In K-fold cross-validation, the aim is to generate K training/validation set pair, where training and validation sets on fold i do no overlap. First, we divide the dataset X into K parts as X1; X2; ... ; XK. Then for each fold i, we use Xi as the validation set and the remaining as the training set.

Possible values of K are 10 or 30. One extreme case of K-fold cross-validation is leave-one-out, where K = N and each validation set has only one instance. If we have more computation power, we can have multiple runs of K-fold cross-validation, such as 10 x 10 cross-validation or 5 x 2 cross-validation.

If we have very small datasets, we do not insist on the non-overlap of training and validation sets. In bootstrapping, we generate K multiple training sets, where each training set contains N examples (like the original dataset). To get N examples, we draw examples with replacement. For the validation set, we use the original dataset. The drawback of bootstrapping is that the bootstrap samples overlap more than the cross-validation sample, hence they are more dependent.

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• C++ Compiler
• Git

To check if you have compatible C++ Compiler installed,

• Open CLion IDE
• Preferences >Build,Execution,Deployment > Toolchain

Install the latest version of Git.

In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:

git clone <your-fork-git-link>

A directory called Classification-CPP will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/Classification-CPP.git

To import projects from Git with version control:

• Open CLion IDE , select Get From Version Control.

• In the Import window, click URL tab and paste github URL.

• Click open as Project.

Result: The imported project is listed in the Project Explorer view and files are loaded.

From IDE

After being done with the downloading and opening project, select Build Project option from Build menu. After compilation process, user can run TestClassification.cpp .

• CrossValidation
• Bootstrap
• KFoldCrossValidation
• StratifiedKFoldCrossValidation

k. eğitim kümesini elde etmek için

ArrayList<T> getTrainFold(int k)

k. test kümesini elde etmek için

ArrayList<T> getTestFold(int k)

Bootstrap için BootStrap sınıfı

Bootstrap(ArrayList<T> instanceList, int seed)

Örneğin elimizdeki veriler a adlı ArrayList'te olsun. Bu veriler üstünden bir bootstrap örneklemi tanımlamak için (5 burada rasgelelik getiren seed'i göstermektedir. 5 değiştirilerek farklı samplelar elde edilebilir)

bootstrap = Bootstrap(a, 5);

ardından üretilen sample'ı çekmek için ise

sample = bootstrap.getSample();

yazılır.

K kat çapraz geçerleme için KFoldCrossValidation sınıfı

KFoldCrossValidation(List<T> instanceList, int K, int seed)

Örneğin elimizdeki veriler a adlı ArrayList'te olsun. Bu veriler üstünden 10 kat çapraz geçerleme yapmak için (2 burada rasgelelik getiren seed'i göstermektedir. 2 değiştirilerek farklı samplelar elde edilebilir)

kfold = KFoldCrossValidation(a, 10, 2);

ardından yukarıda belirtilen getTrainFold ve getTestFold metodları ile sırasıyla i. eğitim ve test kümeleri elde edilebilir.

Stratified K kat çapraz geçerleme için StratifiedKFoldCrossValidation sınıfı

StratifiedKFoldCrossValidation(ArrayList<T>[] instanceLists, int K, int seed)

Örneğin elimizdeki veriler a adlı ArrayList of listte olsun. Stratified bir çapraz geçerlemede sınıflara ait veriler o sınıfın oranında temsil edildikleri için her bir sınıfa ait verilerin ayrı ayrı ArrayList'te olmaları gerekmektedir. Bu veriler üstünden 30 kat çapraz geçerleme yapmak için (4 burada rasgelelik getiren seed'i göstermektedir. 4 değiştirilerek farklı samplelar elde edilebilir)

stratified = StratifiedKFoldCrossValidation(a, 30, 4);

ardından yukarıda belirtilen getTrainFold ve getTestFold metodları ile sırasıyla i. eğitim ve test kümeleri elde edilebilir.

1. First install conan.

pip install conan

Instructions are given in the following page:

https://docs.conan.io/2/installation.html

2. Add conan remote 'ozyegin' with IP: 104.247.163.162 with the following command:

conan remote add ozyegin http://104.247.163.162:8081/artifactory/api/conan/conan-local --insert

3. Use the comman conan list to check for installed packages. Probably there are no installed packages.

conan list

1. Put the correct dependencies in the requires part

    requires = ["math/1.0.0", "classification/1.0.0"]

