Modified the probabilistic() trainer adapter (and the…

Modified the probabilistic() trainer adapter (and the train_probabilistic_decision_function() routine)
so that they work with objects which have an interface compatible with std::vector rather than strictly
just std::vector objects.  For example, random_subset_selector objects can be used (as was the case in
some previous versions of dlib).

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404211

dlib/svm/svm.h

@@ -470,19 +470,19 @@ namespace dlib
 
     template <
         typename trainer_type,
-        typename sample_type,
-        typename scalar_type,
-        typename alloc_type1,
-        typename alloc_type2
+        typename sample_vector_type,
+        typename label_vector_type
         >
     const probabilistic_function<typename trainer_type::trained_function_type> 
     train_probabilistic_decision_function (
         const trainer_type& trainer,
-        const std::vector<sample_type,alloc_type1>& x,
-        const std::vector<scalar_type,alloc_type2>& y,
+        const sample_vector_type& x,
+        const label_vector_type& y,
         const long folds
     )
     {
+        typedef typename sample_vector_type::value_type sample_type;
+        typedef typename label_vector_type::value_type scalar_type;
 
         /*
             This function fits a sigmoid function to the output of the 
@@ -519,14 +519,14 @@ namespace dlib
         const long num_neg_train_samples = num_neg - num_neg_test_samples; 
 
         typename trainer_type::trained_function_type d;
-        std::vector<sample_type,alloc_type1> x_test, x_train;
-        std::vector<scalar_type,alloc_type2> y_test, y_train;
+        std::vector<sample_type> x_test, x_train;
+        std::vector<scalar_type> y_test, y_train;
         x_test.resize (num_pos_test_samples  + num_neg_test_samples);
         y_test.resize (num_pos_test_samples  + num_neg_test_samples);
         x_train.resize(num_pos_train_samples + num_neg_train_samples);
         y_train.resize(num_pos_train_samples + num_neg_train_samples);
 
-        typedef std::vector<scalar_type, alloc_type2 > dvector;
+        typedef std::vector<scalar_type > dvector;
 
         dvector out;
         dvector target;
@@ -658,14 +658,12 @@ namespace dlib
         ) : trainer(trainer_),folds(folds_) {}
 
         template <
-            typename sample_type, 
-            typename scalar_type,
-            typename alloc_type1,
-            typename alloc_type2
+            typename T, 
+            typename U
             >
         const trained_function_type train (
-            const std::vector<sample_type,alloc_type1>& samples,
-            const std::vector<scalar_type,alloc_type2>& labels
+            const T& samples,
+            const U& labels
         ) const
         {
             return train_probabilistic_decision_function(trainer, samples, labels, folds);

dlib/svm/svm_abstract.h

@@ -68,22 +68,21 @@ namespace dlib
 
     template <
         typename trainer_type,
-        typename sample_type,
-        typename scalar_type,
-        typename alloc_type1,
-        typename alloc_type2
+        typename sample_vector_type,
+        typename label_vector_type
         >
     const probabilistic_function<typename trainer_type::trained_function_type> 
     train_probabilistic_decision_function (
         const trainer_type& trainer,
-        const std::vector<sample_type,alloc_type1>& x,
-        const std::vector<scalar_type,alloc_type2>& y,
+        const sample_vector_type& x,
+        const label_vector_type& y,
         const long folds
     );
     /*!
         requires
             - 1 < folds <= x.size()
             - is_binary_classification_problem(x,y) == true
+            - x and y must be std::vector objects or types with a compatible interface.
             - trainer_type == some kind of batch trainer object (e.g. svm_nu_trainer)
         ensures
             - trains a classifier given the training samples in x and labels in y.  

