Updated the example programs so that there isn't this confusing use of the

phase "support vectors" all over the place.  Also fixed them to compile now
that I renamed the support_vectors field in decision_function to basis_vectors.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403279

examples/kcentroid_ex.cpp

@@ -48,9 +48,9 @@ int main()
     // you need to set.  The first argument to the constructor is the kernel we wish to 
     // use.  The second is a parameter that determines the numerical accuracy with which 
     // the object will perform the centroid estimation.  Generally, smaller values 
-    // give better results but cause the algorithm to attempt to use more support vectors 
+    // give better results but cause the algorithm to attempt to use more dictionary vectors 
     // (and thus run slower and use more memory).  The third argument, however, is the 
-    // maximum number of support vectors a kcentroid is allowed to use.  So you can use
+    // maximum number of dictionary vectors a kcentroid is allowed to use.  So you can use
     // it to control the runtime complexity.  
     kcentroid<kernel_type> test(kernel_type(0.1),0.01, 15);
 

examples/kkmeans_ex.cpp

@@ -46,9 +46,9 @@ int main()
     // you need to set.  The first argument to the constructor is the kernel we wish to 
     // use.  The second is a parameter that determines the numerical accuracy with which 
     // the object will perform part of the learning algorithm.  Generally, smaller values 
-    // give better results but cause the algorithm to attempt to use more support vectors 
+    // give better results but cause the algorithm to attempt to use more dictionary vectors 
     // (and thus run slower and use more memory).  The third argument, however, is the 
-    // maximum number of support vectors a kcentroid is allowed to use.  So you can use
+    // maximum number of dictionary vectors a kcentroid is allowed to use.  So you can use
     // it to control the runtime complexity.  
     kcentroid<kernel_type> kc(kernel_type(0.1),0.01, 8);
 
@@ -133,13 +133,13 @@ int main()
         cout << test(samples[i+2*num]) << "\n";
     }
 
-    // Now print out how many support vectors each center used.  Note that 
+    // Now print out how many dictionary vectors each center used.  Note that 
     // the maximum number of 8 was reached.  If you went back to the kcentroid 
     // constructor and changed the 8 to some bigger number you would see that these
     // numbers would go up.  However, 8 is all we need to correctly cluster this dataset.
-    cout << "num sv for center 0: " << test.get_kcentroid(0).dictionary_size() << endl;
+    cout << "num dictionary vectors for center 0: " << test.get_kcentroid(0).dictionary_size() << endl;
-    cout << "num sv for center 1: " << test.get_kcentroid(1).dictionary_size() << endl;
+    cout << "num dictionary vectors for center 1: " << test.get_kcentroid(1).dictionary_size() << endl;
-    cout << "num sv for center 2: " << test.get_kcentroid(2).dictionary_size() << endl;
+    cout << "num dictionary vectors for center 2: " << test.get_kcentroid(2).dictionary_size() << endl;
 
 }
 

examples/rank_features_ex.cpp

@@ -108,9 +108,9 @@ int main()
     // you need to set.  The first argument to the constructor is the kernel we wish to 
     // use.  The second is a parameter that determines the numerical accuracy with which 
     // the object will perform part of the ranking algorithm.  Generally, smaller values 
-    // give better results but cause the algorithm to attempt to use more support vectors 
+    // give better results but cause the algorithm to attempt to use more dictionary vectors 
     // (and thus run slower and use more memory).  The third argument, however, is the 
-    // maximum number of support vectors a kcentroid is allowed to use.  So you can use
+    // maximum number of dictionary vectors a kcentroid is allowed to use.  So you can use
     // it to put an upper limit on the runtime complexity.  
     kcentroid<kernel_type> kc(kernel_type(gamma), 0.001, 25);
 

examples/rvm_ex.cpp

@@ -136,9 +136,9 @@ int main()
     learned_function.normalizer = normalizer;  // save normalization information
     learned_function.function = trainer.train(samples, labels); // perform the actual RVM training and save the results
 
-    // print out the number of support vectors in the resulting decision function
+    // print out the number of relevance vectors in the resulting decision function
-    cout << "\nnumber of support vectors in our learned_function is " 
+    cout << "\nnumber of relevance vectors in our learned_function is " 
-         << learned_function.function.support_vectors.nr() << endl;
+         << learned_function.function.basis_vectors.nr() << endl;
 
     // now lets try this decision_function on some samples we haven't seen before 
     sample_type sample;
@@ -171,10 +171,10 @@ int main()
     learned_pfunct.function = train_probabilistic_decision_function(trainer, samples, labels, 3);
     // Now we have a function that returns the probability that a given sample is of the +1 class.  
 
-    // print out the number of support vectors in the resulting decision function.  
+    // print out the number of relevance vectors in the resulting decision function.  
     // (it should be the same as in the one above)
-    cout << "\nnumber of support vectors in our learned_pfunct is " 
+    cout << "\nnumber of relevance vectors in our learned_pfunct is " 
-         << learned_pfunct.function.decision_funct.support_vectors.nr() << endl;
+         << learned_pfunct.function.decision_funct.basis_vectors.nr() << endl;
 
     sample(0) = 3.123;
     sample(1) = 2;

examples/svm_ex.cpp

@@ -144,7 +144,7 @@ int main()
 
     // print out the number of support vectors in the resulting decision function
     cout << "\nnumber of support vectors in our learned_function is " 
-         << learned_function.function.support_vectors.nr() << endl;
+         << learned_function.function.basis_vectors.nr() << endl;
 
     // now lets try this decision_function on some samples we haven't seen before 
     sample_type sample;
@@ -180,7 +180,7 @@ int main()
     // print out the number of support vectors in the resulting decision function.  
     // (it should be the same as in the one above)
     cout << "\nnumber of support vectors in our learned_pfunct is " 
-         << learned_pfunct.function.decision_funct.support_vectors.nr() << endl;
+         << learned_pfunct.function.decision_funct.basis_vectors.nr() << endl;
 
     sample(0) = 3.123;
     sample(1) = 2;