major restructure of the training code

1 files changed, 0 insertions, 118 deletions

diff --git a/training/optimize_test.cc b/training/optimize_test.cc
deleted file mode 100644
index bff2ca03..00000000
--- a/training/optimize_test.cc
+++ /dev/null
@@ -1,118 +0,0 @@
-#include <cassert>
-#include <iostream>
-#include <sstream>
-#include <boost/program_options/variables_map.hpp>
-#include "optimize.h"
-#include "online_optimizer.h"
-#include "sparse_vector.h"
-#include "fdict.h"
-
-using namespace std;
-
-double TestOptimizer(BatchOptimizer* opt) {
-  cerr << "TESTING NON-PERSISTENT OPTIMIZER\n";
-
-  // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
-  // df/dx1 = 8*x1 + x2
-  // df/dx2 = 2*x2 + x1
-  // df/dx3 = 2*x3 + 6
-  vector<double> x(3);
-  vector<double> g(3);
-  x[0] = 8;
-  x[1] = 8;
-  x[2] = 8;
-  double obj = 0;
-  do {
-    g[0] = 8 * x[0] + x[1];
-    g[1] = 2 * x[1] + x[0];
-    g[2] = 2 * x[2] + 6;
-    obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
-    opt->Optimize(obj, g, &x);
-
-    cerr << x[0] << " " << x[1] << " " << x[2] << endl;
-    cerr << "   obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
-  } while (!opt->HasConverged());
-  return obj;
-}
-
-double TestPersistentOptimizer(BatchOptimizer* opt) {
-  cerr << "\nTESTING PERSISTENT OPTIMIZER\n";
-  // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
-  // df/dx1 = 8*x1 + x2
-  // df/dx2 = 2*x2 + x1
-  // df/dx3 = 2*x3 + 6
-  vector<double> x(3);
-  vector<double> g(3);
-  x[0] = 8;
-  x[1] = 8;
-  x[2] = 8;
-  double obj = 0;
-  string state;
-  bool converged = false;
-  while (!converged) {
-    g[0] = 8 * x[0] + x[1];
-    g[1] = 2 * x[1] + x[0];
-    g[2] = 2 * x[2] + 6;
-    obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
-
-    {
-      if (state.size() > 0) {
-        istringstream is(state, ios::binary);
-        opt->Load(&is);
-      }
-      opt->Optimize(obj, g, &x);
-      ostringstream os(ios::binary); opt->Save(&os); state = os.str();
-
-    }
-
-    cerr << x[0] << " " << x[1] << " " << x[2] << endl;
-    cerr << "   obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
-    converged = opt->HasConverged();
-    if (!converged) {
-      // now screw up the state (should be undone by Load)
-      obj += 2.0;
-      g[1] = -g[2];
-      vector<double> x2 = x;
-      try {
-        opt->Optimize(obj, g, &x2);
-      } catch (...) { }
-    }
-  }
-  return obj;
-}
-
-template <class O>
-void TestOptimizerVariants(int num_vars) {
-  O oa(num_vars);
-  cerr << "-------------------------------------------------------------------------\n";
-  cerr << "TESTING: " << oa.Name() << endl;
-  double o1 = TestOptimizer(&oa);
-  O ob(num_vars);
-  double o2 = TestPersistentOptimizer(&ob);
-  if (o1 != o2) {
-    cerr << oa.Name() << " VARIANTS PERFORMED DIFFERENTLY!\n" << o1 << " vs. " << o2 << endl;
-    exit(1);
-  }
-  cerr << oa.Name() << " SUCCESS\n";
-}
-
-using namespace std::tr1;
-
-void TestOnline() {
-  size_t N = 20;
-  double C = 1.0;
-  double eta0 = 0.2;
-  std::tr1::shared_ptr<LearningRateSchedule> r(new ExponentialDecayLearningRate(N, eta0, 0.85));
-  //shared_ptr<LearningRateSchedule> r(new StandardLearningRate(N, eta0));
-  CumulativeL1OnlineOptimizer opt(r, N, C, std::vector<int>());
-  assert(r->eta(10) < r->eta(1));
-}
-
-int main() {
-  int n = 3;
-  TestOptimizerVariants<LBFGSOptimizer>(n);
-  TestOptimizerVariants<RPropOptimizer>(n);
-  TestOnline();
-  return 0;
-}
-