minor code cleanups

src/fastDTW.cpp

@@ -67,9 +67,9 @@ std::vector<std::vector<T> > fastDTW<T>::downsample(const std::vector<std::vecto
         }
     }
     //TODO: implement downsampling by resolution
-    return shrunkenSeries;
+    return shrunkenSeries;size()
 }
 
 //explicit instantiation
 template class fastDTW<double>;
-template class fastDTW<float>;
\ No newline at end of file
+template class fastDTW<float>;

src/modelSet.cpp

@@ -96,8 +96,8 @@ std::vector<T> modelSet<T>::run(const std::vector<T> &inputVector) {
 template<typename T>
 std::vector<T> json2vector(Json::Value json) {
     std::vector<T> returnVec;
-    for (unsigned int i = 0; i < json.size(); ++i) {
-        returnVec.push_back(json[i].asDouble());
+    for (auto jsonValue : json) {
+        returnVec.push_back(jsonValue.asDouble());
     }
     return returnVec;
 }

src/rapidStream.h

@@ -9,6 +9,7 @@
 #define rapidStream_h
 
 #include <stdint.h>
+#include <atomic>
 #include "../dependencies/bayesfilter/src/BayesianFilter.h"
 
 template<typename T>
@@ -104,7 +105,7 @@ public:
 
 private:
     uint32_t windowSize;
-    uint32_t windowIndex;
+    std::atomic_uint32_t windowIndex;
     T *circularWindow;
     
     T calcCurrentVel(int i);

src/seriesClassification.cpp

@@ -30,6 +30,7 @@ bool seriesClassificationTemplate<T>::train(const std::vector<trainingSeriesTemp
     allTrainingSeries = seriesSet;
     minLength = maxLength = int(allTrainingSeries[0].input.size());
     for (int i = 0; i < allTrainingSeries.size(); ++i) {
+        //for (auto trainingSeries : allTrainingSeries)
         //Global
         int newLength = int(allTrainingSeries[i].input.size());
         if (newLength < minLength) {
@@ -198,4 +199,4 @@ template class seriesClassificationTemplate<float>;
 //std::vector<T> seriesClassification::getCosts(const std::vector<trainingExample> &trainingSet) {
 //    run(trainingSet);
 //    return allCosts;
-//}
\ No newline at end of file
+//}