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