fast creg training code for univariate linear and logistic regression

2 files changed, 20 insertions, 13 deletions

diff --git a/training/liblbfgs/lbfgs++.h b/training/liblbfgs/lbfgs++.h
index 342f9b0e..92ead955 100644
--- a/training/liblbfgs/lbfgs++.h
+++ b/training/liblbfgs/lbfgs++.h
@@ -16,28 +16,33 @@
 template <typename Function>
 class LBFGS {
  public:
-  LBFGS(size_t n,            // number of variables
-        const Function& f,   // function to optimize
-        double l1_c = 0.0,   // l1 penalty strength
-        size_t m = 10        // number of memory buffers
-                             // TODO should use custom allocator here:
+  LBFGS(size_t n,              // number of variables
+        const Function& f,     // function to optimize
+        size_t m = 10,         // number of memory buffers
+        double l1_c = 0.0,     // l1 penalty strength
+        unsigned l1_start = 0, // l1 penalty starting index
+        double eps = 1e-5      // convergence epsilon
+                               // TODO should use custom allocator here:
         ) : p_x(new std::vector<lbfgsfloatval_t>(n, 0.0)),
                              owned(true),
                              m_x(*p_x),
                              func(f) {
-    Init(m, l1_c);
+    Init(m, l1_c, l1_start, eps);
   }
 
   // constructor where external vector storage for variables is used
   LBFGS(std::vector<lbfgsfloatval_t>* px,
         const Function& f,
-        double l1_c = 0.0,   // l1 penalty strength
-        size_t m = 10
+        size_t m = 10,         // number of memory buffers
+        double l1_c = 0.0,     // l1 penalty strength
+        unsigned l1_start = 0, // l1 penalty starting index
+        double eps = 1e-5      // convergence epsilon
+                               // TODO should use custom allocator here:
         ) : p_x(px),
                              owned(false),
                              m_x(*p_x),
                              func(f) {
-    Init(m, l1_c);
+    Init(m, l1_c, l1_start, eps);
   }
 
   ~LBFGS() {
@@ -60,12 +65,14 @@ class LBFGS {
   }
 
  private:
-  void Init(size_t m, double l1_c) {
+  void Init(size_t m, double l1_c, unsigned l1_start, double eps) {
     lbfgs_parameter_init(&param);
     param.m = m;
+    param.epsilon = eps;
     if (l1_c > 0.0) {
       param.linesearch = LBFGS_LINESEARCH_BACKTRACKING;
-      param.orthantwise_c = 1.0;
+      param.orthantwise_c = l1_c;
+      param.orthantwise_start = l1_start;
     }
     silence = false;
   }
diff --git a/training/liblbfgs/ll_test.cc b/training/liblbfgs/ll_test.cc
index 43c0f214..48bc0366 100644
--- a/training/liblbfgs/ll_test.cc
+++ b/training/liblbfgs/ll_test.cc
@@ -5,7 +5,7 @@ using namespace std;
 
 // Function must be lbfgsfloatval_t f(x.begin, x.end, g.begin)
 lbfgsfloatval_t func(const vector<lbfgsfloatval_t>& x, lbfgsfloatval_t* g) {
-    int i;
+    unsigned i;
     lbfgsfloatval_t fx = 0.0;
 
     for (i = 0;i < x.size();i += 2) {
@@ -24,7 +24,7 @@ void Opt(F& f) {
   lbfgs.MinimizeFunction();
 }
 
-int main(int argc, char** argv) {
+int main() {
   Opt(func);
   return 0;
 }