def classifier_multiclasslogisticregression_modular(
fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
z=1,
epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels
try:
from modshogun import MulticlassLogisticRegression
except ImportError:
print("recompile shogun with Eigen3 support")
return
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = MulticlassLogisticRegression(z, feats_train, labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from modshogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_multiclasslogisticregression_modular(
fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
z=1,
epsilon=1e-5,
):
from modshogun import RealFeatures, MulticlassLabels
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = MulticlassLogisticRegression(z, feats_train, labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from modshogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print("Accuracy = %.4f" % acc)
return out
def BuildModel(self, data, responses):
# Create and train the classifier.
model = MulticlassLogisticRegression(self.z, RealFeatures(data.T),
MulticlassLabels(responses))
if self.max_iter is not None:
model.set_max_iter(self.max_iter);
model.train()
return model
def BuildModel(self, data, responses):
# Create and train the classifier.
model = MulticlassLogisticRegression(self.z, RealFeatures(data.T),
MulticlassLabels(responses))
model.train()
return model
def BuildModel(self, data, responses):
# Create and train the classifier.
model = MulticlassLogisticRegression(self.z, RealFeatures(data.T),
MulticlassLabels(responses))
model.train()
return model
