Merge pull request #3 from DEIS-Tools/prepare-1.0.8

Prepare 1.0.8

Author: petergjoel

.gitignore

@@ -30,3 +30,4 @@
 *.exe
 *.out
 *.app
+/build/

src/MLearning.cpp

@@ -166,7 +166,7 @@ namespace prlearn {
             _nodes[rnd].update(rnd, minimization, clouds, _nodes, dimen, false, delta, options);
     }
 
-    qvar_t MLearning::lookup(size_t label, const double* f_var, size_t dimen) const {
+    qvar_t MLearning::lookup(size_t label, const double* f_var, size_t) const {
         for (auto& el : _mapping) {
             if (el._label == label) {
                 auto n = _nodes[el._nid].find_node(_nodes, f_var, el._nid);
@@ -176,7 +176,7 @@ namespace prlearn {
         return qvar_t(std::numeric_limits<double>::quiet_NaN(), 0.0, 0.0);
     }
 
-    void MLearning::print(std::ostream& s, size_t tabs, std::map<size_t, size_t>& edge_map, const std::vector<MLearning>& clouds) const {
+    void MLearning::print(std::ostream& s, size_t tabs, std::map<size_t, size_t>& edge_map, const std::vector<MLearning>&) const {
         s << std::setprecision (std::numeric_limits<double>::digits10 + 1);
         for (size_t i = 0; i < tabs; ++i) s << "\t";
         s << "{";
@@ -225,9 +225,8 @@ namespace prlearn {
         }
     }
 
-    void MLearning::update(const std::vector<MLearning>& clouds, bool minimization) 
+    void MLearning::update(const std::vector<MLearning>&, bool) 
     {
-        std::cerr << "SIZE " << this << " " << _nodes.size() << std::endl;
     }
 
     std::unique_ptr<size_t[] > MLearning::findIntersection(const double* point) const {

src/RefinementTree.cpp

@@ -62,7 +62,7 @@ namespace prlearn {
     }
 
     qvar_t
-    RefinementTree::lookup(size_t label, const double* point, size_t dimen) const {
+    RefinementTree::lookup(size_t label, const double* point, size_t) const {
         el_t lf(label);
         auto res = std::lower_bound(std::begin(_mapping), std::end(_mapping), lf);
         if (res == std::end(_mapping) || res->_label != label)

src/SimpleRegressor.h

@@ -47,7 +47,7 @@ namespace prlearn {
             auto res = std::lower_bound(std::begin(_labels), std::end(_labels), lf);
 
             if (res != std::end(_labels) && res->_label == label)
-                return res->_value;
+                return qvar_t{res->_value.avg(), (double)res->_cnt, res->_value._variance};
             else
                 return qvar_t{std::numeric_limits<double>::quiet_NaN(), 0, 0};
         }
@@ -109,9 +109,9 @@ namespace prlearn {
             el_t(size_t label, double d) : _label(label) {
                 _value += d;
             };
-            size_t _label;
+            size_t _label = 0;
             qvar_t _value;
-            size_t _cnt;
+            size_t _cnt = 0;
 
             bool operator<(const el_t& other) const {
                 return _label < other._label;