used a more sensible data set to actually see whether the method works

examples/undocumented/python_modular/classifier_larank_modular.py

@@ -1,35 +1,41 @@
 #!/usr/bin/env python
-from tools.load import LoadMatrix
-lm=LoadMatrix()
+from numpy import *
+parameter_list = [[10,3,15,0.9,1,2000,1],[20,4,15,0.9,1,5000,2]]
 
-traindat = lm.load_numbers('../data/fm_train_real.dat')
-testdat = lm.load_numbers('../data/fm_test_real.dat')
-label_traindat = lm.load_labels('../data/label_train_multiclass.dat')
-
-parameter_list = [[traindat,testdat,label_traindat,0.9,1,2000],[traindat,testdat,label_traindat,3,1,5000]]
-
-def classifier_larank_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,C=0.9,num_threads=1,num_iter=5):
+def classifier_larank_modular (num_vec,num_class,distance,C=0.9,num_threads=1,num_iter=5,seed=1):
 	from shogun.Features import RealFeatures, MulticlassLabels
 	from shogun.Kernel import GaussianKernel
 	from shogun.Classifier import LaRank
 	from shogun.Mathematics import Math_init_random
-	Math_init_random(17)
-
-	feats_train=RealFeatures(fm_train_real)
-	feats_test=RealFeatures(fm_test_real)
+
+	# reproducible results
+	Math_init_random(seed)
+	random.seed(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)
+	feats_test=RealFeatures(fm_test)
 	width=2.1
 	kernel=GaussianKernel(feats_train, feats_train, width)
 
 	epsilon=1e-5
-	labels=MulticlassLabels(label_train_multiclass)
+	labels=MulticlassLabels(label_train)
 
 	svm=LaRank(C, kernel, labels)
 	#svm.set_tau(1e-3)
 	svm.set_batch_mode(False)
 	#svm.io.enable_progress()
 	svm.set_epsilon(epsilon)
 	svm.train()
-	out=svm.apply(feats_train).get_labels()
+	out=svm.apply(feats_test).get_labels()
 	predictions = svm.apply()
 	return predictions, svm, predictions.get_labels()