test += 1
aux_clusters[curr_indx].remove(k)
aux_clusters[pred_idx].append(k)
self.obj_to_cluster[k] = pred_idx
self.clusters = aux_clusters
return test
def objective_function(self):
print("calculating J_kcm_f_gh...")
result = 0.
self.part2 = self.__all_against_all_cluster_sum()
for i in range(self.c):
for k in self.clusters[i]:
result += 1 - self.__object_against_cluster_sum(k, i) + self.part2[i]
return result
def rand_score(self):
return adjusted_rand_score(labels_true=self.y,
labels_pred=self.obj_to_cluster.values())
if __name__ == "__main__":
import pandas as pd
from load_data import Dataset
datadir='../../data/segmentation_2.test'
df = pd.read_csv(datadir, sep=',')
mydata = Dataset()
mydata.load(df, 'rgb')
kcm = KCM_F_GH(c=7, p=mydata.X.values.shape[1], data=mydata)
if __name__ == '__main__':
datadir = '../../data/segmentation_2.test'
result_dir = '../../results/clustering'
result_file = 'results'
bresult_file = 'best_result'
view = 'rgb'
norm = True
result_file = '{}{}_{}'.format(view, '_norm' if norm else '', result_file)
bresult_file = '{}{}_{}'.format(view, '_norm' if norm else '',
bresult_file)
df = pd.read_csv(datadir, sep=',')
mydata = Dataset()
mydata.load(df, view)
# Variáveis para armazenar resultado da execução do algoritmo
res_obj_function = []
res_cluster = []
res_obj_to_cluster = []
res_hp = []
res_ari = [] # adjusted rand indexes list
res_J = [] # uma lista com a melhor serie de convergência de J
# executar o algoritmo 100x
for epoch in range(5):
start_total_time = time.time()
# inicialização do algoritmo
kcm = KCM_F_GH(c=7, p=mydata.X.shape[1], data=mydata, norm=norm)
kcm.initialization()
# parte iterativa do algoritmo