/student_poisson_mixture

Primary LanguagePythonMIT LicenseMIT

student_poisson_mixture

The code is released for the paper Understanding Student Procrastination vis Mixture Models, Educational Data Mining 2018.

Jihyun Park (jihyunp@ics.uci.edu)
July 2018

Required Packages

Written in Python2.7.
Python packages numpy, scipy, random, and matplotlib are needed to run the code.

Data

  • test_data.csv: Sample data (simulated data) to fit the Poisson mixture model. Each row in the file is considered as a daily activity count vector for a student. 400 rows exist in this sample data.

Demo iPython Notebook

  • demo.ipynb: A quick tutorial of using the code.

Code

  • pmm.py: Code for fitting Poisson mixture model given a count matrix. The file has two classes--
    PoissonMixture for the model and PoisMixResult for storing and plotting the result.
  • utils.py: Has helper functions for calculating log probabilities.