Compare commits
8 Commits
Author | SHA1 | Date | |
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da05de6e15
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b0c2ff1aa8
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306d3a4b55
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bf08b0de89
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b976db53c6
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be39d2dedb
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4ca770d16b
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6bf3b939bc
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@@ -1,4 +1,10 @@
|
||||
# .clang-format
|
||||
---
|
||||
BasedOnStyle: LLVM
|
||||
AccessModifierOffset: -4
|
||||
BreakBeforeBraces: Linux
|
||||
ColumnLimit: 0
|
||||
FixNamespaceComments: false
|
||||
IndentWidth: 4
|
||||
ColumnLimit: 120
|
||||
NamespaceIndentation: All
|
||||
TabWidth: 4
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||||
|
@@ -35,7 +35,8 @@ RUN rm -fr lcov-2.1
|
||||
|
||||
# Install Miniconda
|
||||
RUN mkdir -p /opt/conda
|
||||
RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh" -O /opt/conda/miniconda.sh && \
|
||||
# RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh" -O /opt/conda/miniconda.sh && \
|
||||
RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" -O /opt/conda/miniconda.sh && \
|
||||
bash /opt/conda/miniconda.sh -b -p /opt/miniconda
|
||||
|
||||
# Add conda to PATH
|
||||
|
19
.gitea/workflows/action.yaml
Normal file
19
.gitea/workflows/action.yaml
Normal file
@@ -0,0 +1,19 @@
|
||||
name: Gitea Actions Demo
|
||||
run-name: ${{ github.actor }} is testing out Gitea Actions 🚀
|
||||
on: [push]
|
||||
jobs:
|
||||
Explore-GitHub-Actions:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
|
||||
- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
|
||||
- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
- run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
|
||||
- run: echo "🖥️ The workflow is now ready to test your code on the runner."
|
||||
- name: List files in the repository
|
||||
run: |
|
||||
ls ${{ github.workspace }}
|
||||
- run: echo "🍏 This job's status is ${{ job.status }}."
|
||||
|
19
.gitea/workflows/testing.yaml
Normal file
19
.gitea/workflows/testing.yaml
Normal file
@@ -0,0 +1,19 @@
|
||||
name: Gitea Actions Demo
|
||||
run-name: ${{ github.actor }} is testing out Gitea Actions 🚀
|
||||
on: [push]
|
||||
jobs:
|
||||
Explore-GitHub-Actions:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
|
||||
- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
|
||||
- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
|
||||
- name: Check out repository code
|
||||
uses: actions/checkout@v4
|
||||
- run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
|
||||
- run: echo "🖥️ The workflow is now ready to test your code on the runner."
|
||||
- name: List files in the repository
|
||||
run: |
|
||||
ls ${{ github.workspace }}
|
||||
- run: echo "🍏 This job's status is ${{ job.status }}."
|
||||
|
@@ -7,8 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
## [1.0.7] 2025-03-16
|
||||
### Internal
|
||||
|
||||
- Add changes to .clang-format to ajust to vscode format style thanks to https://clang-format-configurator.site/
|
||||
|
||||
## [1.0.7] 2025-03-16
|
||||
|
||||
### Added
|
||||
|
||||
|
@@ -1,4 +1,4 @@
|
||||
cmake_minimum_required(VERSION 3.20)
|
||||
cmake_minimum_required(VERSION 3.24)
|
||||
|
||||
project(BayesNet
|
||||
VERSION 1.0.7
|
||||
@@ -8,15 +8,11 @@ project(BayesNet
|
||||
)
|
||||
|
||||
if (CODE_COVERAGE AND NOT ENABLE_TESTING)
|
||||
MESSAGE(FATAL_ERROR "Code coverage requires testing enabled")
|
||||
message(FATAL_ERROR "Code coverage requires testing enabled")
|
||||
endif (CODE_COVERAGE AND NOT ENABLE_TESTING)
|
||||
|
||||
find_package(Torch REQUIRED)
|
||||
|
||||
if (POLICY CMP0135)
|
||||
cmake_policy(SET CMP0135 NEW)
|
||||
endif ()
|
||||
|
||||
# Global CMake variables
|
||||
# ----------------------
|
||||
set(CMAKE_CXX_STANDARD 17)
|
||||
@@ -24,7 +20,7 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
|
||||
set(CMAKE_CXX_EXTENSIONS OFF)
|
||||
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
|
||||
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
|
||||
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
|
||||
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
|
||||
if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
