From c5ff1a0b2b8229d8b08c59d28d363f658729bbfd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Tue, 11 Jun 2024 13:43:12 +0200
Subject: [PATCH] Add smoothing parameter for compatibility with BayesNet

---
 CMakeLists.txt         |  2 +-
 pyclfs/PyClassifier.cc |  2 +-
 pyclfs/PyClassifier.h  | 10 +++++-----
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c412b5d..d0409b2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(PyClassifiers
-  VERSION 1.0.1
+  VERSION 1.0.2
   DESCRIPTION "Python Classifiers Wrapper."
   HOMEPAGE_URL "https://github.com/rmontanana/pyclassifiers"
   LANGUAGES CXX
diff --git a/pyclfs/PyClassifier.cc b/pyclfs/PyClassifier.cc
index 9ab656a..47f1185 100644
--- a/pyclfs/PyClassifier.cc
+++ b/pyclfs/PyClassifier.cc
@@ -70,7 +70,7 @@ namespace pywrap {
         fitted = true;
         return *this;
     }
-    PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
     {
         return fit(X, y);
     }
diff --git a/pyclfs/PyClassifier.h b/pyclfs/PyClassifier.h
index ffcdd7f..fbe4005 100644
--- a/pyclfs/PyClassifier.h
+++ b/pyclfs/PyClassifier.h
@@ -17,12 +17,12 @@ namespace pywrap {
     public:
         PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false);
         virtual ~PyClassifier();
-        PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
+        PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
         // X is nxm tensor, y is nx1 tensor
-        PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override;
         PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
-        PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
-        PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override { return *this; };
+        PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
+        PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); }; // Not implemented
         torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros({ 0, 0 }); } // Not implemented
@@ -47,7 +47,7 @@ namespace pywrap {
         void setHyperparameters(const nlohmann::json& hyperparameters) override;
     protected:
         nlohmann::json hyperparameters;
-        void trainModel(const torch::Tensor& weights) override {};
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override {};
         std::vector<std::string> notes;
     private:
         PyWrap* pyWrap;