MOPL

Codes and datasets of our Knowledge-Based Systems paper: Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities

File Structure

.
├── README.md
├── code
│   ├── MOPL.R  # MOPL fit fuction, fit results were saved in /result
│   └── MOPL_draw.py  # draw MOPL fit results
├── data  # raw data, no fit yet
│   ├── different_languages/
│   └── different_types/
├── image  # MOPL fit graph
│   ├── different_types/
│   └── differnet_languages/
└── result
    ├── MOPL_languages.csv  # MOPL fit parameters
    ├── MOPL_types.csv  # MOPL fit parameters
    ├── different_languages/  # MOPL fit results
    └── different_types/. # MOPL fit results

Requirements:

Python:

python -m pip install -U pip
python -m pip install -U matplotlib
pip install numpy

R:

install.packages("zipfextR")
install.packages("ggplot2")

Run

1. Run MOPL.R
2. Run MOPL_draw.py

Publication

Xiaoshi Zhong, Xiang Yu, Erik Cambria, and Jagath C. Rajapakse. Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities. To appear in Knowledge-Based Systems, 2023.