|
||||
@@ -44,19 +40,28 @@ set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_P
|
||||
include(AddGitSubmodule)
|
||||
|
||||
if (CMAKE_BUILD_TYPE STREQUAL "Debug")
|
||||
MESSAGE("Debug mode")
|
||||
message(STATUS "* Debug mode")
|
||||
enable_testing()
|
||||
set(ENABLE_TESTING ON)
|
||||
set(CODE_COVERAGE ON)
|
||||
endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
|
||||
|
||||
# Testing
|
||||
# -------
|
||||
if (ENABLE_TESTING)
|
||||
message(STATUS "Testing enabled")
|
||||
add_subdirectory(tests/lib/catch2)
|
||||
include(CTest)
|
||||
add_subdirectory(tests)
|
||||
endif (ENABLE_TESTING)
|
||||
if (CODE_COVERAGE)
|
||||
include(CodeCoverage)
|
||||
message(STATUS "Code coverage enabled")
|
||||
set(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
|
||||
endif (CODE_COVERAGE)
|
||||
|
||||
get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
|
||||
message(STATUS "Languages=${LANGUAGES}")
|
||||
if (CODE_COVERAGE)
|
||||
enable_testing()
|
||||
include(CodeCoverage)
|
||||
MESSAGE(STATUS "Code coverage enabled")
|
||||
SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
|
||||
endif (CODE_COVERAGE)
|
||||
|
||||
if (ENABLE_CLANG_TIDY)
|
||||
include(StaticAnalyzers) # clang-tidy
|
||||
@@ -74,14 +79,7 @@ add_git_submodule("lib/mdlp")
|
||||
add_subdirectory(config)
|
||||
add_subdirectory(bayesnet)
|
||||
|
||||
# Testing
|
||||
# -------
|
||||
if (ENABLE_TESTING)
|
||||
MESSAGE(STATUS "Testing enabled")
|
||||
add_subdirectory(tests/lib/catch2)
|
||||
include(CTest)
|
||||
add_subdirectory(tests)
|
||||
endif (ENABLE_TESTING)
|
||||
|
||||
|
||||
# Installation
|
||||
# ------------
|
||||
@@ -100,9 +98,7 @@ if (Doxygen_FOUND)
|
||||
set(doxyfile_in ${DOC_DIR}/Doxyfile.in)
|
||||
set(doxyfile ${DOC_DIR}/Doxyfile)
|
||||
configure_file(${doxyfile_in} ${doxyfile} @ONLY)
|
||||
doxygen_add_docs(doxygen
|
||||
WORKING_DIRECTORY ${DOC_DIR}
|
||||
CONFIG_FILE ${doxyfile})
|
||||
doxygen_add_docs(doxygen WORKING_DIRECTORY ${DOC_DIR} CONFIG_FILE ${doxyfile})
|
||||
else (Doxygen_FOUND)
|
||||
MESSAGE("* Doxygen not found")
|
||||
message(WARNING "* Doxygen not found")
|
||||
endif (Doxygen_FOUND)
|
||||
|
10
README.md
10
README.md
@@ -2,12 +2,12 @@
|
||||
|
||||

|
||||
[](<https://opensource.org/licenses/MIT>)
|
||||

|
||||

|
||||
[](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
|
||||
[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
|
||||
[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
|
||||

|
||||
[](html/index.html)
|
||||

|
||||
[](https://gitea.rmontanana.es/rmontanana/BayesNet)
|
||||
[](https://doi.org/10.5281/zenodo.14210344)
|
||||
|
||||
Bayesian Network Classifiers library
|
||||
@@ -76,8 +76,12 @@ make sample fname=tests/data/glass.arff
|
||||
|
||||
#### - [BoostAODE](docs/BoostAODE.md)
|
||||
|
||||
#### - XBAODE
|
||||
|
||||
#### - BoostA2DE
|
||||
|
||||
#### - XBA2DE
|
||||
|
||||
### With Local Discretization
|
||||
|
||||
#### - TANLd
|
||||
|
@@ -4,25 +4,26 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#include <random>
|
||||
#include <set>
|
||||
#include <limits.h>
|
||||
#include <tuple>
|
||||
#include "BoostAODE.h"
|
||||
#include "bayesnet/classifiers/SPODE.h"
|
||||
#include <loguru.hpp>
|
||||
#include <limits.h>
|
||||
#include <loguru.cpp>
|
||||
#include <loguru.hpp>
|
||||
#include <random>
|
||||
#include <set>
|
||||
#include <tuple>
|
||||
|
||||
namespace bayesnet {
|
||||
namespace bayesnet
|
||||
{
|
||||
|
||||
BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
|
||||
{
|
||||
}
|
||||
std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
|
||||
{
|
||||
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
|
||||
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
|
||||
std::vector<int> featuresSelected = featureSelection(weights_);
|
||||
for (const int& feature : featuresSelected) {
|
||||
for (const int &feature : featuresSelected) {
|
||||
std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
|
||||
model->fit(dataset, features, className, states, weights_, smoothing);
|
||||
models.push_back(std::move(model));
|
||||
@@ -32,7 +33,7 @@ namespace bayesnet {
|
||||
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
|
||||
return featuresSelected;
|
||||
}
|
||||
void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
|
||||
void BoostAODE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing)
|
||||
{
|
||||
//
|
||||
// Logging setup
|
