Python SparseRealFeatures.obtain_from_simple Beispiele

Beispiel #1

def classify(true_labels):
    num_feats = 2
    num_vec = true_labels.get_num_labels()

    data_train = numpy.concatenate(
        (numpy.random.randn(num_feats, num_vec / 2) - 1,
         numpy.random.randn(num_feats, num_vec / 2) + 1),
        axis=1)
    realfeat = RealFeatures(data_train)
    feats_train = SparseRealFeatures()
    feats_train.obtain_from_simple(realfeat)
    C = 3.
    svm = SVMOcas(C, feats_train, true_labels)
    svm.train()

    data_test = numpy.concatenate(
        (numpy.random.randn(num_feats, num_vec / 2) - 1,
         numpy.random.randn(num_feats, num_vec / 2) + 1),
        axis=1)
    realfeat = RealFeatures(data_test)
    feats_test = SparseRealFeatures()
    feats_test.obtain_from_simple(realfeat)
    svm.set_features(feats_test)

    return numpy.array(svm.classify().get_labels())

Beispiel #2

Datei: classifier_svmlin_modular.py Projekt: polyactis/test

def svmlin ():
	print 'SVMLin'

	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
	from shogun.Classifier import SVMLin

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	C=0.9
	epsilon=1e-5
	num_threads=1
	labels=Labels(label_train_twoclass)

	svm=SVMLin(C, feats_train, labels)
	svm.set_epsilon(epsilon)
	svm.parallel.set_num_threads(num_threads)
	svm.set_bias_enabled(True)
	svm.train()

	svm.set_features(feats_test)
	svm.get_bias()
	svm.get_w()
	svm.classify().get_labels()

Beispiel #3

Datei: classifier_svmlin_modular.py Projekt: AlexBinder/shogun

def classifier_svmlin_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,C=0.9,epsilon=1e-5,num_threads=1):
	from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
	from shogun.Classifier import SVMLin

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	labels=BinaryLabels(label_train_twoclass)

	svm=SVMLin(C, feats_train, labels)
	svm.set_epsilon(epsilon)
	svm.parallel.set_num_threads(num_threads)
	svm.set_bias_enabled(True)
	svm.train()

	svm.set_features(feats_test)
	svm.get_bias()
	svm.get_w()
	svm.apply().get_labels()
	predictions = svm.apply()
	return predictions, svm, predictions.get_labels()

Beispiel #4

def classifier_svmsgd_modular(fm_train_real=traindat,
                              fm_test_real=testdat,
                              label_train_twoclass=label_traindat,
                              C=0.9,
                              num_threads=1,
                              num_iter=5):

    from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
    from shogun.Classifier import SVMSGD

    realfeat = RealFeatures(fm_train_real)
    feats_train = SparseRealFeatures()
    feats_train.obtain_from_simple(realfeat)
    realfeat = RealFeatures(fm_test_real)
    feats_test = SparseRealFeatures()
    feats_test.obtain_from_simple(realfeat)

    labels = BinaryLabels(label_train_twoclass)

    svm = SVMSGD(C, feats_train, labels)
    svm.set_epochs(num_iter)
    #svm.io.set_loglevel(0)
    svm.train()

    svm.set_features(feats_test)
    svm.apply().get_labels()
    predictions = svm.apply()
    return predictions, svm, predictions.get_labels()

Beispiel #5

Datei: util.py Projekt: TharinduRusira/shogun

def compute_output_plot_isolines(classifier, kernel=None, train=None, sparse=False, pos=None, neg=None, regression=False):
	size=100
	if pos is not None and neg is not None:
		x1_max=max(1.2*pos[0,:])
		x1_min=min(1.2*neg[0,:])
		x2_min=min(1.2*neg[1,:])
		x2_max=max(1.2*pos[1,:])
		x1=linspace(x1_min, x1_max, size)
		x2=linspace(x2_min, x2_max, size)
	else:
		x1=linspace(-5, 5, size)
		x2=linspace(-5, 5, size)

	x, y=meshgrid(x1, x2)

	dense=RealFeatures(array((ravel(x), ravel(y))))
	if sparse:
		test=SparseRealFeatures()
		test.obtain_from_simple(dense)
	else:
		test=dense

	if kernel and train:
		kernel.init(train, test)
	else:
		classifier.set_features(test)

	labels = None
	if regression:
		labels=classifier.apply().get_labels()
	else:
		labels=classifier.apply().get_confidences()
	z=labels.reshape((size, size))

