Ejemplos de LibSVM.set_epsilon en Python

Ejemplo n.º 1

def libsvm():
    print 'LibSVM'

    from shogun.Features import RealFeatures, Labels
    from shogun.Kernel import GaussianKernel
    from shogun.Evaluation import PerformanceMeasures
    from shogun.Classifier import LibSVM

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)

    width = 2.1
    kernel = GaussianKernel(feats_train, feats_train, width)

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

    svm = LibSVM(C, kernel, labels)
    svm.set_epsilon(epsilon)
    svm.train()

    #kernel.init(feats_train, feats_test)
    output = svm.classify(feats_test)  #.get_labels()
    #output_vector = output.get_labels()
    out = svm.classify().get_labels()
    testerr = mean(sign(out) != testlab)
    print testerr

Ejemplo n.º 2

def classifier_multiclassmachine_modular(fm_train_real=traindat,
                                         fm_test_real=testdat,
                                         label_train_multiclass=label_traindat,
                                         width=2.1,
                                         C=1,
                                         epsilon=1e-5):
    from shogun.Features import RealFeatures, MulticlassLabels
    from shogun.Kernel import GaussianKernel
    from shogun.Classifier import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)
    kernel = GaussianKernel(feats_train, feats_train, width)

    labels = MulticlassLabels(label_train_multiclass)

    classifier = LibSVM()
    classifier.set_epsilon(epsilon)
    print labels.get_labels()
    mc_classifier = KernelMulticlassMachine(MulticlassOneVsRestStrategy(),
                                            kernel, classifier, labels)
    mc_classifier.train()

    kernel.init(feats_train, feats_test)
    out = mc_classifier.apply().get_labels()
    return out

Ejemplo n.º 3

Archivo: svm.py Proyecto: BrainTech/openbci

def libsvm ():
	print 'LibSVM'

	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Evaluation import PerformanceMeasures
	from shogun.Classifier import LibSVM

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)

	width=2.1
	kernel=GaussianKernel(feats_train, feats_train, width)

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

	svm=LibSVM(C, kernel, labels)
	svm.set_epsilon(epsilon)
	svm.train()

	#kernel.init(feats_train, feats_test)
	output = svm.classify(feats_test)#.get_labels()
        #output_vector = output.get_labels()
        out=svm.classify().get_labels()
        testerr=mean(sign(out)!=testlab)
        print testerr

Ejemplo n.º 4

def classifier_libsvm_modular(fm_train_real=traindat,
                              fm_test_real=testdat,
                              label_train_twoclass=label_traindat,
                              width=2.1,
                              C=1,
                              epsilon=1e-5):
    from shogun.Features import RealFeatures, Labels
    from shogun.Kernel import GaussianKernel
    from shogun.Classifier import LibSVM

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)

    kernel = GaussianKernel(feats_train, feats_train, width)
    labels = Labels(label_train_twoclass)

    svm = LibSVM(C, kernel, labels)
    svm.set_epsilon(epsilon)
    svm.train()

    kernel.init(feats_train, feats_test)
    labels = svm.classify().get_labels()
    supportvectors = sv_idx = svm.get_support_vectors()
    alphas = svm.get_alphas()
    predictions = svm.classify()
    return predictions, svm, predictions.get_labels()

Ejemplo n.º 5

Archivo: classifier_multiclassmachine_modular.py Proyecto: ashish-sadh/shogun

def classifier_multiclassmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVM, KernelMulticlassMachine, ONE_VS_REST_STRATEGY

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)
	kernel=GaussianKernel(feats_train, feats_train, width)

	labels=Labels(label_train_multiclass)

	classifier = LibSVM(C, kernel, labels)
	classifier.set_epsilon(epsilon)
	mc_classifier = KernelMulticlassMachine(ONE_VS_REST_STRATEGY,kernel,classifier,labels)
	mc_classifier.train()

	kernel.init(feats_train, feats_test)
	out = mc_classifier.apply().get_labels()
	return out

