Ejemplos de SVMLin en Python

Ejemplo n.º 1

Archivo: classifier_svmlin_modular.py Proyecto: 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()

Ejemplo n.º 2

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, 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)

    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()
    predictions = svm.classify()
    return predictions, svm, predictions.get_labels()

Ejemplo n.º 3

Archivo: classifier_svmlin_modular.py Proyecto: 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()