||||
@@ -45,7 +46,7 @@ namespace bayesnet {
|
||||
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
|
||||
fitted = true;
|
||||
double alpha_t = 0;
|
||||
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
|
||||
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
|
||||
bool finished = false;
|
||||
std::vector<int> featuresUsed;
|
||||
n_models = 0;
|
||||
@@ -73,7 +74,7 @@ namespace bayesnet {
|
||||
// validation error is not decreasing
|
||||
// run out of features
|
||||
bool ascending = order_algorithm == Orders.ASC;
|
||||
std::mt19937 g{ 173 };
|
||||
std::mt19937 g{173};
|
||||
while (!finished) {
|
||||
// Step 1: Build ranking with mutual information
|
||||
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
|
||||
@@ -81,10 +82,8 @@ namespace bayesnet {
|
||||
std::shuffle(featureSelection.begin(), featureSelection.end(), g);
|
||||
}
|
||||
// Remove used features
|
||||
featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
|
||||
{ return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
|
||||
end(featureSelection)
|
||||
);
|
||||
featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }),
|
||||
end(featureSelection));
|
||||
int k = bisection ? pow(2, tolerance) : 1;
|
||||
int counter = 0; // The model counter of the current pack
|
||||
// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
|
||||
@@ -176,7 +175,7 @@ namespace bayesnet {
|
||||
}
|
||||
notes.push_back("Number of models: " + std::to_string(n_models));
|
||||
}
|
||||
std::vector<std::string> BoostAODE::graph(const std::string& title) const
|
||||
std::vector<std::string> BoostAODE::graph(const std::string &title) const
|
||||
{
|
||||
return Ensemble::graph(title);
|
||||
}
|
||||
|
@@ -10,12 +10,11 @@
|
||||
#include <random>
|
||||
#include <tuple>
|
||||
|
||||
namespace bayesnet {
|
||||
XBAODE::XBAODE() : Boost(false) {
|
||||
validHyperparameters = {"alpha_block", "order", "convergence", "convergence_best", "bisection",
|
||||
"threshold", "maxTolerance", "predict_voting", "select_features"};
|
||||
}
|
||||
std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing) {
|
||||
namespace bayesnet
|
||||
{
|
||||
XBAODE::XBAODE() : Boost(false) {}
|
||||
std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing)
|
||||
{
|
||||
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
|
||||
std::vector<int> featuresSelected = featureSelection(weights_);
|
||||
for (const int &feature : featuresSelected) {
|
||||
@@ -26,8 +25,9 @@ std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing) {
|
||||
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
|
||||
std::to_string(features.size()) + " with " + select_features_algorithm);
|
||||
return featuresSelected;
|
||||
}
|
||||
void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) {
|
||||
}
|
||||
void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing)
|
||||
{
|
||||
X_train_ = TensorUtils::to_matrix(X_train);
|
||||
y_train_ = TensorUtils::to_vector<int>(y_train);
|
||||
if (convergence) {
|
||||
@@ -180,5 +180,5 @@ void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_
|
||||
}
|
||||
notes.push_back("Number of models: " + std::to_string(n_models));
|
||||
return;
|
||||
}
|
||||
}
|
||||
} // namespace bayesnet
|
||||
|
@@ -96,6 +96,8 @@ abstract C_0002617087915615796317 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
{abstract} +topological_order() = 0 : std::vector<std::string>
|
||||
{abstract} #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : void
|
||||
__
|
||||
#notes : std::vector<std::string>
|
||||
#status : status_t
|
||||
#validHyperparameters : std::vector<std::string>
|
||||
}
|
||||
class "bayesnet::Metrics" as C_0005895723015084986588
|
||||
@@ -153,6 +155,7 @@ abstract C_0016351972983202413152 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+topological_order() : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
#CLASSIFIER_NOT_FITTED : const std::string
|
||||
#className : std::string
|
||||
#dataset : torch::Tensor
|
||||
#features : std::vector<std::string>
|
||||
@@ -161,20 +164,44 @@ __
|
||||
#metrics : Metrics
|
||||
#model : Network
|
||||
#n : unsigned int
|
||||
#notes : std::vector<std::string>
|
||||
#states : std::map<std::string,std::vector<int>>
|
||||
#status : status_t
|
||||
}
|
||||
class "bayesnet::Proposal" as C_0017759964713298103839
|
||||
class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
|
||||
+~Proposal() : void
|
||||
..
