Dunn and Davius Bouldin indices are implemented. It follows the equations presented in theory.pdf.
base.py : Python + NumPy
basec.pyx : Python + NumPy optimized with Cython
basec.pyx tested in Windows 8.1 x64, Python 3.4 and compiled with VS2010 (python setup.py build_ext -i)
dunn(k_list):
Slow implementation of Dunn index that depends on numpy
-- basec.pyx Cython implementation is much faster but slower than dunn_fast()
""" Dunn index [CVI]
Parameters
----------
k_list : list of np.arrays
A list containing a numpy array for each cluster |c| = number of clusters
c[K] is np.array([N, p]) (N : number of samples in cluster K, p : sample dimension)
"""dunn_fast(points, labels):
Fast implementation of Dunn index that depends on numpy and sklearn.pairwise
-- No Cython implementation
""" Dunn index - FAST (using sklearn pairwise euclidean_distance function)
Parameters
----------
points : np.array
np.array([N, p]) of all points
labels: np.array
np.array([N]) labels of all points
"""davisbouldin(k_list, k_centers):
Implementation of Davis Boulding index that depends on numpy
-- basec.pyx Cython implementation is much faster
""" Davis Bouldin Index
Parameters
----------
k_list : list of np.arrays
A list containing a numpy array for each cluster |c| = number of clusters
c[K] is np.array([N, p]) (N : number of samples in cluster K, p : sample dimension)
k_centers : np.array
The array of the cluster centers (prototypes) of type np.array([K, p])
"""Installation
python setup.py install