dlib/test/CMakeLists.txt

@@ -73,6 +73,7 @@ set (tests
    opt_qp_solver.cpp
    pipe.cpp
    pixel.cpp
+   probabilistic.cpp
    queue.cpp
    rand.cpp
    read_write_mutex.cpp

dlib/test/makefile

@@ -83,6 +83,7 @@ SRC += optimization_test_functions.cpp
 SRC += opt_qp_solver.cpp
 SRC += pipe.cpp
 SRC += pixel.cpp
+SRC += probabilistic.cpp
 SRC += queue.cpp
 SRC += rand.cpp
 SRC += read_write_mutex.cpp

dlib/test/probabilistic.cpp

+// Copyright (C) 2011  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+
+
+#include <dlib/matrix.h>
+#include <sstream>
+#include <string>
+#include <cstdlib>
+#include <ctime>
+#include <vector>
+#include "../stl_checked.h"
+#include "../array.h"
+#include "../rand.h"
+#include "checkerboard.h"
+#include <dlib/statistics.h>
+
+#include "tester.h"
+#include <dlib/svm_threaded.h>
+
+
+namespace  
+{
+
+    using namespace test;
+    using namespace dlib;
+    using namespace std;
+
+    logger dlog("test.probabilistic");
+
+// ----------------------------------------------------------------------------------------
+
+    class test_probabilistic : public tester
+    {
+    public:
+        test_probabilistic (
+        ) :
+            tester ("test_probabilistic",
+                    "Runs tests on the probabilistic trainer adapter.")
+        {}
+
+        void perform_test (
+        )
+        {
+            print_spinner();
+
+
+            typedef double scalar_type;
+            typedef matrix<scalar_type,2,1> sample_type;
+
+            std::vector<sample_type> x;
+            std::vector<matrix<double,0,1> > x_linearized;
+            std::vector<scalar_type> y;
+
+            get_checkerboard_problem(x,y, 1000, 2);
+
+            random_subset_selector<sample_type> rx;
+            random_subset_selector<scalar_type> ry;
+            rx.set_max_size(x.size());
+            ry.set_max_size(x.size());
+
+            dlog << LINFO << "pos labels: "<< sum(vector_to_matrix(y) == +1);
+            dlog << LINFO << "neg labels: "<< sum(vector_to_matrix(y) == -1);
+
+            for (unsigned long i = 0; i < x.size(); ++i)
+            {
+                rx.add(x[i]);
+                ry.add(y[i]);
+            }
+
+            const scalar_type gamma = 2.0;
+
+            typedef radial_basis_kernel<sample_type> kernel_type;
+
+            krr_trainer<kernel_type> krr_trainer;
+            krr_trainer.use_classification_loss_for_loo_cv();
+            krr_trainer.set_kernel(kernel_type(gamma));
+            krr_trainer.set_basis(randomly_subsample(x, 100));
+            probabilistic_decision_function<kernel_type> df;
+
+            dlog << LINFO << "cross validation: " << cross_validate_trainer(krr_trainer, rx,ry, 4);
+            print_spinner();
+
+            running_stats<scalar_type> rs_pos, rs_neg;
+
+            print_spinner();
+            df = probabilistic(krr_trainer,3).train(x, y);
+            for (unsigned long i = 0; i < x.size(); ++i)
+            {
+                if (y[i] > 0)
+                    rs_pos.add(df(x[i]));
+                else
+                    rs_neg.add(df(x[i]));
+            }
+            dlog << LINFO << "rs_pos.mean(): "<< rs_pos.mean();
+            dlog << LINFO << "rs_neg.mean(): "<< rs_neg.mean();
+            DLIB_TEST_MSG(rs_pos.mean() > 0.95, rs_pos.mean());
+            DLIB_TEST_MSG(rs_neg.mean() < 0.05, rs_neg.mean());
+            rs_pos.clear();
+            rs_neg.clear();
+
+
+            print_spinner();
+            df = probabilistic(krr_trainer,3).train(rx, ry);
+            for (unsigned long i = 0; i < x.size(); ++i)
+            {
+                if (y[i] > 0)
+                    rs_pos.add(df(x[i]));
+                else
+                    rs_neg.add(df(x[i]));
+            }
+            dlog << LINFO << "rs_pos.mean(): "<< rs_pos.mean();
+            dlog << LINFO << "rs_neg.mean(): "<< rs_neg.mean();
+            DLIB_TEST_MSG(rs_pos.mean() > 0.95, rs_pos.mean());
+            DLIB_TEST_MSG(rs_neg.mean() < 0.05, rs_neg.mean());
+            rs_pos.clear();
+            rs_neg.clear();
+
+        }
+    } a;
+
+}
+
+