|
||||
#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
|
||||
#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
|
||||
#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
|
||||
#prepareX(torch::Tensor & X) : torch::Tensor
|
||||
__
|
||||
#Xf : torch::Tensor
|
||||
#discretizers : map<std::string,mdlp::CPPFImdlp *>
|
||||
#y : torch::Tensor
|
||||
}
|
||||
class "bayesnet::KDB" as C_0008902920152122000044
|
||||
class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+KDB(int k, float theta = 0.03) : void
|
||||
+~KDB() = default : void
|
||||
..
|
||||
#add_m_edges(int idx, std::vector<int> & S, torch::Tensor & weights) : void
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & name = "KDB") const : std::vector<std::string>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::KDBLd" as C_0002756018222998454702
|
||||
class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+KDBLd(int k) : void
|
||||
+~KDBLd() = default : void
|
||||
..
|
||||
+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 Smoothing_t smoothing) : KDBLd &
|
||||
+graph(const std::string & name = "KDB") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
__
|
||||
}
|
||||
class "bayesnet::SPODE" as C_0004096182510460307610
|
||||
class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+SPODE(int root) : void
|
||||
@@ -182,6 +209,20 @@ class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & name = "SPODE") const : std::vector<std::string>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::SPODELd" as C_0010957245114062042836
|
||||
class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+SPODELd(int root) : void
|
||||
+~SPODELd() = default : void
|
||||
..
|
||||
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
|
||||
+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 Smoothing_t smoothing) : SPODELd &
|
||||
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
|
||||
+graph(const std::string & name = "SPODELd") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
__
|
||||
}
|
||||
class "bayesnet::SPnDE" as C_0016268916386101512883
|
||||
@@ -200,44 +241,7 @@ class C_0014087955399074584137 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & name = "TAN") const : std::vector<std::string>
|
||||
__
|
||||
}
|
||||
class "bayesnet::Proposal" as C_0017759964713298103839
|
||||
class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
|
||||
+~Proposal() : void
|
||||
..
|
||||
#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
|
||||
#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
|
||||
#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
|
||||
#prepareX(torch::Tensor & X) : torch::Tensor
|
||||
__
|
||||
#Xf : torch::Tensor
|
||||
#discretizers : map<std::string,mdlp::CPPFImdlp *>
|
||||
#y : torch::Tensor
|
||||
}
|
||||
class "bayesnet::KDBLd" as C_0002756018222998454702
|
||||
class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+KDBLd(int k) : void
|
||||
+~KDBLd() = default : void
|
||||
..
|
||||
+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 Smoothing_t smoothing) : KDBLd &
|
||||
+graph(const std::string & name = "KDB") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
__
|
||||
}
|
||||
class "bayesnet::SPODELd" as C_0010957245114062042836
|
||||
class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+SPODELd(int root) : void
|
||||
+~SPODELd() = default : void
|
||||
..
