def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass,
width, C, epsilon, num_threads, mkl_epsilon,
mkl_norm):
from shogun.Features import CombinedFeatures, RealFeatures, Labels
from shogun.Kernel import CombinedKernel, GaussianKernel, LinearKernel, PolyKernel
from shogun.Classifier 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 = Labels(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
def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass,
width, C, epsilon, num_threads, mkl_epsilon,
mkl_norm):
from shogun.Features import CombinedFeatures, RealFeatures, MulticlassLabels
from shogun.Kernel import CombinedKernel, GaussianKernel, LinearKernel, PolyKernel
from shogun.Classifier 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
