1 files changed, 0 insertions, 165 deletions
diff --git a/ml/dlib/dlib/test/active_learning.cpp b/ml/dlib/dlib/test/active_learning.cpp
deleted file mode 100644
index 9dc0013a5..000000000
--- a/ml/dlib/dlib/test/active_learning.cpp
+++ /dev/null
@@ -1,165 +0,0 @@
-// Copyright (C) 2012  Davis E. King (davis@dlib.net)
-// License: Boost Software License   See LICENSE.txt for the full license.
-
-
-#include <dlib/svm.h>
-
-#include "tester.h"
-
-
-namespace  
-{
-
-    using namespace test;
-    using namespace dlib;
-    using namespace std;
-
-    logger dlog("test.active_learning");
-
-// ----------------------------------------------------------------------------------------
-
-    typedef matrix<double, 0, 1> sample_type;
-    typedef radial_basis_kernel<sample_type> kernel_type;
-
-// ----------------------------------------------------------------------------------------
-
-    void make_dataset (
-        std::vector<sample_type>& samples,
-        std::vector<double>& labels
-    )
-    {
-        for (int r = -10; r <= 10; ++r)
-        {
-            for (int c = -10; c <= 10; ++c)
-            {
-                sample_type samp(2);
-                samp(0) = r;
-                samp(1) = c;
-                samples.push_back(samp);
-
-                // if this point is less than 10 from the origin
-                if (sqrt((double)r*r + c*c) <= 8)
-                    labels.push_back(+1);
-                else
-                    labels.push_back(-1);
-
-            }
-        }
-
-
-        vector_normalizer<sample_type> normalizer;
-        normalizer.train(samples);
-        for (unsigned long i = 0; i < samples.size(); ++i)
-            samples[i] = normalizer(samples[i]); 
-
-        randomize_samples(samples, labels);
-
-        /*
-        cout << "samples.size(): " << samples.size() << endl;
-        cout << "num +1 samples: "<< sum(mat(labels) > 0) << endl;
-        cout << "num -1 samples: "<< sum(mat(labels) < 0) << endl;
-        */
-
-        empirical_kernel_map<kernel_type> ekm;
-        ekm.load(kernel_type(0.15), samples);
-        for (unsigned long i = 0; i < samples.size(); ++i)
-            samples[i] = ekm.project(samples[i]);
-
-        //cout << "dims: "<< ekm.out_vector_size() << endl;
-    }
-
-// ----------------------------------------------------------------------------------------
-
-    double test_rank_unlabeled_training_samples (
-        const std::vector<sample_type>& samples,
-        const std::vector<double>& labels,
-        active_learning_mode mode,
-        int iterations,
-        bool pick_front
-    )
-    {
-        matrix<double,2,1> s;
-        s = sum(mat(labels) > 0), sum(mat(labels) < 0);
-        s /= labels.size();
-
-
-        svm_c_linear_dcd_trainer<linear_kernel<sample_type> > trainer;
-        trainer.set_c(25);
-
-        const unsigned long initial_size = 1;
-        std::vector<sample_type> tsamples(samples.begin(), samples.begin()+initial_size); 
-        std::vector<double> tlabels(labels.begin(), labels.begin()+initial_size); 
-
-        decision_function<linear_kernel<sample_type> > df;
-
-        double random_score = 0;
-        double active_learning_score = 0;
-        for (int i = 0; i < iterations; ++i)
-        {
-            print_spinner();
-            random_subset_selector<sample_type> sss = randomly_subsample(samples,50,i);
-            random_subset_selector<double> ssl = randomly_subsample(labels,50,i);
-            std::vector<unsigned long> results;
-
-            results = rank_unlabeled_training_samples(trainer, tsamples, tlabels, sss, mode);
-
-            const unsigned long idx = pick_front ? results.front() : results.back();
-            tsamples.push_back(sss[idx]);
-            tlabels.push_back(ssl[idx]);
-
-            df = trainer.train(tsamples, tlabels);
-            //cout << "tsamples.size(): " << tsamples.size() << endl;
-            const unsigned long num = tsamples.size();
-            const double active = test_binary_decision_function(df, samples, labels)*s;
-            //cout << "test: "<< active;
-            df = trainer.train(randomly_subsample(samples,num,i), randomly_subsample(labels,num,i));
-            const double random = test_binary_decision_function(df, samples, labels)*s;
-            //cout << "test: "<< random << endl;
-
-            active_learning_score += active;
-            random_score += random;
-
-            //cout << "\n\n***********\n\n" << flush;
-        }
-
-        dlog << LINFO << "pick_front: " << pick_front << "   mode: "<< mode;
-        dlog << LINFO << "active_learning_score: "<< active_learning_score;
-        dlog << LINFO << "random_score:          "<< random_score;
-        return active_learning_score / random_score;
-    }
-
-// ----------------------------------------------------------------------------------------
-
-    class test_active_learning : public tester
-    {
-    public:
-        test_active_learning (
-        ) :
-            tester ("test_active_learning",
-                "Runs tests on the active learning components.")
-        {}
-
-        void perform_test (
-        )
-        {
-            std::vector<sample_type> samples;
-            std::vector<double> labels;
-            print_spinner();
-            make_dataset(samples, labels);
-            dlog << LINFO << "samples.size(): "<< samples.size();
-
-            // When we pick the best/front ranked element then the active learning method
-            // shouldn't do much worse than random selection (and often much better).
-            DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, max_min_margin, 35, true) >= 0.97);
-            DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, ratio_margin, 25, true) >= 0.96);
-            // However, picking the worst ranked element should do way worse than random
-            // selection.
-            DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, max_min_margin, 25, false) < 0.8);
-            DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, ratio_margin, 25, false) < 0.8);
-        }
-    } a;
-
-}
-
-
-