def classifier_multiclassocas_modular(num_vec=10, num_class=3, distance=15, width=2.1, C=1, epsilon=1e-5, seed=1):
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import MulticlassOCAS
from shogun.Mathematics import Math_init_random
# reproducible results
random.seed(seed)
Math_init_random(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)
labels = MulticlassLabels(label_train)
classifier = MulticlassOCAS(C, feats_train, labels)
classifier.train()
out = classifier.apply(feats_test).get_labels()
# print label_test
# print out
return out, classifier
def classifier_multiclassocas_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.Classifier import MulticlassOCAS feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=Labels(label_train_multiclass) classifier = MulticlassOCAS(C,feats_train,labels) classifier.train() out = classifier.apply(feats_test).get_labels() return out
def classifier_multiclassocas_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.Classifier import MulticlassOCAS
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = MulticlassOCAS(C, feats_train, labels)
classifier.train()
out = classifier.apply(feats_test).get_labels()
return out
def classifier_multiclassocas_modular(num_vec=10,
num_class=3,
distance=15,
width=2.1,
C=1,
epsilon=1e-5,
seed=1):
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import MulticlassOCAS
from shogun.Mathematics import Math_init_random
# reproducible results
random.seed(seed)
Math_init_random(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)
labels = MulticlassLabels(label_train)
classifier = MulticlassOCAS(C, feats_train, labels)
classifier.train()
out = classifier.apply(feats_test).get_labels()
#print label_test
#print out
return out, classifier