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)
print('Accuracy = %.4f' % acc)
def structure_hmsvm_mosek(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
try:
from shogun.Structure import PrimalMosekSOSVM
except ImportError:
print "Mosek not available"
import sys
sys.exit(0)
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 = PrimalMosekSOSVM(model, loss, labels)
sosvm.train()
print sosvm.get_w()
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
print('Accuracy = %.4f' % acc)
Fds = np.array(res.hist_Fp)
wdists = np.array(res.hist_wdist)
plt.figure()
plt.subplot(221)
plt.title('Fp and Fd history')
plt.plot(xrange(res.nIter), Fps, hold=True)
plt.plot(xrange(res.nIter), Fds, hold=True)
plt.subplot(222)
plt.title('w dist history')
plt.plot(xrange(res.nIter), wdists)
# Evaluation
out = sosvm.apply()
Evaluation = StructuredAccuracy()
acc = Evaluation.evaluate(out, labels)
print "Correct classification rate: %0.4f%%" % (100.0 * acc)
# show figure
Z = get_so_labels(sosvm.apply(RealFeatures(X2)))
x = (X2[0, :]).reshape(cnt, cnt)
y = (X2[1, :]).reshape(cnt, cnt)
z = Z.reshape(cnt, cnt)
plt.subplot(223)
plt.pcolor(x, y, z, shading='interp')
plt.contour(x, y, z, linewidths=1, colors='black', hold=True)
plt.plot(X[:, 0], X[:, 1], 'yo')
plt.axis('tight')
