def visualize(path, levels, threshold, ccore_enabled, **kwargs):
sample = read_sample(path)
clique_instance = clique(sample, levels, threshold, ccore=ccore_enabled)
clique_instance.process()
cells = clique_instance.get_cells()
clique_visualizer.show_grid(cells, sample)
def visualize(path, levels, threshold, ccore_enabled, **kwargs):
sample = read_sample(path)
clique_instance = clique(sample, levels, threshold, ccore=ccore_enabled)
clique_instance.process()
cells = clique_instance.get_cells()
clique_visualizer.show_grid(cells, sample)
def template_clustering(data_path, intervals, density_threshold, **kwargs):
print("Sample: '%s'." % os.path.basename(data_path))
data = read_sample(data_path)
clique_instance = clique(data, intervals, density_threshold)
clique_instance.process()
clusters = clique_instance.get_clusters()
noise = clique_instance.get_noise()
cells = clique_instance.get_cells()
print([len(cluster) for cluster in clusters])
clique_visualizer.show_grid(cells, data)
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, data)
visualizer.append_cluster(noise, data, marker='x')
visualizer.show()
intervals = 5
# Density threshold
threshold = 0
clique_instance = clique(data_values, intervals, threshold)
clique_instance.process()
clique_cluster = clique_instance.get_clusters()
noise = clique_instance.get_noise()
cells = clique_instance.get_cells()
print("Amount of clusters:", len(clique_cluster))
for cluster in clique_cluster:
print(cluster)
labelList = [0] * 200
j = 1
for cluster in clique_cluster:
for x in cluster:
labelList[x] = j
j = j + 1
labels = np.array(labelList)
clique_visualizer.show_grid(cells, data_values)
visualizer = cluster_visualizer_multidim()
visualizer.append_clusters(clique_cluster, data_values)
BETACV = betacv.betacv(data, labels)
print(BETACV)
zeros[each] = 5
# for each in clique_cluster[4]:
# zeros[each] = 1
g_answer = zeros
#print(g_answer)
sum = 0
for i in range(len(g_truth)):
if g_answer[i] == g_truth[i]:
sum += 1
rate = sum / len(g_truth)
print(rate)
#print(g_truth)
#print(g_answer)
print("Amount of clusters:", len(clique_cluster))
print(clique_cluster)
print(f"运行时间：{(t2-t1)/1e6}ms") #运行时间
# 显示由算法形成的网格
clique_visualizer.show_grid(cells, TestData)
# 显示聚类结果
clique_visualizer.show_clusters(TestData, clique_cluster,
noise) # show clustering results
# 准确率
print(f"准确率：{Coopcheckdiv(np.array(g_answer),data_M[:,0])}")