Link to the package documentation: http://ugtm.readthedocs.io/en/latest/

GTM (Generative Topographic Mapping) is a dimensionality reduction algorithm (as t-SNE, LLE, etc) created by Bishop et al. (https://www.microsoft.com/en-us/research/wp-content/uploads/1998/01/bishop-gtm-ncomp-98.pdf) and a probabilistic counterpart of Kohonen maps.

ugtm is a python package implementing GTM and GTM prediction algorithms. ugtm contains the core functions and runGTM.py (in bin directory) is an easy-to-use program. The kernel version of the algorithm (kGTM) is also implemented. You can also generate regression or classification maps, or evaluate the predictive accuracy (classification) or RMSE/R2 (regression) in repeated cross-validation experiments.

Simple installation:

• pip install ugtm

If you get error messages, try upgrading packages:

• pip install --upgrade pip numpy scikit-learn matplotlib scipy mpld3 jinja2
• sudo pip install --upgrade pip numpy scikit-learn matplotlib scipy mpld3 jinja2

If you have problems with anaconda packages, try to create a virtual env called "p2" for python 2.7.14:

• conda create -n p2 python=2.7.14 numpy=1.14.5 scikit-learn=0.20 matplotlib=2.2.2 scipy=0.19.1 mpld3=0.3 jinja2=2.10
• source activate p2
• pip install ugtm

Or p3 for python 3.6.6:

• conda create -n p3 python=3.6.6 numpy=1.14.5 scikit-learn=0.20 matplotlib=2.2.2 scipy=0.19.1 mpld3=0.3 jinja2=2.10
• source activate p3
• pip install ugtm

Readthedocs

Python 2.7 or + (tested on Python 3.4.6 and Python 2.7.14)

and following packages:

• scikit-learn>=0.20
• numpy>=1.14.5
• matplotlib>=2.2.2
• scipy>=0.19.1
• mpld3>=0.3
• jinja2>=2.10

Cite ugtm version and the following paper:

@ARTICLE{Gaspar2018-qt,
  title   = "ugtm: A Python Package for Data Modeling and Visualization Using
             Generative Topographic Mapping",
  author  = "Gaspar, H{\'e}l{\'e}na Alexandra",
  journal = "Journal of Open Research Software",
  volume  =  6,
  pages   = "215",
  month   =  dec,
  year    =  2018
}

Héléna A. Gaspar, [email protected], https://github.com/hagax8