def test_hclust(self):
x_vals = numpy.random.rand(10)
y_vals = numpy.random.rand(10)
xys = zip(x_vals, y_vals)
distance = 0.2
clusters = cluster_r.hclust(xys, distance, "single")
parser.add_argument('--clusters', '-c', action='store_true', default=False, help='output points')
args = parser.parse_args()
# convert csv to array of float tuples
rows = csv.reader(args.input)
pts = map(lambda row: (float(row[0]), float(row[1])), rows)
if(len(pts) < 1):
sys.stderr.write("No input points found")
exit()
# convert meters to kilometers since hclust assumes km
km = args.distance / 1000.0
clusters = cluster_r.hclust(pts, km, args.method)
clusters.sort(key=len, reverse=True)
centroids = []
for cluster in clusters:
unzipped = zip(*cluster)
centroids.append(util.points_to_centroid(unzipped))
if args.clusters:
print('clusters')
for cluster in clusters:
print cluster
if args.centroids:
print('centroids')
for centroid in centroids:
print centroid
