1 files changed, 0 insertions, 108 deletions
diff --git a/utils/lbfgs.cpp b/utils/lbfgs.cpp
deleted file mode 100644
index bd26f048..00000000
--- a/utils/lbfgs.cpp
+++ /dev/null
@@ -1,108 +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 LBFGS_MAX_ITER = 300;
-const static double MIN_GRAD_NORM = 0.0001;
-
-double ME_Model::backtracking_line_search(const Vec& x0, const Vec& grad0,
-                                          const double f0, const Vec& dx,
-                                          Vec& x, Vec& grad1) {
-  double t = 1.0 / LINE_SEARCH_BETA;
-
-  double f;
-  do {
-    t *= LINE_SEARCH_BETA;
-    x = x0 + t * dx;
-    f = FunctionGradient(x.STLVec(), grad1.STLVec());
-    //        cout << "*";
-  } while (f > f0 + LINE_SEARCH_ALPHA * t * dot_product(dx, grad0));
-
-  return f;
-}
-
-//
-// Jorge Nocedal, "Updating Quasi-Newton Matrices With Limited Storage",
-// Mathematics of Computation, Vol. 35, No. 151, pp. 773-782, 1980.
-//
-Vec approximate_Hg(const int iter, const Vec& grad, const Vec s[],
-                   const Vec y[], const double z[]) {
-  int offset, bound;
-  if (iter <= M) {
-    offset = 0;
-    bound = iter;
-  } else {
-    offset = iter - M;
-    bound = M;
-  }
-
-  Vec q = grad;
-  double alpha[M], beta[M];
-  for (int i = bound - 1; i >= 0; i--) {
-    const int j = (i + offset) % M;
-    alpha[i] = z[j] * dot_product(s[j], q);
-    q += -alpha[i] * y[j];
-  }
-  if (iter > 0) {
-    const int j = (iter - 1) % M;
-    const double gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
-    //    static double gamma;
-    //    if (gamma == 0) gamma = ((1.0 / z[j]) / dot_product(y[j], y[j]));
-    q *= gamma;
-  }
-  for (int i = 0; i <= bound - 1; i++) {
-    const int j = (i + offset) % M;
-    beta[i] = z[j] * dot_product(y[j], q);
-    q += s[j] * (alpha[i] - beta[i]);
-  }
-
-  return q;
-}
-
-vector<double> ME_Model::perform_LBFGS(const vector<double>& x0) {
-  const size_t dim = x0.size();
-  Vec x = x0;
-
-  Vec grad(dim), dx(dim);
-  double f = FunctionGradient(x.STLVec(), grad.STLVec());
-
-  Vec s[M], y[M];
-  double z[M];  // rho
-
-  for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) {
-
-    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(grad, grad)) < MIN_GRAD_NORM) break;
-
-    dx = -1 * approximate_Hg(iter, grad, s, y, z);
-
-    Vec x1(dim), grad1(dim);
-    f = backtracking_line_search(x, grad, 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();
-}