7.1 kmeans configure
The kmeans package is Python based and utilises the system application ffmpeg for generating a video of the algorithm in action. Python libraries utilised include numpy, matplotlib, pandas, and scikit-learn. On a Ubuntu system the first three can be system installed as python3-numpy, python3-matplotlib, and python3-pandas. The scikit-learn library can be installed from PyPI. For MacOS they can all be installed from PyPI.
ml configure kmeans
Output will be something like:
*** The following required system packages are already installed: ffmpeg *** The following required system packages are already installed: python3-numpy python3-matplotlib python3-pandas *** The following required pip packages are already installed: scikit-learn *** Downloading required files ... To view the model's README: ml readme kmeans
Python and support files are downloaded from GitHub as specified in the MLHUB.yaml file.
meta: name : kmeans title : An animation demonstration for the kmeans clustering keywords : python, visualisation, clustering languages : py license : gpl3 author : Gefei Shan, Anita Williams url : https://github.com/acwkayon/kmeans dependencies: system: - ffmpeg python3: - numpy - matplotlib - pandas pip3: - scikit-learn - plotnine files: - README.md - demo.py - train.py - predict.py - utils.py - iris.csv commands: demo : Demonstrate the model capabilities in one easy session. train : Training the kmeans model on a given csv data file. predict : Label the dataset with a provided model file (csv for cluster centres). normalise : Remap numeric input csv columns using z-score. visualise : Generate static plot of the final clustering.
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