1 files changed, 122 insertions, 0 deletions
diff --git a/ml/dlib/dlib/test/elastic_net.cpp b/ml/dlib/dlib/test/elastic_net.cpp
new file mode 100644
index 000000000..0e0501639
--- /dev/null
+++ b/ml/dlib/dlib/test/elastic_net.cpp
@@ -0,0 +1,122 @@
+// Copyright (C) 2016  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+
+#include <dlib/optimization/elastic_net.h>
+#include "tester.h"
+#include <dlib/svm.h>
+#include <dlib/rand.h>
+#include <dlib/string.h>
+#include <vector>
+#include <sstream>
+#include <ctime>
+
+namespace  
+{
+    using namespace test;
+    using namespace dlib;
+    using namespace std;
+    dlib::logger dlog("test.elastic_net");
+
+// ----------------------------------------------------------------------------------------
+
+    matrix<double,0,1> basic_elastic_net(
+        const matrix<double>& X,
+        const matrix<double,0,1>& Y,
+        double ridge_lambda,
+        double lasso_budget,
+        double eps
+    )
+    {
+        DLIB_CASSERT(X.nc() == Y.nr(),"");
+
+
+        typedef matrix<double,0,1> sample_type;
+        typedef linear_kernel<sample_type> kernel_type;
+
+        svm_c_linear_dcd_trainer<kernel_type> trainer;
+        trainer.solve_svm_l2_problem(true);
+        const double C = 1/(2*ridge_lambda);
+        trainer.set_c(C);
+        trainer.set_epsilon(eps);
+        trainer.enable_shrinking(true);
+        trainer.include_bias(false);
+
+
+        std::vector<sample_type> samples;
+        std::vector<double> labels;
+        for (long r = 0; r < X.nr(); ++r)
+        {
+            sample_type temp = trans(rowm(X,r));
+
+            const double xmul = (1/lasso_budget);
+            samples.push_back(temp - xmul*Y);
+            labels.push_back(+1);
+            samples.push_back(temp + xmul*Y);
+            labels.push_back(-1);
+        }
+
+        svm_c_linear_dcd_trainer<kernel_type>::optimizer_state state;
+        auto df = trainer.train(samples, labels, state);
+        auto&& alpha = state.get_alpha();
+
+        matrix<double,0,1> betas(alpha.size()/2);
+        for (long i = 0; i < betas.size(); ++i)
+            betas(i) = lasso_budget*(alpha[2*i] - alpha[2*i+1]);
+        betas /= sum(mat(alpha));
+        return betas;
+    }
+
+// ----------------------------------------------------------------------------------------
+
+    class test_elastic_net : public tester
+    {
+    public:
+        test_elastic_net (
+        ) :
+            tester (
+                "test_elastic_net",       
+                "Run tests on the elastic_net object.", 
+                0                     
+            )
+        {
+        }
+
+        void perform_test (
+        )
+        {
+            matrix<double> w = {1,2,0,4, 0,0,0,0,0, 6, 7,8,0, 9, 0};
+
+            matrix<double> X = randm(w.size(),1000);
+            matrix<double> Y = trans(X)*w;
+            Y += 0.1*(randm(Y.nr(), Y.nc())-0.5);
+
+
+            double ridge_lambda = 0.1;
+            double lasso_budget = sum(abs(w));
+            double eps = 0.0000001;
+
+            dlib::elastic_net solver(X*trans(X),X*Y);
+            solver.set_epsilon(eps);
+
+
+            matrix<double,0,1> results;
+            matrix<double,0,1> results2;
+            for (double s = 1.2; s > 0.10; s *= 0.9)
+            {
+                print_spinner();
+                dlog << LINFO << "s: "<< s;
+                // make sure the two solvers agree.  
+                results = basic_elastic_net(X, Y, ridge_lambda, lasso_budget*s, eps);
+                results2 = solver(ridge_lambda, lasso_budget*s);
+                dlog << LINFO << "error: "<< max(abs(results - results2));
+                DLIB_TEST(max(abs(results - results2)) < 1e-3);
+            }
+        }
+    } a;
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+
+