Combine everything related to maxent to a single file

1 files changed, 0 insertions, 127 deletions

diff --git a/utils/owlqn.cpp b/utils/owlqn.cpp
deleted file mode 100644
index c3a0f0da..00000000
--- a/utils/owlqn.cpp
+++ /dev/null
@@ -1,127 +0,0 @@
-#include <vector>
-#include <iostream>
-#include <cmath>
-#include <stdio.h>
-#include "mathvec.h"
-#include "lbfgs.h"
-#include "maxent.h"
-
-using namespace std;
-
-const static int M = LBFGS_M;
-const static double LINE_SEARCH_ALPHA = 0.1;
-const static double LINE_SEARCH_BETA = 0.5;
-
-// stopping criteria
-int OWLQN_MAX_ITER = 300;
-const static double MIN_GRAD_NORM = 0.0001;
-
-Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
-                   const Vec y[], const double z[]);
-
-inline int sign(double x) {
-  if (x > 0) return 1;
-  if (x < 0) return -1;
-  return 0;
-};
-
-static Vec pseudo_gradient(const Vec& x, const Vec& grad0, const double C) {
-  Vec grad = grad0;
-  for (size_t i = 0; i < x.Size(); i++) {
-    if (x[i] != 0) {
-      grad[i] += C * sign(x[i]);
-      continue;
-    }
-    const double gm = grad0[i] - C;
-    if (gm > 0) {
-      grad[i] = gm;
-      continue;
-    }
-    const double gp = grad0[i] + C;
-    if (gp < 0) {
-      grad[i] = gp;
-      continue;
-    }
-    grad[i] = 0;
-  }
-
-  return grad;
-}
-
-double ME_Model::regularized_func_grad(const double C, const Vec& x,
-                                       Vec& grad) {
-  double f = FunctionGradient(x.STLVec(), grad.STLVec());
-  for (size_t i = 0; i < x.Size(); i++) {
-    f += C * fabs(x[i]);
-  }
-
-  return f;
-}
-
-double ME_Model::constrained_line_search(double C, const Vec& x0,
-                                         const Vec& grad0, const double f0,
-                                         const Vec& dx, Vec& x, Vec& grad1) {
-  // compute the orthant to explore
-  Vec orthant = x0;
-  for (size_t i = 0; i < orthant.Size(); i++) {
-    if (orthant[i] == 0) orthant[i] = -grad0[i];
-  }
-
-  double t = 1.0 / LINE_SEARCH_BETA;
-
-  double f;
-  do {
-    t *= LINE_SEARCH_BETA;
-    x = x0 + t * dx;
-    x.Project(orthant);
-    //    for (size_t i = 0; i < x.Size(); i++) {
-    //      if (x0[i] != 0 && sign(x[i]) != sign(x0[i])) x[i] = 0;
-    //    }
-
-    f = regularized_func_grad(C, x, grad1);
-    //        cout << "*";
-  } while (f > f0 + LINE_SEARCH_ALPHA * dot_product(x - x0, grad0));
-
-  return f;
-}
-
-vector<double> ME_Model::perform_OWLQN(const vector<double>& x0,
-                                       const double C) {
-  const size_t dim = x0.size();
-  Vec x = x0;
-
-  Vec grad(dim), dx(dim);
-  double f = regularized_func_grad(C, x, grad);
-
-  Vec s[M], y[M];
-  double z[M];  // rho
-
-  for (int iter = 0; iter < OWLQN_MAX_ITER; iter++) {
-    Vec pg = pseudo_gradient(x, grad, C);
-
-    fprintf(stderr, "%3d  obj(err) = %f (%6.4f)", iter + 1, -f, _train_error);
-    if (_nheldout > 0) {
-      const double heldout_logl = heldout_likelihood();
-      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", heldout_logl,
-              _heldout_error);
-    }
-    fprintf(stderr, "\n");
-
-    if (sqrt(dot_product(pg, pg)) < MIN_GRAD_NORM) break;
-
-    dx = -1 * approximate_Hg(iter, pg, s, y, z);
-    if (dot_product(dx, pg) >= 0) dx.Project(-1 * pg);
-
-    Vec x1(dim), grad1(dim);
-    f = constrained_line_search(C, x, pg, f, dx, x1, grad1);
-
-    s[iter % M] = x1 - x;
-    y[iter % M] = grad1 - grad;
-    z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]);
-
-    x = x1;
-    grad = grad1;
-  }
-
-  return x.STLVec();
-}