Multilinear

.github/workflows/ci.yml

@@ -35,12 +35,12 @@ jobs:
 
     - name: Configure with CMake
       working-directory: ./build
-      run: cmake ..
+      run: cmake -DCMAKE_BUILD_TYPE=Debug ..
 
     - name: Build
       working-directory: ./build
-      run: cmake --build .
+      run: cmake --build . --config Debug
 
     - name: Run tests
       working-directory: ./build
-      run: ctest --output-on-failure
+      run: ctest -C Debug --output-on-failure

.gitignore

@@ -37,3 +37,6 @@ docs/doxygen/
 .DS_Store
 Thumbs.db
 .cache/
+
+# Remove solution
+cpp-ml/

CMakeLists.txt

@@ -7,14 +7,15 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
 # Option to build examples (enabled by default)
 option(BUILD_EXAMPLES "Build examples" ON)
 
+# Global include directories for headers
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml_library_include)
+
 # Source files
-file(GLOB_RECURSE SOURCES "src/*.cpp")
+file(GLOB_RECURSE SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp")
 
 # Define the library target
 add_library(cpp_ml_library STATIC ${SOURCES})
-
-# Include directory for headers
-target_include_directories(cpp_ml_library PUBLIC ${PROJECT_SOURCE_DIR}/ml_library_include)
+target_include_directories(cpp_ml_library PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/ml_library_include)
 
 # Installation
 install(TARGETS cpp_ml_library DESTINATION lib)
@@ -23,33 +24,41 @@ install(DIRECTORY ml_library_include/ DESTINATION include)
 # Enable testing
 enable_testing()
 
-# Add Catch2 for testing
-include(FetchContent)
-FetchContent_Declare(
-    Catch2
-    GIT_REPOSITORY https://github.com/catchorg/Catch2.git
-    GIT_TAG v3.0.1  # Use the latest stable release of Catch2 v3 for improved compatibility
-)
-FetchContent_MakeAvailable(Catch2)
-
-# Add tests with Catch2 v3
-file(GLOB_RECURSE TEST_SOURCES "tests/**/*.cpp")
-foreach(TEST_SOURCE ${TEST_SOURCES})
-    get_filename_component(TEST_NAME ${TEST_SOURCE} NAME_WE)
-    add_executable(${TEST_NAME} ${TEST_SOURCE})
-    target_link_libraries(${TEST_NAME} cpp_ml_library Catch2::Catch2WithMain)
-    target_include_directories(${TEST_NAME} PUBLIC ${PROJECT_SOURCE_DIR}/ml_library_include)
-    add_test(NAME ${TEST_NAME} COMMAND ${TEST_NAME})
-endforeach()
+# Add individual test files as separate executables and define a macro for each
+add_executable(LogisticRegressionTest tests/regression/LogisticRegressionTest.cpp)
+target_compile_definitions(LogisticRegressionTest PRIVATE TEST_LOGISTIC_REGRESSION)
+target_link_libraries(LogisticRegressionTest cpp_ml_library)
+
+add_executable(PolynomialRegressionTest tests/regression/PolynomialRegressionTest.cpp)
+target_compile_definitions(PolynomialRegressionTest PRIVATE TEST_POLYNOMIAL_REGRESSION)
+target_link_libraries(PolynomialRegressionTest cpp_ml_library)
+
+add_executable(MultiLinearRegressionTest tests/regression/MultilinearRegressionTest.cpp)
+target_compile_definitions(MultiLinearRegressionTest PRIVATE TEST_MULTILINEAR_REGRESSION)
+target_link_libraries(MultiLinearRegressionTest cpp_ml_library)
+
+# Register individual tests
+add_test(NAME LogisticRegressionTest COMMAND LogisticRegressionTest)
+add_test(NAME PolynomialRegressionTest COMMAND PolynomialRegressionTest)
+add_test(NAME MultiLinearRegressionTest COMMAND MultiLinearRegressionTest)
 
