def structure_discrete_hmsvm_mosek (m_data_dict=data_dict):
from modshogun import RealMatrixFeatures
from modshogun import SequenceLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE
from modshogun import StructuredAccuracy
try:
from modshogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = SequenceLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500)
num_obs = 4 # given by the data file used
model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs)
sosvm = PrimalMosekSOSVM(model, labels)
sosvm.train()
#print(sosvm.get_w())
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
def structure_discrete_hmsvm_mosek (m_data_dict=data_dict):
from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
from modshogun import StructuredAccuracy, SMT_TWO_STATE
try:
from modshogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = SequenceLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500)
num_obs = 4 # given by the data file used
model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs)
sosvm = PrimalMosekSOSVM(model, labels)
sosvm.train()
#print(sosvm.get_w())
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
def so_multiclass(fm_train_real=traindat,
label_train_multiclass=label_traindat):
try:
from modshogun import RealFeatures
from modshogun import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
except ImportError:
print("Mosek not available")
return
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = PrimalMosekSOSVM(model, labels)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
print("Correct classification rate: %0.2f" %
(100.0 * count / out.get_num_labels()))
def structure_plif_hmsvm_mosek (num_examples, example_length, num_features, num_noise_features):
from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
from modshogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features)
sosvm = PrimalMosekSOSVM(model, model.get_labels())
sosvm.train()
#print(sosvm.get_w())
predicted = sosvm.apply(model.get_features())
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, model.get_labels())
def structure_plif_hmsvm_mosek(num_examples, example_length, num_features,
num_noise_features):
from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
from modshogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
model = TwoStateModel.simulate_data(num_examples, example_length,
num_features, num_noise_features)
sosvm = PrimalMosekSOSVM(model, model.get_labels())
sosvm.train()
#print(sosvm.get_w())
predicted = sosvm.apply(model.get_features())
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, model.get_labels())
def so_multiclass (fm_train_real=traindat,label_train_multiclass=label_traindat):
try:
from modshogun import RealFeatures
from modshogun import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
except ImportError:
print("Mosek not available")
return
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = PrimalMosekSOSVM(model, labels)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
print("Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() ))