Create installation process

CMakeLists.txt

@@ -30,6 +30,7 @@ SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
 option(ENABLE_TESTING "Unit testing build"                        OFF)
 option(CODE_COVERAGE "Collect coverage from test library"         OFF)
+option(INSTALL_GTEST "Enable installation of googletest."         OFF)
 
 # CMakes modules
 # --------------
@@ -76,3 +77,12 @@ if (ENABLE_TESTING)
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)
+
+# Installation
+# ------------
+install(TARGETS BayesNet
+        ARCHIVE DESTINATION lib
+        LIBRARY DESTINATION lib
+        CONFIGURATIONS Release)
+install(DIRECTORY src/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
+install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)

Makefile

@@ -47,6 +47,11 @@ clean: ## Clean the tests info
 	$(call ClearTests)
 	@echo ">>> Done";
 
+install: ## Install library
+	@echo ">>> Installing BayesNet...";
+	@cmake --install $(f_release)
+	@echo ">>> Done";
+
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet...";
 	@if [ -d ./$(f_debug) ]; then rm -rf ./$(f_debug); fi

lib/catch2

@@ -1 +1 @@
-Subproject commit ed6ac8a629f9a4206575be784c1e340da2a94855
+Subproject commit 8ac8190e494a381072c89f5e161b92a08d98b37b

sample/sample.cc

@@ -1,6 +1,6 @@
-#include "ArffFiles.h"
-#include "CPPFImdlp.h"
-#include "BoostAODE.h"
+#include <ArffFiles.h>
+#include <CPPFImdlp.h>
+#include "ensembles/BoostAODE.h"
 
 std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
 {

src/CMakeLists.txt

@@ -4,15 +4,10 @@ include_directories(
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
     ${BayesNet_SOURCE_DIR}/src
-    ${BayesNet_SOURCE_DIR}/src/feature_selection
-    ${BayesNet_SOURCE_DIR}/src/bayesian_network
-    ${BayesNet_SOURCE_DIR}/src/classifiers
-    ${BayesNet_SOURCE_DIR}/src/ensembles
-    ${BayesNet_SOURCE_DIR}/src/utils
     ${CMAKE_BINARY_DIR}/configured_files/include
 )
 
 file(GLOB_RECURSE Sources "*.cc")
 
 add_library(BayesNet ${Sources})
-target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

src/bayesian_network/Network.cc

@@ -1,7 +1,7 @@
 #include <thread>
 #include <mutex>
 #include "Network.h"
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 namespace bayesnet {
     Network::Network() : features(std::vector<std::string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
     Network::Network(float maxT) : features(std::vector<std::string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}

src/classifiers/Classifier.cc

@@ -1,5 +1,5 @@
 #include "Classifier.h"
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}

src/classifiers/Classifier.h

@@ -2,8 +2,8 @@
 #define CLASSIFIER_H
 #include <torch/torch.h>
 #include "BaseClassifier.h"
-#include "Network.h"
-#include "BayesMetrics.h"
+#include "bayesian_network/Network.h"
+#include "utils/BayesMetrics.h"
 
 namespace bayesnet {
     class Classifier : public BaseClassifier {

src/classifiers/KDB.h

@@ -2,7 +2,7 @@
 #define KDB_H
 #include <torch/torch.h>
 #include "Classifier.h"
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 namespace bayesnet {
     class KDB : public Classifier {
     private:

src/classifiers/Proposal.h

@@ -3,7 +3,7 @@
 #include <string>
 #include <map>
 #include <torch/torch.h>
-#include "Network.h"
+#include "bayesian_network/Network.h"
 #include "CPPFImdlp.h"
 #include "Classifier.h"
 

src/ensembles/AODE.h

@@ -1,7 +1,7 @@
 #ifndef AODE_H
 #define AODE_H
 #include "Ensemble.h"
-#include "SPODE.h"
+#include "classifiers/SPODE.h"
 namespace bayesnet {
     class AODE : public Ensemble {
     public:

src/ensembles/AODELd.h

@@ -1,8 +1,8 @@
 #ifndef AODELD_H
 #define AODELD_H
 #include "Ensemble.h"
-#include "Proposal.h"
-#include "SPODELd.h"
+#include "classifiers/Proposal.h"
+#include "classifiers/SPODELd.h"
 
 namespace bayesnet {
     class AODELd : public Ensemble, public Proposal {

src/ensembles/BoostAODE.cc

@@ -2,11 +2,11 @@
 #include <functional>
 #include <limits.h>
 #include <tuple>
+#include <folding.hpp>
 #include "BoostAODE.h"
-#include "CFS.h"
-#include "FCBF.h"
-#include "IWSS.h"
-#include "folding.hpp"
+#include "feature_selection/CFS.h"
+#include "feature_selection/FCBF.h"
+#include "feature_selection/IWSS.h"
 
 namespace bayesnet {
     struct {

src/ensembles/BoostAODE.h

@@ -2,8 +2,8 @@
 #define BOOSTAODE_H
 #include "Ensemble.h"
 #include <map>
-#include "SPODE.h"
-#include "FeatureSelect.h"
+#include "classifiers/SPODE.h"
+#include "feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class BoostAODE : public Ensemble {
     public:

src/ensembles/Ensemble.h

@@ -1,9 +1,9 @@
 #ifndef ENSEMBLE_H
 #define ENSEMBLE_H
 #include <torch/torch.h>
-#include "Classifier.h"
-#include "BayesMetrics.h"
-#include "bayesnetUtils.h"
+#include "classifiers/Classifier.h"
+#include "utils/BayesMetrics.h"
+#include "utils/bayesnetUtils.h"
 
 namespace bayesnet {
     class Ensemble : public Classifier {

src/feature_selection/CFS.cc

@@ -1,6 +1,6 @@
 #include "CFS.h"
 #include <limits>
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 namespace bayesnet {
     void CFS::fit()
     {

src/feature_selection/CFS.h

@@ -2,7 +2,7 @@
 #define CFS_H
 #include <torch/torch.h>
 #include <vector>
-#include "FeatureSelect.h"
+#include "feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class CFS : public FeatureSelect {
     public:

src/feature_selection/FCBF.cc

@@ -1,4 +1,4 @@
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 #include "FCBF.h"
 namespace bayesnet {
 

src/feature_selection/FCBF.h

@@ -2,7 +2,7 @@
 #define FCBF_H
 #include <torch/torch.h>
 #include <vector>
-#include "FeatureSelect.h"
+#include "feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class FCBF : public FeatureSelect {
     public:

src/feature_selection/FeatureSelect.cc

@@ -1,6 +1,6 @@
 #include "FeatureSelect.h"
 #include <limits>
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 namespace bayesnet {
     FeatureSelect::FeatureSelect(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights) :
         Metrics(samples, features, className, classNumStates), maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures), weights(weights)

src/feature_selection/FeatureSelect.h

@@ -2,7 +2,7 @@
 #define FEATURE_SELECT_H
 #include <torch/torch.h>
 #include <vector>
-#include "BayesMetrics.h"
+#include "utils/BayesMetrics.h"
 namespace bayesnet {
     class FeatureSelect : public Metrics {
     public:

src/feature_selection/IWSS.cc

@@ -1,6 +1,6 @@
 #include "IWSS.h"
 #include <limits>
-#include "bayesnetUtils.h"
+#include "utils/bayesnetUtils.h"
 namespace bayesnet {
     IWSS::IWSS(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights, const double threshold) :
         FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)