Python SparseRealFeatures.obtain_from_simple方法代码示例

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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()

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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()

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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()

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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())

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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()

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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()

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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

# 需要导入模块: from shogun.Features import SparseRealFeatures [as 别名]
# 或者: from shogun.Features.SparseRealFeatures import obtain_from_simple [as 别名]
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()