def main():
# PARSE ARGS
data_filename = None
header_filename = None
k = None
distance_metric = None
parser = lab5.get_k_means_args()
args = parser.parse_args()
data_filename = args.csv_filename
if args.header_filename:
header_filename = args.header_filename
elif args.infer_header:
header_filename = get_header_filename(data_filename)
k = args.k
distance_metric = get_distance_metric(args)
print("Data Filename: %s" % data_filename)
print("Header Filename: %s" % header_filename)
print("k : %d" % k)
# READ DATA
dataset = Dataset(data_filename, header_filename)
# CALC K MEANS
k_means = KMeans(distance_metric)
centroids, clusters = k_means.disk_k_means(dataset, k)
assert len(centroids) == len(clusters) and len(clusters) == k
num = print_stats(dataset, k_means, clusters, centroids)
assert num == dataset.size(), "num(%d) != D.size(%d)" % (num, dataset.size())
print("Datapoints clustered: %d" % num)
print("Datapoints total : %d" % dataset.size())
return 0
def main():
# PARSE ARGS
data_filename = None
header_filename = None
threshold = None
distance_metric = None
cluster_distance = None
parser = lab5.get_hierarchical_args()
args = parser.parse_args()
data_filename = args.csv_filename
if args.header_filename:
header_filename = args.header_filename
elif args.infer_header:
header_filename = get_header_filename(data_filename)
threshold = args.threshold
distance_metric = get_distance_metric(args)
cluster_distance = get_cluster_distance(args)
print('Data Filename: %s' % data_filename)
print('Header Filename: %s' % header_filename)
print('Threshold : %.3f' % threshold) if threshold else None
# READ DATA
dataset = Dataset(data_filename, header_filename)
#for d in dataset:
# print(d)
# CALC AGGLOMERATIVE
agglomerative = Agglomerative(distance_metric, cluster_distance)
dendrogram = agglomerative.agglomerative(dataset)
all_clusters = get_all_clusters(dendrogram)
if threshold:
trimmed_tree, centroids, clusters = get_clusters(dendrogram, threshold)
for c in clusters:
print(c)
print(len(clusters))
print_tree(trimmed_tree)
num_datapoints = print_stats(dataset, agglomerative, clusters, centroids)
print('Datapoints clustered: %d' % num_datapoints)
print('Datapoints total : %d' % dataset.size())
else:
print_tree(dendrogram)
return 0
