PCA Assignment (2D → 1D) in C++

This project demonstrates Principal Component Analysis (PCA) using Eigen Decomposition in C++.
It reduces a 2D dataset to 1D and visualizes the result using matplotlib-cpp.

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Project Structure

• dataset.hpp → Generate 2D dataset
• center.hpp → Center data
• covariance.hpp → Compute covariance matrix
• eigen.hpp → Eigen decomposition
• pca_projection.hpp → Project data & variance explained
• plot.hpp → Reconstruct & plot data
• main.cpp → Integrates all parts
• CMakeLists.txt → Build configuration

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Setup & Run

1. Clone repository and include matplotlib-cpp folder. Use git clone https://github.com/lava/matplotlib-cpp command.
2. C++ Compiler (g++ recommended, C++17 support)
3. Python 3.13 (or compatible version)
4. Python packages: numpy and matplotlib

Install Python packages:

python -m pip install numpy matplotlib

```bash
mkdir build && cd build
cmake ..
make

## Run 

./PCA_Assignment