Rename old HMSVM python examples to discrete and add new ones for PLiFs

examples/undocumented/python_modular/structure_hmsvm_bmrm.py → examples/undocumented/python_modular/structure_discrete_hmsvm_bmrm.py

@@ -8,7 +8,7 @@
 
 parameter_list=[[data_dict]]
 
-def structure_hmsvm_bmrm (m_data_dict=data_dict):
+def structure_discrete_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
@@ -24,19 +24,18 @@ def structure_hmsvm_bmrm (m_data_dict=data_dict):
 	features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500)
 
 	loss = HingeLoss()
-	model = HMSVMModel(features, labels, SMT_TWO_STATE, 4)
+	num_obs = 4	# given by the data file used
+	model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs)
 
 	sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0)
 	sosvm.train()
-
 	#print sosvm.get_w()
 
-	predicted = sosvm.apply()
+	predicted = sosvm.apply(features)
 	evaluator = StructuredAccuracy()
 	acc = evaluator.evaluate(predicted, labels)
-
 	#print('Accuracy = %.4f' % acc)
 
 if __name__ == '__main__':
-	print("HMSVM BMRM")
-	structure_hmsvm_bmrm(*parameter_list[0])
+	print("Discrete HMSVM BMRM")
+	structure_discrete_hmsvm_bmrm(*parameter_list[0])

examples/undocumented/python_modular/structure_hmsvm_mosek.py → examples/undocumented/python_modular/structure_discrete_hmsvm_mosek.py

@@ -8,7 +8,7 @@
 
 parameter_list=[[data_dict]]
 
-def structure_hmsvm_mosek (m_data_dict=data_dict):
+def structure_discrete_hmsvm_mosek (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
@@ -29,18 +29,18 @@ def structure_hmsvm_mosek (m_data_dict=data_dict):
 	features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500)
 
 	loss = HingeLoss()
-	model = HMSVMModel(features, labels, SMT_TWO_STATE, 4)
+	num_obs = 4	# given by the data file used
+	model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs)
 
 	sosvm = PrimalMosekSOSVM(model, loss, labels)
 	sosvm.train()
-	print(sosvm.get_w())
+	#print(sosvm.get_w())
 
 	predicted = sosvm.apply()
 	evaluator = StructuredAccuracy()
 	acc = evaluator.evaluate(predicted, labels)
-
-	print('Accuracy = %.4f' % acc)
+	#print('Accuracy = %.4f' % acc)
 
 if __name__ == '__main__':
-	print("HMSVM Mosek")
-	structure_hmsvm_mosek(*parameter_list[0])
+	print("Discrete HMSVM Mosek")
+	structure_discrete_hmsvm_mosek(*parameter_list[0])

examples/undocumented/python_modular/structure_plif_hmsvm_bmrm.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+
+parameter_list=[[100, 250, 10, 2]]
+
+def structure_plif_hmsvm_bmrm (num_examples, example_length, num_features, num_noise_features):
+	from shogun.Features   import RealMatrixFeatures
+	from shogun.Loss       import HingeLoss
+	from shogun.Structure  import TwoStateModel, DualLibQPBMSOSVM
+	from shogun.Evaluation import StructuredAccuracy
+
+	model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features)
+	loss = HingeLoss()
+	sosvm = DualLibQPBMSOSVM(model, loss, 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())
+	#print('Accuracy = %.4f' % acc)
+
+if __name__ == '__main__':
+	print("PLiF HMSVM BMRM")
+	structure_plif_hmsvm_bmrm(*parameter_list[0])

examples/undocumented/python_modular/structure_plif_hmsvm_mosek.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+
+parameter_list=[[100, 250, 10, 2]]
+
+def structure_plif_hmsvm_mosek (num_examples, example_length, num_features, num_noise_features):
+	from shogun.Features   import RealMatrixFeatures
+	from shogun.Loss       import HingeLoss
+	from shogun.Structure  import TwoStateModel
+	from shogun.Evaluation import StructuredAccuracy
+
+	try:
+		from shogun.Structure import PrimalMosekSOSVM
+	except ImportError:
+		print("Mosek not available")
+		return
+
+	model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features)
+	loss = HingeLoss()
+	sosvm = PrimalMosekSOSVM(model, loss, model.get_labels())
+
+	sosvm.train()
+	#print(sosvm.get_w())
+
+	predicted = sosvm.apply(model.get_features())
+	evaluator = StructuredAccuracy()
+	acc = evaluator.evaluate(predicted, model.get_labels())
+	#print('Accuracy = %.4f' % acc)
+
+if __name__ == '__main__':
+	print("PLiF HMSVM Mosek")
+	structure_plif_hmsvm_mosek(*parameter_list[0])