minor fixes

setup.py

@@ -80,7 +80,7 @@ def build_extension(self, ext):
 
 setup(
     name='Fred-Frechet',
-    version='1.11',
+    version='1.11.2',
     author='Dennis Rohde',
     author_email='[email protected]',
     description='A fast, scalable and light-weight C++ Fréchet distance library, exposed to python and focused on (k,l)-clustering of polygonal curves.',

src/clustering.cpp

@@ -128,8 +128,10 @@ Clustering_Result kl_cluster(const curve_number_t num_centers, const curve_size_
     }
 
     if (not consecutive_call) {
-        if (Config::verbosity > 0) py::print("KL_CLUST: allocating ", in.size(), " x ", in.size(), " distance_matrix");
-        if (use_distance_matrix) distances = Distance_Matrix(in.size(), in.size());
+        if (use_distance_matrix) {
+            if (Config::verbosity > 0) py::print("KL_CLUST: allocating ", in.size(), " x ", in.size(), " distance_matrix");
+            distances = Distance_Matrix(in.size(), in.size());
+        }
         if (Config::verbosity > 0) py::print("KL_CLUST: allocating space for ", in.size(), " simplifications, each of complexity ", ell);
         simplifications = Curves(in.size(), ell, in.dimensions());
     } else if (use_distance_matrix) {

src/simplification.cpp

@@ -167,6 +167,7 @@ Curve approximate_minimum_link_simplification(const Curve &pcurve, const distanc
 
 Curve approximate_minimum_error_simplification(const Curve &curve, const curve_size_t ell) {
     if (Config::verbosity > 1) py::print("ASIMPL: computing approximate minimum error simplification");
+    if (ell >= curve.complexity()) return curve;
     Curve simplification(curve.dimensions()), segment(2, curve.dimensions());
 
     segment[0] = curve.front();