def kmeans(features, projection, ite = 50, k = 4, threshold = 1e-5):
""" perform k_keamns clustering and return a the result as a subsapce clustering object """
from scipy.cluster.vq import kmeans, vq
import datetime
from measures import spatial_coherence
centroids, distance = kmeans(features, k, iter=ite, thresh=threshold)
code, _ = vq(features, centroids)
run_ = datetime.datetime.now().strftime("%y_%m_%d_%H_%M")
params = "projection_size=%d, k=%d" %(len(projection), k)
clusters = clusters_from_code(code, k, projection)
clustering_id = "(%s)_(%s)_(%s)_(%s)" %("exhaustive_kmeans", params, run_, projection)
#print clustering_id
km_clt = KMClustering(algorithm ="exhaustive_kmeans", parameters = params, run = run_,
clustering_id = clustering_id, clusters = clusters, ccontains_noise = False, cclustering_on_dimension = True)
measures = {'spatial_coherence': spatial_coherence(km_clt, len(features))[0], 'distortion': distance}
km_clt.update_measures(measures)
return km_clt
def kmeans(features, projection, ite = 50, k = 4, threshold = 1e-5):
""" perform k_keamns clustering and return a the result as a subsapce clustering object """
from scipy.cluster.vq import kmeans, vq
import datetime
from measures import spatial_coherence
#features = dims_arg
centroids, distance = kmeans(features, k, iter=ite, thresh=threshold)
code, _ = vq(features, centroids)
run_ = datetime.datetime.now().strftime("%y_%m_%d_%H_%M")
params = "projection_size=%d, k=%d" %(len(projection), k)
km_clt = KMClustering(algorithm ="exhaustive_kmeans", parameters = params, run = run_, clustering_id = None)
clusters = clusters_from_code(code, k, projection)
for cluster in clusters:
km_clt.add_cluster(cluster)
measures = {'spatial_coherence': spatial_coherence(km_clt, len(features))[0], 'distortion': distance}
km_clt.update_measures(measures)
return km_clt
