def main():
datamat, datalabels = loadDataset("../dataset/glass.data")
print 'data ready'
nmi_max = -inf
ari_max = -inf
for i in range(10):
clusters = random.randint(2, 11)
predicted_label = AgglomerativeClustering(
linkage='average', n_clusters=clusters).fit_predict(datamat)
predicted_label = predicted_label.tolist()
nmi = normalized_mutual_info_score(datalabels, predicted_label)
ari = adjusted_rand_score(datalabels, predicted_label)
if nmi > nmi_max:
nmi_max = nmi
if ari > ari_max:
ari_max = ari
print('nmi值为：')
print(nmi_max)
print('ari值为：')
print(ari_max)
def Agglomerative(data, Genes, pathway, n_clusters):
model = AgglomerativeClustering(n_clusters,
affinity='precomputed',
linkage='complete').fit_predict(data)
GeneNames = [gene.GeneName for gene in Genes]
return model.tolist()
