def classifier_larank_modular(fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
C=0.9,
num_threads=1,
num_iter=5):
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import GaussianKernel
from shogun.Classifier import LaRank
from shogun.Mathematics import Math_init_random
Math_init_random(17)
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
width = 2.1
kernel = GaussianKernel(feats_train, feats_train, width)
epsilon = 1e-5
labels = Labels(label_train_multiclass)
svm = LaRank(C, kernel, labels)
#svm.set_tau(1e-3)
svm.set_batch_mode(False)
#svm.io.enable_progress()
svm.set_epsilon(epsilon)
svm.train()
out = svm.apply(feats_train).get_labels()
predictions = svm.apply()
return predictions, svm, predictions.get_labels()
def classifier_larank_modular (num_vec,num_class,distance,C=0.9,num_threads=1,num_iter=5,seed=1): from shogun.Features import RealFeatures, MulticlassLabels from shogun.Kernel import GaussianKernel from shogun.Classifier import LaRank from shogun.Mathematics import Math_init_random # reproducible results Math_init_random(seed) random.seed(seed) # generate some training data where each class pair is linearly separable label_train=array([mod(x,num_class) for x in range(num_vec)],dtype="float64") label_test=array([mod(x,num_class) for x in range(num_vec)],dtype="float64") fm_train=array(random.randn(num_class,num_vec)) fm_test=array(random.randn(num_class,num_vec)) for i in range(len(label_train)): fm_train[label_train[i],i]+=distance fm_test[label_test[i],i]+=distance feats_train=RealFeatures(fm_train) feats_test=RealFeatures(fm_test) width=2.1 kernel=GaussianKernel(feats_train, feats_train, width) epsilon=1e-5 labels=MulticlassLabels(label_train) svm=LaRank(C, kernel, labels) #svm.set_tau(1e-3) svm.set_batch_mode(False) #svm.io.enable_progress() svm.set_epsilon(epsilon) svm.train() out=svm.apply(feats_test).get_labels() predictions = svm.apply() return predictions, svm, predictions.get_labels()