From 1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@allegro.clab.cs.cmu.edu>
Date: Sun, 18 Nov 2012 13:35:42 -0500
Subject: major restructure of the training code

---
 training/utils/optimize_test.cc | 118 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)
 create mode 100644 training/utils/optimize_test.cc

(limited to 'training/utils/optimize_test.cc')

diff --git a/training/utils/optimize_test.cc b/training/utils/optimize_test.cc
new file mode 100644
index 00000000..bff2ca03
--- /dev/null
+++ b/training/utils/optimize_test.cc
@@ -0,0 +1,118 @@
+#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;
+}
+
-- 
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