This package is an example Kmeans' package for BMI500's project in Emory University. The package will automatically download the iris data collected from UCI. The dataset contains three classes of flowers, and the clustering algorithm is to seperate each group of the flowers. It does not necessary tell you what kind of flower is, but will tell you which flowers are in a same group.
In your command line, type "pip install BMI500caonia"
python
from BMI500caonia import BMI500clustering
BMI500clustering.Kmeans_run(n, iteration, random_state)
(n is the number of clusters, iteration is the number of iteration, random_state is the number of random initializations)
The running time is 14 seconds in Titan'x 12 GB gpu and Intel Iris 16 GB cpu.
The function should be modified to be more flexible in the future, so the user can customize parameters.