 # Add example executables if BUILD_EXAMPLES is ON
 if(BUILD_EXAMPLES)
-    file(GLOB_RECURSE EXAMPLE_SOURCES "examples/*.cpp")
+    file(GLOB_RECURSE EXAMPLE_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/examples/*.cpp")
     foreach(EXAMPLE_SOURCE ${EXAMPLE_SOURCES})
         get_filename_component(EXAMPLE_NAME ${EXAMPLE_SOURCE} NAME_WE)
         set(EXAMPLE_TARGET "example_${EXAMPLE_NAME}") # Add a prefix to the executable name
         add_executable(${EXAMPLE_TARGET} ${EXAMPLE_SOURCE}) # Use prefixed name for executable
         target_link_libraries(${EXAMPLE_TARGET} cpp_ml_library)
-        target_include_directories(${EXAMPLE_TARGET} PUBLIC ${PROJECT_SOURCE_DIR}/ml_library_include)
+        target_include_directories(${EXAMPLE_TARGET} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/ml_library_include)
+
+        # Define specific macros to control main() inclusion if necessary
+        if(EXAMPLE_NAME STREQUAL "LogisticRegressionExample")
+            target_compile_definitions(${EXAMPLE_TARGET} PRIVATE TEST_LOGISTIC_REGRESSION)
+        elseif(EXAMPLE_NAME STREQUAL "MultilinearRegressionExample")
+            target_compile_definitions(${EXAMPLE_TARGET} PRIVATE TEST_MULTILINEAR_REGRESSION)
+        elseif(EXAMPLE_NAME STREQUAL "PolynomialRegressionExample")
+            target_compile_definitions(${EXAMPLE_TARGET} PRIVATE TEST_POLYNOMIAL_REGRESSION)
+        endif()
     endforeach()
 endif()

README.md

@@ -58,8 +58,10 @@ To use this library in your C++ project, include the master header file:
 The following machine learning algorithms are planned, inspired by concepts and techniques taught in the Udemy course:
 
 1. **Regression**
-   - [ ] Polynomial Regression
-   - [ ] Multi-Linear Regression
+   - [x] Polynomial Regression
+   - [x] Multi-Linear Regression
+   - [x] Logistic Regression
+
 
 2. **Classification**
    - [ ] Decision Tree Classifier
@@ -85,8 +87,9 @@ The following machine learning algorithms are planned, inspired by concepts and
 
 | Algorithm Category       | Algorithm                    | Implemented | Tests | Examples |
 |--------------------------|------------------------------|-------------|-------|----------|
-| **Regression**           | Polynomial Regression        | [ ]         | [ ]   | [ ]      |
-|                          | Multi-Linear Regression      | [ ]         | [ ]   | [ ]      |
+| **Regression**           | Polynomial Regression        | [x]         | [ ]   | [x]      |
+|                          | Logistic Regression      | [x]         | [ ]   | [x]      |
+|                          | Multi-Linear Regression      | [x]         | [ ]   | [x]      |
 | **Classification**       | Decision Tree Classifier     | [ ]         | [ ]   | [ ]      |
 |                          | Random Forest Classifier     | [ ]         | [ ]   | [ ]      |
 |                          | K-Nearest Neighbors          | [ ]         | [ ]   | [ ]      |

examples/LogisticRegressionExample.cpp

@@ -0,0 +1,41 @@
+#include "../ml_library_include/ml/regression/LogisticRegression.hpp"
+#include <iostream>
+#include <vector>
+#include <cmath>
+
+// Test function for Logistic Regression
+void testLogisticRegression() {
+    LogisticRegression model(0.1, 1000);
+
+    std::vector<std::vector<double>> features = {
+        {0.0, 0.0},
+        {1.0, 1.0},
+        {1.0, 0.0},
+        {0.0, 1.0}
+    };
+
+    std::vector<int> labels = { 0, 1, 1, 0 };
+
+    model.train(features, labels);
+
+    std::vector<double> testInput = { 1.0, 1.0 };
+    int predictedClass = model.predict(testInput);
+
+    std::cout << "Predicted class for {1.0, 1.0}: " << predictedClass << std::endl;
+
+    // Optionally add a check to verify the predicted class
+    if (predictedClass == 1) {
+        std::cout << "Test passed: Correct prediction." << std::endl;
+    }
+    else {
+        std::cerr << "Test failed: Prediction was " << predictedClass << ", expected 1." << std::endl;
+    }
+}
+
+// Only include main if TEST_LOGISTIC_REGRESSION is defined
+#ifdef TEST_LOGISTIC_REGRESSION
+int main() {
+    testLogisticRegression();
+    return 0;
+}
+#endif

