def kmedoidsWithScores(filenameData, filenameSilhMean, filenameDBS,
filenameCHS, kClusters):
data = read_sample(str(root) + '\\' + filenameData)
#kClusters = canoc(data, kmin, kmax)
initial_medoids = randomCenters(len(data), kClusters)
kmedoids_instance = kmedoids(data, initial_medoids, metric=metricResearch)
kmedoids_instance.process()
clusters = kmedoids_instance.get_clusters()
predicted = kmedoids_instance.predict(data)
silhouetteScore = silhouette(data, clusters).process().get_score()
meanSilhouetteScore = np.mean(silhouetteScore)
witTXT(meanSilhouetteScore,
filenameSilhMean,
filepath=root,
note='k: ' + str(kClusters))
dbsScore = dbs(data, predicted)
witTXT(dbsScore, filenameDBS, filepath=root, note='k: ' + str(kClusters))
chsScore = chs(data, predicted)
witTXT(chsScore, filenameCHS, filepath=root, note='k: ' + str(kClusters))
def kmedoidsWithScores(filenameData, filenameSilhMean, nameDBS, nameCHS,
kClusters, measure):
path = pathlib.Path(str(root) + '\\' + filenameData)
if path.is_file():
data = read_sample(path)
clusters, predicted = kmedoidsRun(data, kClusters, measure)
meanSilhouetteScore = meanSilh(data, clusters)
witTXT(meanSilhouetteScore,
filenameSilhMean,
filepath=root,
note=filenameData + " k: " + str(kClusters))
dbsScore = dbs(data, predicted)
witTXT(dbsScore,
nameDBS,
filepath=root,
note=filenameData + " k: " + str(kClusters))
chsScore = chs(data, predicted)
witTXT(chsScore,
nameCHS,
filepath=root,
note=filenameData + " k: " + str(kClusters))