Python MKLMulticlass.set_epsilon示例

示例#1

def mkl(train_features,
        train_labels,
        test_features,
        test_labels,
        width=5,
        C=1.2,
        epsilon=1e-2,
        mkl_epsilon=0.001,
        mkl_norm=2):
    from modshogun import CombinedKernel, CombinedFeatures
    from modshogun import GaussianKernel, LinearKernel, PolyKernel
    from modshogun import MKLMulticlass, MulticlassAccuracy

    kernel = CombinedKernel()
    feats_train = CombinedFeatures()
    feats_test = CombinedFeatures()

    feats_train.append_feature_obj(train_features)
    feats_test.append_feature_obj(test_features)
    subkernel = GaussianKernel(10, width)
    kernel.append_kernel(subkernel)

    feats_train.append_feature_obj(train_features)
    feats_test.append_feature_obj(test_features)
    subkernel = LinearKernel()
    kernel.append_kernel(subkernel)

    feats_train.append_feature_obj(train_features)
    feats_test.append_feature_obj(test_features)
    subkernel = PolyKernel(10, 2)
    kernel.append_kernel(subkernel)

    kernel.init(feats_train, feats_train)
    mkl = MKLMulticlass(C, kernel, train_labels)

    mkl.set_epsilon(epsilon)
    mkl.set_mkl_epsilon(mkl_epsilon)
    mkl.set_mkl_norm(mkl_norm)

    mkl.train()
    train_output = mkl.apply()

    kernel.init(feats_train, feats_test)

    test_output = mkl.apply()
    evaluator = MulticlassAccuracy()
    print 'MKL training error is %.4f' % (
        (1 - evaluator.evaluate(train_output, train_labels)) * 100)
    print 'MKL test error is %.4f' % (
        (1 - evaluator.evaluate(test_output, test_labels)) * 100)

示例#2

文件： mkl_multiclass_modular.py 项目： manantomar/test

def mkl_multiclass_modular (fm_train_real, fm_test_real, label_train_multiclass,
	width, C, epsilon, num_threads, mkl_epsilon, mkl_norm):

	from modshogun import CombinedFeatures, RealFeatures, MulticlassLabels
	from modshogun import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel
	from modshogun import MKLMulticlass

	kernel = CombinedKernel()
	feats_train = CombinedFeatures()
	feats_test = CombinedFeatures()

	subkfeats_train = RealFeatures(fm_train_real)
	subkfeats_test = RealFeatures(fm_test_real)
	subkernel = GaussianKernel(10, width)
	feats_train.append_feature_obj(subkfeats_train)
	feats_test.append_feature_obj(subkfeats_test)
	kernel.append_kernel(subkernel)

	subkfeats_train = RealFeatures(fm_train_real)
	subkfeats_test = RealFeatures(fm_test_real)
	subkernel = LinearKernel()
	feats_train.append_feature_obj(subkfeats_train)
	feats_test.append_feature_obj(subkfeats_test)
	kernel.append_kernel(subkernel)

	subkfeats_train = RealFeatures(fm_train_real)
	subkfeats_test = RealFeatures(fm_test_real)
	subkernel = PolyKernel(10,2)
	feats_train.append_feature_obj(subkfeats_train)
	feats_test.append_feature_obj(subkfeats_test)
	kernel.append_kernel(subkernel)

	kernel.init(feats_train, feats_train)

	labels = MulticlassLabels(label_train_multiclass)

	mkl = MKLMulticlass(C, kernel, labels)

	mkl.set_epsilon(epsilon);
	mkl.parallel.set_num_threads(num_threads)
	mkl.set_mkl_epsilon(mkl_epsilon)
	mkl.set_mkl_norm(mkl_norm)

	mkl.train()

	kernel.init(feats_train, feats_test)

	out =  mkl.apply().get_labels()
	return out

示例#3

文件： multiclass_digits.py 项目： iglesias/tests

def mkl(train_features, train_labels, test_features, test_labels, width=5, C=1.2, epsilon=1e-2, mkl_epsilon=0.001, mkl_norm=2):
	from modshogun import CombinedKernel, CombinedFeatures
	from modshogun import GaussianKernel, LinearKernel, PolyKernel
	from modshogun import MKLMulticlass, MulticlassAccuracy

	kernel = CombinedKernel()
	feats_train = CombinedFeatures()
	feats_test = CombinedFeatures()

	feats_train.append_feature_obj(train_features)
	feats_test.append_feature_obj(test_features)
	subkernel = GaussianKernel(10,width)
	kernel.append_kernel(subkernel)

	feats_train.append_feature_obj(train_features)
	feats_test.append_feature_obj(test_features)
	subkernel = LinearKernel()
	kernel.append_kernel(subkernel)

	feats_train.append_feature_obj(train_features)
	feats_test.append_feature_obj(test_features)
	subkernel = PolyKernel(10,2)
	kernel.append_kernel(subkernel)

	kernel.init(feats_train, feats_train)
	mkl = MKLMulticlass(C, kernel, train_labels)

	mkl.set_epsilon(epsilon);
	mkl.set_mkl_epsilon(mkl_epsilon)
	mkl.set_mkl_norm(mkl_norm)

	mkl.train()
	train_output = mkl.apply()

	kernel.init(feats_train, feats_test)

	test_output = mkl.apply()
	evaluator = MulticlassAccuracy()
	print 'MKL training error is %.4f' % ((1-evaluator.evaluate(train_output, train_labels))*100)
	print 'MKL test error is %.4f' % ((1-evaluator.evaluate(test_output, test_labels))*100)