Fix some more lint warnings

src/BayesNet/Mst.cc

@@ -7,7 +7,7 @@
 
 namespace bayesnet {
     using namespace std;
-    Graph::Graph(int V)
+    Graph::Graph(int V) : V(V)
     {
         parent = vector<int>(V);
         for (int i = 0; i < V; i++)
@@ -34,10 +34,10 @@ namespace bayesnet {
     }
     void Graph::kruskal_algorithm()
     {
-        int i, uSt, vEd;
         // sort the edges ordered on decreasing weight
-        sort(G.begin(), G.end(), [](auto& left, auto& right) {return left.first > right.first;});
+        sort(G.begin(), G.end(), [](const auto& left, const auto& right) {return left.first > right.first;});
-        for (i = 0; i < G.size(); i++) {
+        for (int i = 0; i < G.size(); i++) {
+            int uSt, vEd;
             uSt = find_set(G[i].second.first);
             vEd = find_set(G[i].second.second);
             if (uSt != vEd) {

src/BayesNet/Node.cc

@@ -88,18 +88,15 @@ namespace bayesnet {
     {
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
-        for (auto father : getParents()) {
+        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
-            dimensions.push_back(father->getNumStates());
+
-        }
         // Create a tensor of zeros with the dimensions of the CPT
         cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
         // Fill table with counts
         for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
             torch::List<c10::optional<torch::Tensor>> coordinates;
             coordinates.push_back(torch::tensor(dataset[name][n_sample]));
-            for (auto father : getParents()) {
+            transform(parents.begin(), parents.end(), back_inserter(coordinates), [&dataset, &n_sample](const auto& parent) { return torch::tensor(dataset[parent->getName()][n_sample]); });
-                coordinates.push_back(torch::tensor(dataset[father->getName()][n_sample]));
-            }
             // Increment the count of the corresponding coordinate
             cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
         }
@@ -111,19 +108,15 @@ namespace bayesnet {
         torch::List<c10::optional<torch::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(torch::tensor(evidence[name]));
-        for (auto parent : getParents()) {
+        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return torch::tensor(evidence[parent->getName()]); });
-            coordinates.push_back(torch::tensor(evidence[parent->getName()]));
-        }
         return cpTable.index({ coordinates }).item<float>();
     }
-    vector<string> Node::graph(string className)
+    vector<string> Node::graph(const string& className)
     {
         auto output = vector<string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
         output.push_back(name + " [shape=circle" + suffix + "] \n");
-        for (auto& child : children) {
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
-            output.push_back(name + " -> " + child->getName());
-        }
         return output;
     }
 }

src/BayesNet/Node.h

@@ -16,7 +16,7 @@ namespace bayesnet {
         vector<int64_t> dimensions; // dimensions of the cpTable
     public:
         vector<pair<string, string>> combinations(const vector<string>&);
-        Node(const std::string&, int);
+        Node(const string&, int);
         void clear();
         void addParent(Node*);
         void addChild(Node*);
@@ -30,7 +30,7 @@ namespace bayesnet {
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
-        vector<string> graph(string clasName); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& clasName); // Returns a vector of strings representing the graph in graphviz format
         float getFactorValue(map<string, int>&);
     };
 }

src/Platform/Datasets.cc

@@ -21,9 +21,7 @@ namespace platform {
     vector<string> Datasets::getNames()
     {
         vector<string> result;
-        for (auto& d : datasets) {
+        transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
-            result.push_back(d.first);
-        }
         return result;
     }
     vector<string> Datasets::getFeatures(string name)
@@ -79,7 +77,7 @@ namespace platform {
         }
         return datasets[name]->getTensors();
     }
-    bool Datasets::isDataset(string name)
+    bool Datasets::isDataset(const string& name)
     {
         return datasets.find(name) != datasets.end();
     }
@@ -193,9 +191,8 @@ namespace platform {
         yv = arff.getY();
         // Get className & Features
         className = arff.getClassName();
-        for (auto feature : arff.getAttributes()) {
+        auto attributes = arff.getAttributes();
-            features.push_back(feature.first);
+        transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& attribute) { return attribute.first; });
-        }
     }
     void Dataset::load()
     {

src/Platform/Datasets.h

@@ -49,7 +49,7 @@ namespace platform {
         bool discretize;
         void load(); // Loads the list of datasets
     public:
-        Datasets(const string& path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
+        explicit Datasets(const string& path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
         vector<string> getNames();
         vector<string> getFeatures(string name);
         int getNSamples(string name);
@@ -58,7 +58,7 @@ namespace platform {
         pair<vector<vector<float>>&, vector<int>&> getVectors(string name);
         pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(string name);
         pair<torch::Tensor&, torch::Tensor&> getTensors(string name);
-        bool isDataset(string name);
+        bool isDataset(const string& name);
     };
 };