def cv(data, probs):
cluster_cnt = data.cluster_cnt * 3
return Pipe() \
.x(data.X) \
.y(data.Y) \
.pipe(badness_denclue(bandwidth=data.bandwidth, prepare=True)) \
.split(len(probs), seeder(probs)) \
.pipe(badness_denclue()) \
.split(10, cross('y_seed')) \
.connect(kmeans_ssl(cluster_cnt, data.K_for_KNN)) \
.merge('evaluation', total('evaluation')) \
.merge('result', group('evaluation', 'badness_denclue', 'name')) \
.connect(stop())
def cv(data, prob):
cluster_cnt = data.cluster_cnt * 3
return Pipe() \
.x(data.X) \
.y(data.Y) \
.pipe(badness_agglomeratvie_l_method(prepare=True)) \
.split(random_cnt) \
.y_seed(seeding_random(prob)) \
.pipe(badness_agglomeratvie_l_method()) \
.split(10, cross('y_seed')) \
.connect(kmeans_ssl(cluster_cnt, data.K_for_KNN)) \
.merge('evaluation', total('evaluation')) \
.merge('result', group('evaluation', 'badness')) \
.connect(stop())
