def structure_hmsvm_bmrm(m_data_dict=data_dict):
from shogun.Features import RealMatrixFeatures
from shogun.Loss import HingeLoss
from shogun.Structure import HMSVMLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE
from shogun.Evaluation import StructuredAccuracy
from shogun.Structure import DualLibQPBMSOSVM
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = HMSVMLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250,
500)
loss = HingeLoss()
model = HMSVMModel(features, labels, SMT_TWO_STATE, 4)
sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0)
sosvm.train()
#print sosvm.get_w()
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
def structure_hmsvm_bmrm (m_data_dict=data_dict): from shogun.Features import RealMatrixFeatures from shogun.Loss import HingeLoss from shogun.Structure import SequenceLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE from shogun.Evaluation import StructuredAccuracy from shogun.Structure import DualLibQPBMSOSVM 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) loss = HingeLoss() model = HMSVMModel(features, labels, SMT_TWO_STATE, 4) sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply() evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, labels)
def structure_plif_hmsvm_bmrm(num_examples, example_length, num_features, num_noise_features):
from shogun.Features import RealMatrixFeatures
from shogun.Structure import TwoStateModel, DualLibQPBMSOSVM
from shogun.Evaluation import StructuredAccuracy
model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features)
sosvm = DualLibQPBMSOSVM(model, model.get_labels(), 5000.0)
sosvm.train()
# print sosvm.get_w()
predicted = sosvm.apply(model.get_features())
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, model.get_labels())
def structure_discrete_hmsvm_bmrm(m_data_dict=data_dict):
from shogun.Features import RealMatrixFeatures
from shogun.Structure import SequenceLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE
from shogun.Evaluation import StructuredAccuracy
from shogun.Structure import DualLibQPBMSOSVM
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 = DualLibQPBMSOSVM(model, labels, 5000.0)
sosvm.train()
# print sosvm.get_w()
predicted = sosvm.apply(features)
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
dim = 2
X, y = gen_data()
cnt = 250
X2, y2 = fill_data(cnt, np.min(X), np.max(X))
labels = MulticlassSOLabels(y)
features = RealFeatures(X.T)
model = MulticlassModel(features, labels)
loss = HingeLoss()
lambda_ = 1e1
sosvm = DualLibQPBMSOSVM(model, loss, labels, lambda_)
sosvm.set_cleanAfter(
10
) # number of iterations that cutting plane has to be inactive for to be removed
sosvm.set_cleanICP(True) # enables inactive cutting plane removal feature
sosvm.set_TolRel(0.001) # set relative tolerance
sosvm.set_verbose(True) # enables verbosity of the solver
sosvm.set_cp_models(16) # set number of cutting plane models
sosvm.set_solver(BMRM) # select training algorithm
#sosvm.set_solver(PPBMRM)
#sosvm.set_solver(P3BMRM)
sosvm.train()
res = sosvm.get_result()
# Dimension of the data dim = 2 X, y = gen_data() cnt = 250 X2, y2 = fill_data(cnt, np.min(X), np.max(X)) labels = MulticlassSOLabels(y) features = RealFeatures(X.T) model = MulticlassModel(features, labels) lambda_ = 1e1 sosvm = DualLibQPBMSOSVM(model, labels, lambda_) sosvm.set_cleanAfter(10) # number of iterations that cutting plane has to be inactive for to be removed sosvm.set_cleanICP(True) # enables inactive cutting plane removal feature sosvm.set_TolRel(0.001) # set relative tolerance sosvm.set_verbose(True) # enables verbosity of the solver sosvm.set_cp_models(16) # set number of cutting plane models sosvm.set_solver(BMRM) # select training algorithm #sosvm.set_solver(PPBMRM) #sosvm.set_solver(P3BMRM) sosvm.train() res = sosvm.get_result() Fps = np.array(res.get_hist_Fp_vector()) Fds = np.array(res.get_hist_Fp_vector())
#!/usr/bin/env python
import numpy
import scipy
from scipy import io
from shogun.Features import RealMatrixFeatures
from shogun.Loss import HingeLoss
from shogun.Structure import HMSVMLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE
from shogun.Evaluation import StructuredAccuracy
from shogun.Structure import DualLibQPBMSOSVM
data_dict = scipy.io.loadmat('../data/hmsvm_data_large_integer.mat')
labels_array = data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = HMSVMLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(data_dict['signal'].astype(float), 250, 500)
loss = HingeLoss()
model = HMSVMModel(features, labels, SMT_TWO_STATE, 4)
sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0)
sosvm.train()
print sosvm.get_w()
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
print('Accuracy = %.4f' % acc)
