MC_NDCC

Multi-Class Normal Distribution Cubic Clusters Dataset Generator

Description

MC_NDCC is a data generator that creates synthetic datasets with multiple classes using normally distributed clusters. It generates random centers for multivariate normal distributions, assigns class labels based on separating planes, and randomly generates data points from these distributions.

Features

• Generate multi-class datasets with customizable number of samples, features, and classes
• Random center generation for multivariate normal distributions
• Automatic class assignment based on separating planes
• Support for Python and MATLAB implementations

Requirements

Python

• numpy
• pandas

MATLAB

• MATLAB R2019b or later

Usage

Python

from MC_NDCC import MC_NDCC

# Initialize an instance (will prompt for inputs)
ndcc = MC_NDCC()

# Get the dataset as a numpy matrix
dataset = ndcc.get_matrix()

# Save the dataset as a CSV file
ndcc.get_csv('dataset.csv')

MATLAB

1. Run MC_NDCC_matlab/NDCC.m
2. Enter the number of samples, features, and classes when prompted
3. The generated dataset will be saved as dataset.csv in the current directory

Project Structure

MC_NDCC/
├── MC_NDCC.py              # Python implementation (root)
├── MC_NDCC_python/         # Python package directory
├── MC_NDCC_matlab/         # MATLAB implementation
└── README.md               # This file

Authors

Dr. Hossein Moosaei, Saeed Khosravi

Date

10/09/2020