Removed unnecessary copying of data from implementation of the new fe…

…ature accumulation and changed name of the function exposed to python to accumulateEdgeStatistics
ukoethe · Mar 17, 2016 · 1aa1f6d · 1aa1f6d
1 parent 987a7bc
commit 1aa1f6d
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Showing 2 changed files with 7 additions and 8 deletions.
diff --git a/vigranumpy/lib/__init__.py b/vigranumpy/lib/__init__.py
@@ -1223,12 +1223,12 @@ def accumulateEdgeFeatures(self,edgeFeatures,acc='mean',out=None):
                 else:
                     return graphs._ragEdgeFeatures(self,graph,affiliatedEdges,edgeFeatures,weights,acc,out)
 
-        def accumulateEdgeFeaturesNew(self, edgeFeatures, out=None):
+        def accumulateEdgeStatistics(self, edgeFeatures, out=None):
             if not isinstance(self, RegionAdjacencyGraph):
                 raise AttributeError("accumulateEdgeFeaturesNew not implemented for " + type(self))
             graph = self.baseGraph
             affiliatedEdges = self.affiliatedEdges
-            out = graphs._ragEdgeFeaturesNew(self, graph, affiliatedEdges, edgeFeatures, out)
+            out = graphs._ragEdgeStatistics(self, graph, affiliatedEdges, edgeFeatures, out)
             return out
 
         def accumulateNodeFeatures(self,nodeFeatures,acc='mean',out=None):

diff --git a/vigranumpy/src/core/export_graph_rag_visitor.hxx b/vigranumpy/src/core/export_graph_rag_visitor.hxx
@@ -162,7 +162,7 @@ public:
                     python::arg("out")=python::object()
                 )
             );
-            python::def("_ragEdgeFeaturesNew",
+            python::def("_ragEdgeStatistics",
                 registerConverters(
                     &pyRagEdgeFeaturesFromImplicit< float, float, ImplicitEdgeMap >
                 ),
@@ -689,11 +689,10 @@ public:
                 n_bins = std::max( n_bins_min, std::min(n_bins, n_bins_max) );
                 a.setHistogramOptions(HistogramOptions().setBinCount(n_bins));
 
-                // copy values to multi array
-                MultiArray<1, double> data( Shape1(affEdges.size()) );
-                for(size_t i=0;i<affEdges.size();++i)
-                    data(i) = otfEdgeMap[affEdges[i]];
-                extractFeatures(data.begin(), data.end(), a);
+                // accumulate the values of this edge
+                for(unsigned int k=1; k <= a.passesRequired(); ++k)
+                    for(size_t i=0;i<affEdges.size();++i)
+                        a.updatePassN( otfEdgeMap[affEdges[i]], k );
 
                 feat[0] = get<Mean>(a);
                 feat[1] = get<Sum>(a);