	return x, y, z

Beispiel #6

def compute_output_plot_isolines(classifier, kernel=None, train=None, sparse=False, pos=None, neg=None, regression=False):
	size=100
	if pos is not None and neg is not None:
		x1_max=max(1.2*pos[0,:])
		x1_min=min(1.2*neg[0,:])
		x2_min=min(1.2*neg[1,:])
		x2_max=max(1.2*pos[1,:])
		x1=linspace(x1_min, x1_max, size)
		x2=linspace(x2_min, x2_max, size)
	else:
		x1=linspace(-5, 5, size)
		x2=linspace(-5, 5, size)

	x, y=meshgrid(x1, x2)

	dense=RealFeatures(array((ravel(x), ravel(y))))
	if sparse:
		test=SparseRealFeatures()
		test.obtain_from_simple(dense)
	else:
		test=dense

	if kernel and train:
		kernel.init(train, test)
	else:
		classifier.set_features(test)

	labels = None
	if regression:
		labels=classifier.apply().get_labels()
	else:
		labels=classifier.apply().get_values()
	z=labels.reshape((size, size))

	return x, y, z

Beispiel #7

Datei: classifier_subgradientsvm_modular.py Projekt: polyactis/test

def subgradient_svm ():
	print 'SubGradientSVM'

	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
	from shogun.Classifier import SubGradientSVM

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	C=0.9
	epsilon=1e-3
	num_threads=1
	max_train_time=1.
	labels=Labels(label_train_twoclass)

	svm=SubGradientSVM(C, feats_train, labels)
	svm.set_epsilon(epsilon)
	svm.parallel.set_num_threads(num_threads)
	svm.set_bias_enabled(False)
	svm.set_max_train_time(max_train_time)
	svm.train()

	svm.set_features(feats_test)
	svm.classify().get_labels()

Beispiel #8

def classifier_subgradientsvm_modular(fm_train_real, fm_test_real,
                                      label_train_twoclass, C, epsilon,
                                      max_train_time):

    from shogun.Features import RealFeatures, SparseRealFeatures, Labels
    from shogun.Classifier import SubGradientSVM

    realfeat = RealFeatures(fm_train_real)
    feats_train = SparseRealFeatures()
    feats_train.obtain_from_simple(realfeat)
    realfeat = RealFeatures(fm_test_real)
    feats_test = SparseRealFeatures()
    feats_test.obtain_from_simple(realfeat)

    labels = Labels(label_train_twoclass)

    svm = SubGradientSVM(C, feats_train, labels)
    svm.set_epsilon(epsilon)
    svm.set_max_train_time(max_train_time)
    svm.train()

    svm.set_features(feats_test)
    labels = svm.apply().get_labels()

    return labels, svm

Beispiel #9

Datei: all_evaluation.py Projekt: AsherBond/shogun

def classify (true_labels):
	num_feats=2
	num_vec=true_labels.get_num_labels()

	data_train=numpy.concatenate(
		(numpy.random.randn(num_feats, num_vec/2)-1,
			numpy.random.randn(num_feats, num_vec/2)+1),
		axis=1)
	realfeat=RealFeatures(data_train)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	C=3.
	svm=SVMOcas(C, feats_train, true_labels)
	svm.train()

	data_test=numpy.concatenate(
		(numpy.random.randn(num_feats, num_vec/2)-1,
			numpy.random.randn(num_feats, num_vec/2)+1),
		axis=1)
	realfeat=RealFeatures(data_test)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)
	svm.set_features(feats_test)

	return numpy.array(svm.classify().get_labels())

Beispiel #10

Datei: classifier_svmocas_modular.py Projekt: behollis/muViewBranch

def classifier_svmocas_modular(fm_train_real=traindat,
                               fm_test_real=testdat,
                               label_train_twoclass=label_traindat,
                               C=0.9,
                               epsilon=1e-5,
                               num_threads=1):

    from shogun.Features import RealFeatures, SparseRealFeatures, BinaryLabels
    from shogun.Classifier import SVMOcas

    realfeat = RealFeatures(fm_train_real)
    feats_train = SparseRealFeatures()
    feats_train.obtain_from_simple(realfeat)
    realfeat = RealFeatures(fm_test_real)
    feats_test = SparseRealFeatures()
    feats_test.obtain_from_simple(realfeat)

    labels = BinaryLabels(label_train_twoclass)

    svm = SVMOcas(C, feats_train, labels)
    svm.set_epsilon(epsilon)
    svm.parallel.set_num_threads(num_threads)
    svm.set_bias_enabled(False)
    svm.train()

    svm.set_features(feats_test)
    svm.apply().get_labels()
    predictions = svm.apply()
    return predictions, svm, predictions.get_labels()