|
||||
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
|
||||
+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 Smoothing_t smoothing) : SPODELd &
|
||||
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
|
||||
+graph(const std::string & name = "SPODELd") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::TANLd" as C_0013350632773616302678
|
||||
@@ -250,6 +254,64 @@ class C_0013350632773616302678 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
__
|
||||
}
|
||||
class "bayesnet::XSp2de" as C_0007640742442325463418
|
||||
class C_0007640742442325463418 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+XSp2de(int spIndex1, int spIndex2) : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
|
||||
+getClassNumStates() const : int
|
||||
+getNFeatures() const : int
|
||||
+getNumberOfEdges() const : int
|
||||
+getNumberOfNodes() const : int
|
||||
+getNumberOfStates() const : int
|
||||
+graph(const std::string & title) const : std::vector<std::string>
|
||||
+predict(const std::vector<int> & instance) const : int
|
||||
+predict(std::vector<std::vector<int>> & test_data) : std::vector<int>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
+predict_proba(const std::vector<int> & instance) const : std::vector<double>
|
||||
+predict_proba(std::vector<std::vector<int>> & test_data) : std::vector<std::vector<double>>
|
||||
+predict_proba(torch::Tensor & X) : torch::Tensor
|
||||
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
|
||||
+score(torch::Tensor & X, torch::Tensor & y) : float
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
+to_string() const : std::string
|
||||
#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::XSpode" as C_0015654113248178830206
|
||||
class C_0015654113248178830206 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+XSpode(int spIndex) : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
|
||||
+getClassNumStates() const : int
|
||||
+getNFeatures() const : int
|
||||
+getNumberOfEdges() const : int
|
||||
+getNumberOfNodes() const : int
|
||||
+getNumberOfStates() const : int
|
||||
+getStates() : std::vector<int> &
|
||||
+graph(const std::string & title) const : std::vector<std::string>
|
||||
+normalize(std::vector<double> & v) const : void
|
||||
+predict(const std::vector<int> & instance) const : int
|
||||
+predict(std::vector<std::vector<int>> & X) : std::vector<int>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
|
||||
+predict_proba(torch::Tensor & X) : torch::Tensor
|
||||
+predict_proba(const std::vector<int> & instance) const : std::vector<double>
|
||||
+score(torch::Tensor & X, torch::Tensor & y) : float
|
||||
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
+to_string() const : std::string
|
||||
#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::TensorUtils" as C_0010304804115474100819
|
||||
class C_0010304804115474100819 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
{static} +to_matrix(const torch::Tensor & X) : std::vector<std::vector<int>>
|
||||
{static} +to_vector<T>(const torch::Tensor & y) : std::vector<T>
|
||||
__
|
||||
}
|
||||
class "bayesnet::Ensemble" as C_0015881931090842884611
|
||||
class C_0015881931090842884611 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Ensemble(bool predict_voting = true) : void
|
||||
@@ -302,6 +364,17 @@ class C_0006288892608974306258 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::AODELd" as C_0003898187834670349177
|
||||
class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+AODELd(bool predict_voting = true) : void
|
||||
+~AODELd() = default : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+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 Smoothing_t smoothing) : AODELd &
|
||||
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
|
||||
abstract C_0013562609546004646591 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+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) : void
|
||||
@@ -324,15 +397,15 @@ __
|
||||
#suLabels : std::vector<double>
|
||||
#weights : const torch::Tensor &
|
||||
}
|
||||
class "bayesnet::(anonymous_60342586)" as C_0005584545181746538542
|
||||
class C_0005584545181746538542 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60357672)" as C_0006397015156479549697
|
||||
class C_0006397015156479549697 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60343240)" as C_0016227156982041949444
|
||||
class C_0016227156982041949444 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60358326)" as C_0013066254331852347304
|
||||
class C_0013066254331852347304 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
@@ -343,14 +416,17 @@ class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Boost(bool predict_voting = false) : void
|
||||
+~Boost() = default : void
|
||||
..
