Update version number and Changelog

CHANGELOG.md

@@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
+## [1.0.4]
 
 ### Added
 
@@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Changed
 
 - Change the library structure adding folders for each group of classes (classifiers, ensembles, etc).
+- The significances of the models generated under the feature selection algorithm are now computed after all the models have been generated and an &alpha;<sub>t</sub> value is computed and assigned to each model.
 
 ## [1.0.3]
 

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 1.0.3
+  VERSION 1.0.4
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX

docs/BoostAODE.md

@@ -16,7 +16,7 @@ The hyperparameters defined in the algorithm are:
 
 - ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*.
 
-- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same alpha_t. Default value: *""*.
+- ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same &alpha;<sub>t</sub>. Default value: *""*.
 
 - ***threshold*** (*double*): Sets the necessary value for the IWSS and FCBF algorithms to function. Accepted values are:
   - IWSS: $threshold \in [0, 0.5]$

src/ensembles/BoostAODE.cc

@@ -181,17 +181,17 @@ namespace bayesnet {
             prob_table += model->predict_proba(X) * 1.0;
         }
         // prob_table doesn't store probabilities but the sum of them
-        // to have them we need to divide by the sum of the significances but we
-        // don't need them to predict label values
+        // to have them we need to divide by the sum of the "weights" used to 
+        // consider the results obtanined in the model's predict_proba.
         return prob_table.argmax(1);
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         initialize_prob_table = true;
         fitted = true;
         double alpha_t = 0;
-        // Algorithm based on the adaboost algorithm for classification
-        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         bool exitCondition = false;
         std::unordered_set<int> featuresUsed;