Ejemplo n.º 6

Archivo: classifier_multiclassmachine_modular.py Proyecto: lgatto/shogun

def classifier_multiclassmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
	from shogun.Features import RealFeatures, MulticlassLabels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)
	kernel=GaussianKernel(feats_train, feats_train, width)

	labels=MulticlassLabels(label_train_multiclass)

	classifier = LibSVM()
	classifier.set_epsilon(epsilon)
	#print labels.get_labels()
	mc_classifier = KernelMulticlassMachine(MulticlassOneVsRestStrategy(),kernel,classifier,labels)
	mc_classifier.train()

	kernel.init(feats_train, feats_test)
	out = mc_classifier.apply().get_labels()
	return out

Ejemplo n.º 7

Archivo: classifier_libsvm_modular.py Proyecto: harshitsyal/shogun

def classifier_libsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVM

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)
	
	kernel=GaussianKernel(feats_train, feats_train, width)
	labels=Labels(label_train_twoclass)

	svm=LibSVM(C, kernel, labels)
	svm.set_epsilon(epsilon)
	svm.train()

	kernel.init(feats_train, feats_test)
	labels = svm.apply().get_labels()
	supportvectors = sv_idx=svm.get_support_vectors()
	alphas=svm.get_alphas()
	predictions = svm.apply()
	return predictions, svm, predictions.get_labels()

Ejemplo n.º 8

Archivo: classifier_libsvm_modular.py Proyecto: memimo/shogun-liblinear

def libsvm ():
	print 'LibSVM'

	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVM

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)
	width=2.1
	kernel=GaussianKernel(feats_train, feats_train, width)

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

	svm=LibSVM(C, kernel, labels)
	svm.set_epsilon(epsilon)
	svm.train()

	kernel.init(feats_train, feats_test)
	svm.classify().get_labels()
	sv_idx=svm.get_support_vectors()
	alphas=svm.get_alphas()

Ejemplo n.º 9

Archivo: test_svm_kernel_multiple.py Proyecto: vinodrajendran001/ASP

from shogun.Classifier import LibSVM
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import LinearKernel

num_feats = 23
num_vec = 42

scale = 2.1
size_cache = 10

C = 0.017
epsilon = 1e-5
tube_epsilon = 1e-2
svm = LibSVM()
svm.set_C(C, C)
svm.set_epsilon(epsilon)
svm.set_tube_epsilon(tube_epsilon)

for i in xrange(3):
    data_train = random.rand(num_feats, num_vec)
    data_test = random.rand(num_feats, num_vec)
    feats_train = RealFeatures(data_train)
    feats_test = RealFeatures(data_test)
    labels = Labels(random.rand(num_vec).round() * 2 - 1)

    svm.set_kernel(LinearKernel(size_cache, scale))
    svm.set_labels(labels)

    kernel = svm.get_kernel()
    print "kernel cache size: %s" % (kernel.get_cache_size())

Ejemplo n.º 10

Archivo: test_svm_kernel_multiple.py Proyecto: 42MachineLearning/shogun

from shogun.Features import RealFeatures, Labels
from shogun.Kernel import LinearKernel


num_feats=23
num_vec=42

scale=2.1
size_cache=10

C=0.017
epsilon=1e-5
tube_epsilon=1e-2
svm=LibSVM()
svm.set_C(C, C)
svm.set_epsilon(epsilon)
svm.set_tube_epsilon(tube_epsilon)

for i in xrange(3):
	data_train=random.rand(num_feats, num_vec)
	data_test=random.rand(num_feats, num_vec)
	feats_train=RealFeatures(data_train)
	feats_test=RealFeatures(data_test)
	labels=Labels(random.rand(num_vec).round()*2-1)

	svm.set_kernel(LinearKernel(size_cache, scale))
	svm.set_labels(labels)

	kernel=svm.get_kernel()
	print "kernel cache size: %s" % (kernel.get_cache_size())