2. Default settings are:

    settings = "os", "compiler", "build_type", "arch"
    options = {"shared": [True, False], "fPIC": [True, False]}
    default_options = {"shared": True, "fPIC": True}
    exports_sources = "src/*", "Test/*"

    def layout(self):
        cmake_layout(self, src_folder="src")

    def generate(self):
        tc = CMakeToolchain(self)
        tc.generate()
        deps = CMakeDeps(self)
        deps.generate()

    def build(self):
        cmake = CMake(self)
        cmake.configure()
        cmake.build()

    def package(self):
        copy(conanfile=self, keep_path=False, src=join(self.source_folder), dst=join(self.package_folder, "include"), pattern="*.h")
        copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "lib"), pattern="*.a")
        copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "lib"), pattern="*.so")
        copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "lib"), pattern="*.dylib")
        copy(conanfile=self, keep_path=False, src=self.build_folder, dst=join(self.package_folder, "bin"), pattern="*.dll")

    def package_info(self):
        self.cpp_info.libs = ["ComputationalGraph"]

1. Set the C++ standard with compiler flags.

	set(CMAKE_CXX_STANDARD 20)
	set(CMAKE_CXX_FLAGS "-O3")

2. Dependent packages should be given with find_package.

	find_package(util_c REQUIRED)
	find_package(data_structure_c REQUIRED)

3. For library part, use add_library and target_link_libraries commands. Use m library for math linker in Linux.

	add_library(Math src/Distribution.cpp src/Distribution.h src/DiscreteDistribution.cpp src/DiscreteDistribution.h src/Vector.cpp src/Vector.h src/Eigenvector.cpp src/Eigenvector.h src/Matrix.cpp src/Matrix.h src/Tensor.cpp src/Tensor.h)
	target_link_libraries(Math util_c::util_c data_structure_c::data_structure_c m)

4. For executable tests, use add_executable and target_link_libraries commands. Use m library for math linker in Linux.

	add_executable(DiscreteDistributionTest src/Distribution.cpp src/Distribution.h src/DiscreteDistribution.cpp src/DiscreteDistribution.h src/Vector.cpp src/Vector.h src/Eigenvector.cpp src/Eigenvector.h src/Matrix.cpp src/Matrix.h src/Tensor.cpp src/Tensor.h Test/DiscreteDistributionTest.cpp)
	target_link_libraries(DiscreteDistributionTest util_c::util_c data_structure_c::data_structure_c m)

1. Add data files to the cmake-build-debug folder.

1. If needed, comparator operators == and < should be implemented for map and set data structures.

    bool operator==(const Word &anotherWord) const{
        return (name == anotherWord.name);
    }
    bool operator<(const Word &anotherWord) const{
        return (name < anotherWord.name);
    }

2. Do not forget to comment each function.

	/**
 	* A constructor of Word class which gets a String name as an input and assigns to the name variable.
	*
	* @param _name String input.
 	*/
	Word::Word(const string &_name) {

3. Function names should follow caml case.

	int Word::charCount() const

4. Write getter and setter methods.

	string Word::getName() const
	void Word::setName(const string &_name)

5. Use catch.hpp for testing purposes. Add

#define CATCH_CONFIG_MAIN  // This tells Catch to provide a main() - only do this in one cpp file

line in only one of the test files. Add

#include "catch.hpp"

line in all test files. Example test file is given below:

TEST_CASE("DictionaryTest") {
    TxtDictionary lowerCaseDictionary = TxtDictionary("lowercase.txt", "turkish_misspellings.txt");
    TxtDictionary mixedCaseDictionary = TxtDictionary("mixedcase.txt", "turkish_misspellings.txt");
    TxtDictionary dictionary = TxtDictionary();
    SECTION("testSize"){
        REQUIRE(29 == lowerCaseDictionary.size());
        REQUIRE(58 == mixedCaseDictionary.size());
        REQUIRE(62113 == dictionary.size());
    }
    SECTION("testGetWord"){
        for (int i = 0; i < dictionary.size(); i++){
            REQUIRE_FALSE(nullptr == dictionary.getWord(i));
        }
    }
    SECTION("testLongestWordSize"){
        REQUIRE(1 == lowerCaseDictionary.longestWordSize());
        REQUIRE(1 == mixedCaseDictionary.longestWordSize());
        REQUIRE(21 == dictionary.longestWordSize());
    }

6. Enumerated types should be declared with enum class.

	enum class Pos {
		ADJECTIVE,
		NOUN,
		VERB,
		ADVERB,

7. Every header file should start with

	#ifndef MATH_DISTRIBUTION_H
	#define MATH_DISTRIBUTION_H

and end with

	#endif //MATH_DISTRIBUTION_H

8. Do not forget to use const expression for parameters, if they will not be changed in the function.

	void Word::setName(const string &_name);

9. Do not forget to use const expression for methods, which do not modify any class attribute. Also use [[dodiscard]]

	[[nodiscard]] bool isPunctuation() const;

10. Use xmlparser package for parsing xml files.

    auto* doc = new XmlDocument("test.xml");
    doc->parse();
    XmlElement* root = doc->getFirstChild();
    XmlElement* firstChild = root->getFirstChild();

11. Data structures: Use map for hash map, unordered_map for linked hash map, vector for array list, unordered_set for hash set