Apply clang-format

1 files changed, 23 insertions, 25 deletions

diff --git a/utils/synutils/maxent-3.0/lbfgs.cpp b/utils/synutils/maxent-3.0/lbfgs.cpp
index 9eb04bef..bd26f048 100644
--- a/utils/synutils/maxent-3.0/lbfgs.cpp
+++ b/utils/synutils/maxent-3.0/lbfgs.cpp
@@ -8,20 +8,17 @@
 
 using namespace std;
 
-const static int    M = LBFGS_M;
+const static int M = LBFGS_M;
 const static double LINE_SEARCH_ALPHA = 0.1;
-const static double LINE_SEARCH_BETA  = 0.5;
+const static double LINE_SEARCH_BETA = 0.5;
 
 // stopping criteria
-int    LBFGS_MAX_ITER      = 300;
+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 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;
@@ -39,20 +36,23 @@ ME_Model::backtracking_line_search(
 // 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[])
-{
+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;    }
+  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];
+    alpha[i] = z[j] * dot_product(s[j], q);
+    q += -alpha[i] * y[j];
   }
   if (iter > 0) {
     const int j = (iter - 1) % M;
@@ -63,16 +63,14 @@ approximate_Hg(const int iter, const Vec & grad,
   }
   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]);
+    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)
-{
+vector<double> ME_Model::perform_LBFGS(const vector<double>& x0) {
   const size_t dim = x0.size();
   Vec x = x0;
 
@@ -84,10 +82,11 @@ ME_Model::perform_LBFGS(const vector<double> & x0)
 
   for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) {
 
-    fprintf(stderr, "%3d  obj(err) = %f (%6.4f)", iter+1, -f, _train_error);
+    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, "  heldout_logl(err) = %f (%6.4f)", heldout_logl,
+              _heldout_error);
     }
     fprintf(stderr, "\n");
 
@@ -107,4 +106,3 @@ ME_Model::perform_LBFGS(const vector<double> & x0)
 
   return x.STLVec();
 }
-