ACGT',100*'ACGT', 100*'ACGT', 
100*'ACGT',100*'ACGT', 100*'ACGT', 100*'ACGT',100*'ACGT', 100*'ACGT', 
100*'ACGT',100*'ACGT', 100*'ACGT', 100*'ACGT',100*'ACGT', 100*'ACGT', 
100*'ACGT',100*'ACGT', 100*'ACGT', 100*'ACGT']
order=7
gap=0
reverse=False

for i in xrange(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_preproc(pre)
    trainudat.apply_preproc()
    spec = CommWordStringKernel(10, False)
    spec.set_normalizer(IdentityKernelNormalizer())
    spec.init(trainudat, trainudat)
    K=mat(spec.get_kernel_matrix())

del POS
del NEG
del order
del gap
del reverse