|
||||
#add_model(std::unique_ptr<Classifier> model, double significance) : void
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
#featureSelection(torch::Tensor & weights_) : std::vector<int>
|
||||
#remove_last_model() : void
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
#update_weights(torch::Tensor & ytrain, torch::Tensor & ypred, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
|
||||
#update_weights_block(int k, torch::Tensor & ytrain, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
|
||||
__
|
||||
#X_test : torch::Tensor
|
||||
#X_train : torch::Tensor
|
||||
#alpha_block : bool
|
||||
#bisection : bool
|
||||
#block_update : bool
|
||||
#convergence : bool
|
||||
@@ -364,31 +440,6 @@ __
|
||||
#y_test : torch::Tensor
|
||||
#y_train : torch::Tensor
|
||||
}
|
||||
class "bayesnet::AODELd" as C_0003898187834670349177
|
||||
class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+AODELd(bool predict_voting = true) : void
|
||||
+~AODELd() = default : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+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 Smoothing_t smoothing) : AODELd &
|
||||
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60275628)" as C_0009086919615463763584
|
||||
class C_0009086919615463763584 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60276282)" as C_0015251985607563196159
|
||||
class C_0015251985607563196159 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::BoostA2DE" as C_0000272055465257861326
|
||||
class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+BoostA2DE(bool predict_voting = false) : void
|
||||
@@ -398,15 +449,15 @@ class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60275502)" as C_0016033655851510053155
|
||||
class C_0016033655851510053155 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60425028)" as C_0000461144706913711531
|
||||
class C_0000461144706913711531 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60276156)" as C_0000379522761622473555
|
||||
class C_0000379522761622473555 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60425682)" as C_0014849589915262463453
|
||||
class C_0014849589915262463453 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
@@ -421,6 +472,38 @@ class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::XBA2DE" as C_0008480973840710001141
|
||||
class C_0008480973840710001141 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+XBA2DE(bool predict_voting = false) : void
|
||||
+~XBA2DE() = default : void
|
||||
..
|
||||
+getVersion() : std::string
|
||||
+graph(const std::string & title = "XBA2DE") const : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60414016)" as C_0008746994658440620779
|
||||
class C_0008746994658440620779 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60414670)" as C_0008030559132212449356
|
||||
class C_0008030559132212449356 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::XBAODE" as C_0005198482342493966768
|
||||
class C_0005198482342493966768 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+XBAODE() : void
|
||||
..
|
||||
+getVersion() : std::string
|
||||
#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::CFS" as C_0000093018845530739957
|
||||
class C_0000093018845530739957 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
|
||||
@@ -445,43 +528,43 @@ class C_0000066148117395428429 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+fit() : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60730495)" as C_0004857727320042830573
|
||||
class C_0004857727320042830573 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60810808)" as C_0012002108046995621535
|
||||
class C_0012002108046995621535 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60731150)" as C_0000076541533312623385
|
||||
class C_0000076541533312623385 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60811462)" as C_0004735044229422764240
|
||||
class C_0004735044229422764240 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60653004)" as C_0001444063444142949758
|
||||
class C_0001444063444142949758 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60804220)" as C_0007082100550474633839
|
||||
class C_0007082100550474633839 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60653658)" as C_0007139277546931322856
|
||||
class C_0007139277546931322856 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60804874)" as C_0003669430095936529648
|
||||
class C_0003669430095936529648 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60731375)" as C_0010493853592456211189
|
||||
class C_0010493853592456211189 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60809706)" as C_0012336951062058157227
|
||||
