new easy toy data data for example

examples/undocumented/python_modular/classifier_multiclassocas_modular.py

@@ -1,24 +1,37 @@
 #!/usr/bin/env python
-from tools.multiclass_shared import prepare_data
+from numpy import *
+parameter_list = [[10,3,15,2.1,1,1e-5,1],[20,4,15,2.2,2,1e-5,2]]
 
-[traindat, label_traindat, testdat, label_testdat] = prepare_data()
-
-parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
-
-def classifier_multiclassocas_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
+def classifier_multiclassocas_modular (num_vec=10,num_class=3,distance=15,width=2.1,C=1,epsilon=1e-5,seed=1):
 	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Classifier import MulticlassOCAS
+	from shogun.Mathematics import Math_init_random
+
+	# reproducible results
+	random.seed(seed)
+	Math_init_random(seed)
+
+	# generate some training data where each class pair is linearly separable
+	label_train=array([mod(x,num_class) for x in range(num_vec)],dtype="float64")
+	label_test=array([mod(x,num_class) for x in range(num_vec)],dtype="float64")
+	fm_train=array(random.randn(num_class,num_vec))
+	fm_test=array(random.randn(num_class,num_vec))
+	for i in range(len(label_train)):
+		fm_train[label_train[i],i]+=distance
+		fm_test[label_test[i],i]+=distance
 
-	feats_train=RealFeatures(fm_train_real)
-	feats_test=RealFeatures(fm_test_real)
+	feats_train=RealFeatures(fm_train)
+	feats_test=RealFeatures(fm_test)
 
-	labels=MulticlassLabels(label_train_multiclass)
+	labels=MulticlassLabels(label_train)
 
 	classifier = MulticlassOCAS(C,feats_train,labels)
 	classifier.train()
 
 	out = classifier.apply(feats_test).get_labels()
-	return out
+	#print label_test
+	#print out
+	return out,classifier
 
 if __name__=='__main__':
 	print('MulticlassOCAS')