examples/MultilinearRegressionExample.cpp

@@ -0,0 +1,49 @@
+#include "../ml_library_include/ml/regression/MultiLinearRegression.hpp"
+#include <vector>
+#include <iostream>
+#include <cmath>
+
+// Helper function for approximate equality check
+inline bool approxEqual(double a, double b, double tolerance = 0.1) {
+    return std::fabs(a - b) < tolerance;
+}
+
+void testMultilinearRegression() {
+    MultilinearRegression model(0.01, 1000);
+
+    std::vector<std::vector<double>> features = {
+        {1.0, 2.0},
+        {2.0, 3.0},
+        {3.0, 4.0},
+        {4.0, 5.0}
+    };
+
+    std::vector<double> target = { 3.0, 5.0, 7.0, 9.0 };
+
+    try {
+        model.train(features, target);
+        std::cout << "Training passed." << std::endl;
+    }
+    catch (...) {
+        std::cerr << "Training failed with an exception!" << std::endl;
+        return;
+    }
+
+    std::vector<double> testFeatures = { 5.0, 6.0 };
+    double prediction = model.predict(testFeatures);
+
+    if (approxEqual(prediction, 11.0)) {
+        std::cout << "Test passed: Prediction is within tolerance." << std::endl;
+    }
+    else {
+        std::cerr << "Test failed: Prediction is " << prediction << ", expected ~11.0." << std::endl;
+    }
+}
+
+// Only include main if TEST_MULTILINEAR_REGRESSION is defined
+#ifdef TEST_MULTILINEAR_REGRESSION
+int main() {
+    testMultilinearRegression();
+    return 0;
+}
+#endif

examples/PolynomialRegressionExample.cpp

@@ -0,0 +1,40 @@
+#include "../ml_library_include/ml/regression/PolynomialRegression.hpp"
+#include <iostream>
+#include <vector>
+#include <cmath>
+
+// Helper function for approximate equality check
+inline bool approxEqual(double a, double b, double tolerance = 0.1) {
+    return std::fabs(a - b) < tolerance;
+}
+
+// Test function for Polynomial Regression
+void testPolynomialRegression() {
+    PolynomialRegression model(2); // Quadratic regression
+
+    std::vector<double> x = { 1.0, 2.0, 3.0, 4.0 };
+    std::vector<double> y = { 3.0, 5.0, 7.0, 9.0 };
+
+    model.train(x, y);
+
+    double testInput = 5.0;
+    double prediction = model.predict(testInput);
+
+    std::cout << "Predicted value for " << testInput << ": " << prediction << std::endl;
+
+    // Check if prediction is close to the expected value
+    if (approxEqual(prediction, 11.0)) {
+        std::cout << "Test passed: Prediction is within tolerance." << std::endl;
+    }
+    else {
+        std::cerr << "Test failed: Prediction is " << prediction << ", expected ~11.0." << std::endl;
+    }
+}
+
+// Only include main if TEST_POLYNOMIAL_REGRESSION is defined
+#ifdef TEST_POLYNOMIAL_REGRESSION
+int main() {
+    testPolynomialRegression();
+    return 0;
+}
+#endif

ml_library_include/ml/ml.hpp

@@ -0,0 +1,17 @@
+// ml.h - Master header file
+#ifndef ML_H
+#define ML_H
+
+#include "tree/DecisionTreeClassifier.hpp"
+#include "tree/DecisionTreeRegressor.hpp"
+#include "tree/RandomForestClassifier.hpp"
+#include "tree/RandomForestRegressor.hpp"
+#include "regression/PolynomialRegression.hpp"
+#include "regression/MultiLinearRegression.hpp"
+#include "neural_network/ANN.hpp"
+#include "neural_network/CNN.hpp"
+#include "clustering/KMeans.hpp"
+#include "association/Apriori.hpp"
+#include "association/Eclat.hpp"
+
+#endif // ML_H