class C_0012336951062058157227 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60732030)" as C_0007011438637915849564
|
||||
class C_0007011438637915849564 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60810360)" as C_0002435892998884329673
|
||||
class C_0002435892998884329673 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
@@ -513,37 +596,43 @@ C_0010428199432536647474 --> C_0010428199432536647474 : -children
|
||||
C_0009493661199123436603 ..> C_0013393078277439680282
|
||||
C_0009493661199123436603 o-- C_0010428199432536647474 : -nodes
|
||||
C_0002617087915615796317 ..> C_0013393078277439680282
|
||||
C_0002617087915615796317 ..> C_0005907365846270811004
|
||||
C_0002617087915615796317 o-- C_0005907365846270811004 : #status
|
||||
C_0016351972983202413152 ..> C_0013393078277439680282
|
||||
C_0016351972983202413152 ..> C_0005907365846270811004
|
||||
C_0016351972983202413152 o-- C_0009493661199123436603 : #model
|
||||
C_0016351972983202413152 o-- C_0005895723015084986588 : #metrics
|
||||
C_0016351972983202413152 o-- C_0005907365846270811004 : #status
|
||||
C_0002617087915615796317 <|-- C_0016351972983202413152
|
||||
|
||||
C_0017759964713298103839 ..> C_0009493661199123436603
|
||||
C_0016351972983202413152 <|-- C_0008902920152122000044
|
||||
|
||||
C_0016351972983202413152 <|-- C_0004096182510460307610
|
||||
|
||||
C_0016351972983202413152 <|-- C_0016268916386101512883
|
||||
|
||||
C_0016351972983202413152 <|-- C_0014087955399074584137
|
||||
|
||||
C_0017759964713298103839 ..> C_0009493661199123436603
|
||||
C_0002756018222998454702 ..> C_0013393078277439680282
|
||||
C_0008902920152122000044 <|-- C_0002756018222998454702
|
||||
|
||||
C_0017759964713298103839 <|-- C_0002756018222998454702
|
||||
|
||||
C_0016351972983202413152 <|-- C_0004096182510460307610
|
||||
|
||||
C_0010957245114062042836 ..> C_0013393078277439680282
|
||||
C_0004096182510460307610 <|-- C_0010957245114062042836
|
||||
|
||||
C_0017759964713298103839 <|-- C_0010957245114062042836
|
||||
|
||||
C_0016351972983202413152 <|-- C_0016268916386101512883
|
||||
|
||||
C_0016351972983202413152 <|-- C_0014087955399074584137
|
||||
|
||||
C_0013350632773616302678 ..> C_0013393078277439680282
|
||||
C_0014087955399074584137 <|-- C_0013350632773616302678
|
||||
|
||||
C_0017759964713298103839 <|-- C_0013350632773616302678
|
||||
|
||||
C_0007640742442325463418 ..> C_0013393078277439680282
|
||||
C_0016351972983202413152 <|-- C_0007640742442325463418
|
||||
|
||||
C_0015654113248178830206 ..> C_0013393078277439680282
|
||||
C_0016351972983202413152 <|-- C_0015654113248178830206
|
||||
|
||||
C_0015881931090842884611 ..> C_0013393078277439680282
|
||||
C_0015881931090842884611 o-- C_0016351972983202413152 : #models
|
||||
C_0016351972983202413152 <|-- C_0015881931090842884611
|
||||
@@ -552,22 +641,29 @@ C_0015881931090842884611 <|-- C_0001410789567057647859
|
||||
|
||||
C_0015881931090842884611 <|-- C_0006288892608974306258
|
||||
|
||||
C_0005895723015084986588 <|-- C_0013562609546004646591
|
||||
|
||||
C_0009819322948617116148 --> C_0013562609546004646591 : #featureSelector
|
||||
C_0015881931090842884611 <|-- C_0009819322948617116148
|
||||
|
||||
C_0003898187834670349177 ..> C_0013393078277439680282
|
||||
C_0015881931090842884611 <|-- C_0003898187834670349177
|
||||
|
||||
C_0017759964713298103839 <|-- C_0003898187834670349177
|
||||
|
||||
C_0005895723015084986588 <|-- C_0013562609546004646591
|
||||
|
||||
C_0009819322948617116148 ..> C_0016351972983202413152
|
||||
C_0009819322948617116148 --> C_0013562609546004646591 : #featureSelector
|
||||
C_0015881931090842884611 <|-- C_0009819322948617116148
|
||||
|
||||
C_0000272055465257861326 ..> C_0013393078277439680282
|
||||
C_0009819322948617116148 <|-- C_0000272055465257861326
|
||||
|
||||
C_0002867772739198819061 ..> C_0013393078277439680282
|
||||
C_0009819322948617116148 <|-- C_0002867772739198819061
|
||||
|
||||
C_0008480973840710001141 ..> C_0013393078277439680282
|
||||
C_0009819322948617116148 <|-- C_0008480973840710001141
|
||||
|
||||
C_0005198482342493966768 ..> C_0013393078277439680282
|
||||
C_0009819322948617116148 <|-- C_0005198482342493966768
|
||||
|
||||
C_0013562609546004646591 <|-- C_0000093018845530739957
|
||||
|
||||
C_0013562609546004646591 <|-- C_0001157456122733975432
|
||||
|
File diff suppressed because one or more lines are too long
Before Width: | Height: | Size: 196 KiB After Width: | Height: | Size: 229 KiB |
@@ -162,7 +162,7 @@ TEST_CASE("Bisection Best", "[BoostA2DE]")
|
||||
{"maxTolerance", 3},
|
||||
{"convergence", true},
|
||||
{"block_update", false},
|
||||
{"convergence_best", false},
|
||||
{"convergence_best", true},
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 480);
|
||||
|
Reference in New Issue
Block a user