Beispiel #11

Datei: classifier_svmsgd_modular.py Projekt: polyactis/test

def svmsgd ():
	print 'SVMSGD'

	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
	from shogun.Classifier import SVMSGD

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	C=0.9	
	num_threads=1
	num_iter=5
	labels=Labels(label_train_twoclass)

	svm=SVMSGD(C, feats_train, labels)
	svm.set_epochs(num_iter)
	#svm.io.set_loglevel(0)
	svm.train()
	
	
	svm.set_features(feats_test)
	labelPrediction = svm.classify().get_labels()
	print labelPrediction>0

Beispiel #12

Datei: distance_sparseeuclidean_modular.py Projekt: behollis/muViewBranch

def distance_sparseeuclidean_modular (fm_train_real=traindat,fm_test_real=testdat):
	from shogun.Features import RealFeatures, SparseRealFeatures
	from shogun.Distance import SparseEuclidianDistance

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	distance=SparseEuclidianDistance(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

Beispiel #13

def distance_sparseeuclidean_modular (fm_train_real=traindat,fm_test_real=testdat):
	from shogun.Features import RealFeatures, SparseRealFeatures
	from shogun.Distance import SparseEuclidianDistance

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	distance=SparseEuclidianDistance(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

Beispiel #14

Datei: distance_sparseeuclidean_modular.py Projekt: memimo/shogun-liblinear

def sparse_euclidian_distance ():
	print 'SparseEuclidianDistance'

	from shogun.Features import RealFeatures, SparseRealFeatures
	from shogun.Distance import SparseEuclidianDistance

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	distance=SparseEuclidianDistance(feats_train, feats_train)

	dm_train=distance.get_distance_matrix()
	distance.init(feats_train, feats_test)
	dm_test=distance.get_distance_matrix()

Beispiel #15

Datei: classifier_svmsgd_modular.py Projekt: Anshul-Bansal/gsoc

def classifier_svmsgd_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,C=0.9,num_threads=1,num_iter=5):

	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
	from shogun.Classifier import SVMSGD

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	labels=Labels(label_train_twoclass)

	svm=SVMSGD(C, feats_train, labels)
	svm.set_epochs(num_iter)
	#svm.io.set_loglevel(0)
	svm.train()

	svm.set_features(feats_test)
	svm.apply().get_labels()
	predictions = svm.apply()
	return predictions, svm, predictions.get_labels()

Beispiel #16

Datei: classifier_subgradientsvm_modular.py Projekt: Anshul-Bansal/gsoc

def classifier_subgradientsvm_modular(fm_train_real, fm_test_real, label_train_twoclass, C, epsilon, max_train_time):

    from shogun.Features import RealFeatures, SparseRealFeatures, Labels
    from shogun.Classifier import SubGradientSVM

    realfeat = RealFeatures(fm_train_real)
    feats_train = SparseRealFeatures()
    feats_train.obtain_from_simple(realfeat)
    realfeat = RealFeatures(fm_test_real)
    feats_test = SparseRealFeatures()
    feats_test.obtain_from_simple(realfeat)

    labels = Labels(label_train_twoclass)

    svm = SubGradientSVM(C, feats_train, labels)
    svm.set_epsilon(epsilon)
    svm.set_max_train_time(max_train_time)
    svm.train()

    svm.set_features(feats_test)
    labels = svm.apply().get_labels()

    return labels, svm

Beispiel #17

Datei: classifier_svmsgd_modular.py Projekt: memimo/shogun-liblinear

def svmsgd ():
	print 'SVMSGD'

	from shogun.Features import RealFeatures, SparseRealFeatures, Labels
	from shogun.Classifier import SVMSGD

	realfeat=RealFeatures(fm_train_real)
	feats_train=SparseRealFeatures()
	feats_train.obtain_from_simple(realfeat)
	realfeat=RealFeatures(fm_test_real)
	feats_test=SparseRealFeatures()
	feats_test.obtain_from_simple(realfeat)

	C=0.9
	epsilon=1e-5
	num_threads=1
	labels=Labels(label_train_twoclass)

	svm=SVMSGD(C, feats_train, labels)
	#svm.io.set_loglevel(0)
	svm.train()

	svm.set_features(feats_test)
	svm.classify().get_labels()