def cluster(self, points, kernel_bandwidth, iteration_callback=None):
if iteration_callback:
iteration_callback(points, 0)
shift_points = np.array(points)
max_min_dist = 1
iteration_number = 0
still_shifting = [True] * points.shape[0]
while max_min_dist > MIN_DISTANCE:
# print max_min_dist
max_min_dist = 0
iteration_number += 1
for i in range(0, len(shift_points)):
if not still_shifting[i]:
continue
p_new = shift_points[i]
p_new_start = p_new
p_new = self._shift_point(p_new, points, kernel_bandwidth)
dist = ms_utils.euclidean_dist(p_new, p_new_start)
if dist > max_min_dist:
max_min_dist = dist
if dist < MIN_DISTANCE:
still_shifting[i] = False
shift_points[i] = p_new
if iteration_callback:
iteration_callback(shift_points, iteration_number)
point_grouper = pg.PointGrouper()
group_assignments = point_grouper.group_points(shift_points.tolist())
return MeanShiftResult(points, shift_points, group_assignments)
def _distance_to_group(self, point, group):
min_distance = sys.float_info.max
for pt in group:
dist = ms_utils.euclidean_dist(point, pt)
if dist < min_distance:
min_distance = dist
return min_distance
