warnings, style here

src/neuralNetwork.cpp

@@ -475,7 +475,7 @@ void neuralNetwork<T>::backpropagate(const T& desiredOutput)
     }
 
     //deltas between hidden
-    for (int i = numHiddenLayers - 1; i >= 0; --i)
+  for (int i { static_cast<int>(numHiddenLayers)- 1 }; i >= 0; --i)
     {
         for (size_t j = 0; j < numHiddenNodes; ++j)
         {

src/regression.cpp

@@ -62,21 +62,22 @@ regressionTemplate<T>::regressionTemplate(const std::vector<trainingExampleTempl
 };
 
 template<typename T>
-std::vector<int> regressionTemplate<T>::getNumHiddenLayers() const 
+std::vector<size_t> regressionTemplate<T>::getNumHiddenLayers() const
 {
-    std::vector<int> vecNumHiddenLayers;
-    if (std::begin(modelSet<T>::myModelSet) != std::end(modelSet<T>::myModelSet)) 
+    std::vector<size_t> vecNumHiddenLayers;
+
+    if (std::begin(modelSet<T>::myModelSet) != std::end(modelSet<T>::myModelSet))
     {
-        for (baseModel<T>* model : modelSet<T>::myModelSet) 
+        for (const baseModel<T>* model : modelSet<T>::myModelSet)
         {
-            neuralNetwork<T>* nnModel = dynamic_cast<neuralNetwork<T>*>(model); //FIXME: I really dislike this design
-            vecNumHiddenLayers.push_back(nnModel->getNumHiddenLayers());
+            vecNumHiddenLayers.push_back(dynamic_cast<const neuralNetwork<T>*>(model)->getNumHiddenLayers()); //FIXME: I really dislike this design
         }
     } 
     else 
     {
         vecNumHiddenLayers = { numHiddenLayers };
     }
+
     return vecNumHiddenLayers;
 }
 
@@ -96,21 +97,22 @@ void regressionTemplate<T>::setNumHiddenLayers(const int &num_hidden_layers)
 }
 
 template<typename T>
-std::vector<int> regressionTemplate<T>::getNumHiddenNodes() const 
+std::vector<size_t> regressionTemplate<T>::getNumHiddenNodes() const
 {
-    std::vector<int> vecNumHiddenNodes;
-    if (std::begin(modelSet<T>::myModelSet) != std::end(modelSet<T>::myModelSet)) 
+    std::vector<size_t> vecNumHiddenNodes;
+
+    if (std::begin(modelSet<T>::myModelSet) != std::end(modelSet<T>::myModelSet))
     {
-        for (baseModel<T>* model : modelSet<T>::myModelSet) 
+        for (const baseModel<T>* model : modelSet<T>::myModelSet)
         {
-            neuralNetwork<T>* nnModel = dynamic_cast<neuralNetwork<T>*>(model); //FIXME: I really dislike this design
-            vecNumHiddenNodes.push_back(nnModel->getNumHiddenNodes());
+            vecNumHiddenNodes.push_back( dynamic_cast<const neuralNetwork<T>*>(model)->getNumHiddenNodes()); //FIXME: I really dislike this design
         }
     } 
     else 
     {
         vecNumHiddenNodes = { numHiddenNodes };
     }
+
     return vecNumHiddenNodes;
 }
 

src/regression.h

@@ -23,52 +23,52 @@ template<typename T>
 class regressionTemplate final : public modelSet<T>
 {
 public:
-    /** with no arguments, just make an empty vector */
-    regressionTemplate();
-    /** create based on training set inputs and outputs */
-    regressionTemplate(const std::vector<trainingExampleTemplate<T> > &trainingSet);
-    /** create with proper models, but not trained */
-    regressionTemplate(const int &numInputs, const int &numOutputs);
-    
-    /** destructor */
-    ~regressionTemplate() {};
-    
-    /** Train on a specified set, causes creation if not created */
-    bool train(const std::vector<trainingExampleTemplate<T> > &trainingSet) override;
-
-    /** Check how far the training has gotten. Averages progress over all models in training */
-    float getTrainingProgress();
-    
-    /** Check how many training epochs each model will run. This feature is temporary, and will be replaced by a different design. */
-    std::vector<size_t> getNumEpochs() const;
-    
-    /** Call before train, to set the number of training epochs */
-    void setNumEpochs(const size_t &epochs);
-    
-    /** Check how many hidden layers are in each model. This feature is temporary, and will be replaced by a different design. */
-    std::vector<int> getNumHiddenLayers() const;
-    
-    /** Set how many hidden layers are in all models. This feature is temporary, and will be replaced by a different design. */
-    void setNumHiddenLayers(const int &num_hidden_layers);
-    
-    /** Check how many hidden nodes are in each model. This feature is temporary, and will be replaced by a different design. */
-    std::vector<int> getNumHiddenNodes() const;
-    
-    /** Set how many hidden layers are in all models. This feature is temporary, and will be replaced by a different design. */
-    void setNumHiddenNodes(const int &num_hidden_nodes);
-    
+  /** with no arguments, just make an empty vector */
+  regressionTemplate();
+  /** create based on training set inputs and outputs */
+  regressionTemplate(const std::vector<trainingExampleTemplate<T> > &trainingSet);
+  /** create with proper models, but not trained */
+  regressionTemplate(const int &numInputs, const int &numOutputs);
+
+  /** destructor */
+  ~regressionTemplate() {};
+
+  /** Train on a specified set, causes creation if not created */
+  bool train(const std::vector<trainingExampleTemplate<T> > &trainingSet) override;
+  
+  /** Check how far the training has gotten. Averages progress over all models in training */
+  float getTrainingProgress();
+
+  /** Check how many training epochs each model will run. This feature is temporary, and will be replaced by a different design. */
+  std::vector<size_t> getNumEpochs() const;
+
+  /** Call before train, to set the number of training epochs */
+  void setNumEpochs(const size_t &epochs);
+
+  /** Check how many hidden layers are in each model. This feature is temporary, and will be replaced by a different design. */
+  std::vector<size_t> getNumHiddenLayers() const;
+
+  /** Set how many hidden layers are in all models. This feature is temporary, and will be replaced by a different design. */
+  void setNumHiddenLayers(const int &num_hidden_layers);
+
+  /** Check how many hidden nodes are in each model. This feature is temporary, and will be replaced by a different design. */
+  std::vector<size_t> getNumHiddenNodes() const;
+
+  /** Set how many hidden layers are in all models. This feature is temporary, and will be replaced by a different design. */
+  void setNumHiddenNodes(const int &num_hidden_nodes);
+
 private:
-    int numHiddenLayers; //Temporary -- this should be part of the nn class. -mz
-    size_t numEpochs; //Temporary -- also should be part of nn only. -mz
-    int numHiddenNodes; //Temporary -- also should be part of nn only. -mz
-    bool created;
+  size_t numHiddenLayers { 1 }; //Temporary -- this should be part of the nn class. -mz
+  size_t numEpochs; //Temporary -- also should be part of nn only. -mz
+  size_t numHiddenNodes; //Temporary -- also should be part of nn only. -mz
+  bool created;
 };
 
 namespace rapidLib
 {
-    //This is here so the old API still works
-    using regression = regressionTemplate<double>;
-    using regressionFloat = regressionTemplate<float>;
+//This is here so the old API still works
+using regression = regressionTemplate<double>;
+using regressionFloat = regressionTemplate<float>;
 };
 
 #endif