Fix smell issues in markdown and python

CLAUDE.md

@@ -9,14 +9,17 @@ BayesNet is a C++ library implementing Bayesian Network Classifiers. It provides
 ## Build System & Dependencies
 
 ### Dependency Management
+
 The project supports **two package managers**:
 
 #### vcpkg (Default)
-- Uses vcpkg with private registry at https://github.com/rmontanana/vcpkg-stash
+
+- Uses vcpkg with private registry at <https://github.com/rmontanana/vcpkg-stash>
 - Core dependencies: libtorch, nlohmann-json, folding, fimdlp, arff-files, catch2
 - All dependencies defined in `vcpkg.json` with version overrides
 
 #### Conan (Alternative)
+
 - Modern C++ package manager with better dependency resolution
 - Configured via `conanfile.py` for packaging and distribution
 - Supports subset of dependencies (libtorch, nlohmann-json, catch2)
@@ -25,6 +28,7 @@ The project supports **two package managers**:
 ### Build Commands
 
 #### Using vcpkg (Default)
+
 ```bash
 # Initialize dependencies
 make init
@@ -49,6 +53,7 @@ make clean
 ```
 
 #### Using Conan
+
 ```bash
 # Install Conan first: pip install conan
 
@@ -74,6 +79,7 @@ make conan-clean
 ```
 
 ### CMake Configuration
+
 - Uses CMake 3.27+ with C++17 standard
 - Debug builds automatically enable testing and coverage
 - Release builds optimize with `-Ofast`
@@ -89,6 +95,7 @@ make conan-clean
 - Coverage reporting with lcov/genhtml
 
 ### Test Categories
+
 - A2DE, BoostA2DE, BoostAODE, XSPODE, XSPnDE, XBAODE, XBA2DE
 - Classifier, Ensemble, FeatureSelection, Metrics, Models
 - Network, Node, MST, Modules
@@ -96,6 +103,7 @@ make conan-clean
 ## Code Architecture
 
 ### Core Structure
+
 ```
 bayesnet/
 ├── BaseClassifier.h          # Abstract base for all classifiers
@@ -107,12 +115,14 @@ bayesnet/
 ```
 
 ### Key Design Patterns
+
 - **BaseClassifier** abstract interface for all algorithms
 - Template-based design with both std::vector and torch::Tensor support
 - Network/Node abstraction for Bayesian network representation
 - Feature selection as separate, composable modules
 
 ### Data Handling
+
 - Supports both discrete integer data and continuous data with discretization
 - ARFF file format support through arff-files library
 - Tensor operations via PyTorch C++ (libtorch)
@@ -128,6 +138,7 @@ bayesnet/
 ## Sample Applications
 
 Sample code in `sample/` directory demonstrates library usage:
+
 ```bash
 make sample fname=tests/data/iris.arff model=TANLd
 ```
@@ -135,6 +146,7 @@ make sample fname=tests/data/iris.arff model=TANLd
 ## Package Distribution
 
 ### Creating Conan Packages
+
 ```bash
 # Create package locally
 make conan-create
@@ -148,7 +160,9 @@ make conan-upload remote=myremote profile=myprofile
 ```
 
 ### Using the Library
+
 With Conan:
+
 ```python
 # conanfile.txt or conanfile.py
 [requires]
@@ -159,6 +173,7 @@ cmake
 ```
 
 With vcpkg:
+
 ```json
 {
   "dependencies": ["bayesnet"]
@@ -170,7 +185,7 @@ With vcpkg:
 - **Add new classifier**: Extend BaseClassifier, implement in appropriate subdirectory
 - **Add new test**: Update `tests/CMakeLists.txt` and create test in `tests/`
 - **Modify build**: Edit main `CMakeLists.txt` or use Makefile targets
-- **Update dependencies**: 
+- **Update dependencies**:
   - vcpkg: Modify `vcpkg.json` and run `make init`
   - Conan: Modify `conanfile.py` and run `make conan-init`
-- **Package for distribution**: Use `make conan-create` for Conan packaging
+- **Package for distribution**: Use `make conan-create` for Conan packaging

CONAN_README.md

@@ -16,29 +16,29 @@ conan profile new default --detect
 
 1. Create a `conanfile.txt` in your project:
 
-```ini
-[requires]
-libtorch/2.7.0
-bayesnet/1.2.0
+    ```ini
+    [requires]
+    libtorch/2.7.0
+    bayesnet/1.2.0
 
-[generators]
-CMakeDeps
-CMakeToolchain
+    [generators]
+    CMakeDeps
+    CMakeToolchain
 
-```
+    ```
 
-2. Install dependencies:
+1. Install dependencies:
 
-```bash
-conan install . --build=missing
-```
+    ```bash
+    conan install . --build=missing
+    ```
 
-3. In your CMakeLists.txt:
+1. In your CMakeLists.txt:
 
-```cmake
-find_package(bayesnet REQUIRED)
-target_link_libraries(your_target bayesnet::bayesnet)
-```
+    ```cmake
+    find_package(bayesnet REQUIRED)
+    target_link_libraries(your_target bayesnet::bayesnet)
+    ```
 
 ### Building BayesNet with Conan
 
@@ -69,8 +69,8 @@ make conan-create
 For the custom dependencies, you'll need to create Conan recipes:
 
 1. **folding**: Cross-validation library
-2. **fimdlp**: Discretization library
-3. **arff-files**: ARFF file format parser
+1. **fimdlp**: Discretization library
+1. **arff-files**: ARFF file format parser
 
 Contact the maintainer or create custom recipes for these packages.
 

conanfile.py

@@ -18,7 +18,7 @@ class BayesNetConan(ConanFile):
         "enable_testing": False,
         "enable_coverage": False
     }
-    
+
     # Sources are located in the same place as this recipe, copy them to the recipe
     exports_sources = "CMakeLists.txt", "bayesnet/*", "config/*", "cmake/*", "docs/*", "tests/*", "bayesnetConfig.cmake.in"
     
@@ -35,7 +35,7 @@ class BayesNetConan(ConanFile):
             self.version = match.group(1)
         else:
             raise Exception("Version not found in CMakeLists.txt")
-        self.version = match.group(1) 
+        self.version = match.group(1)
 
     def config_options(self):
         if self.settings.os == "Windows":
@@ -50,7 +50,7 @@ class BayesNetConan(ConanFile):
         self.requires("libtorch/2.7.0")
         self.requires("nlohmann_json/3.11.3")
         self.requires("folding/1.1.1")  # Custom package
-        self.requires("fimdlp/2.1.0")   # Custom package  
+        self.requires("fimdlp/2.1.0")   # Custom package
     
     def build_requirements(self):
         self.build_requires("cmake/[>=3.27]")