Machine Learning with C++

Currently, the code contains a minor CLI for training and testing mlpack's Linear Regression and Logistic Regression classes.

There is example data located in /world_happiness_data.

Prepare Your Data

Assuming your data is already in CSV format, you can use the csv.py file to clean your CSV file(s).

The CLI expects CSV data for Linear and Logistic Regression models.

Checkout the example data in /world_happiness_data for more context.

Use the CLI

Any command is a type of action followed by a type of model, and then the respective required parameters.

Actions:

• Train a model with -trm.
• Test a model with -tem.

Models:

• Use a Linear Regression model with -lr.
• Use a Logistic Regression model with -lor.

Parameters:

Linear Regression:

1. : A folder containing your CSV files.
2. : Your CSV file.
3. <model_name>: The name of your model. (When you save a model it uses this name for the .bin file).
4. <important_column: integer>: The index of the dependent variable for your Linear Regression model (the 'correct' answers) inside your CSV file.
5. *<lambda: integer>: Only required when training.

Logistic Regression:

1. : A folder containing your CSV files.
2. : Your CSV file.
3. <model_name>: The name of your model. (When you save a model it uses this name for the .bin file).
4. *<lambda: integer>: Only required when training.

Examples:

Linear Regression

Train a Linear Regression model:

a.exe -trm -lr world_happiness_data 2020a.csv 2020_lr_model 0 25

Test a Linear Regression model:

Here we use the same model name as before, but update our data to a test set, 2020b.csv. We also remove the lambda parameter.

a.exe -tem -lr world_happiness_data 2020b.csv 2020_lr_model 0

Logistic Regression

Train a Logistic Regression model:

Here 2020a identifies the prefix for a properly initialized Logistic Regression dataset, where there is a file called 2020a.csv and another file called 2020a_threshold.csv. The 'threshold file' contains one row with the same amount of columns as there are rows in 2020a.csv, where each column holds a value (0 or 1) representing correct/incorrect or happy/unhappy in our case.

a.exe -trm -lor -trm -lor world_happiness_data 2020a 2020_lor_model 25

Test a Logistic Regression model:

a.exe -trm -lor -trm -lor world_happiness_data 2020b 2020_lor_model

Citations

Conrad Sanderson and Ryan Curtin.
Armadillo: An Efficient Framework for Numerical Linear Algebra.
International Conference on Computer and Automation Engineering, 2025.

Conrad Sanderson and Ryan Curtin.
Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation.
Mathematical and Computational Applications, Vol. 24, No. 3, 2019.