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
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 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 cluster(self,
points,
kernel_bandwidth,
pressureWeights,
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]
old_max_min_dist = max_min_dist + 1
while max_min_dist > MIN_DISTANCE and np.linalg.norm(
old_max_min_dist -
max_min_dist) > 0.0001 and iteration_number < 50:
print('old_max_min_dis is ' + str(old_max_min_dist))
print('max_min_dis is ' + str(max_min_dist))
print('diff is ' +
str(np.linalg.norm(old_max_min_dist - max_min_dist)))
# print max_min_dist
old_max_min_dist = 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,
pressureWeights)
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 cluster_cell(self, points, kernel_bandwidth, iteration_callback=None):
if (iteration_callback):
iteration_callback(points, 0)
#将这些点的集合变成np的数组
shift_points = np.array(points)
#最大最小距离？
max_min_dist = 1
iteration_number = 0
#shape[0] 代表行数，[true........] 多少个点就多少个，这就是个标志位
still_shifting = [True] * points.shape[0]
#应该是这个最小的欧氏距离小到一定的程度，我就停止移位我的点
while max_min_dist > MIN_DISTANCE:
# print max_min_dist
max_min_dist = 0
iteration_number += 1
#这个小循环遍历每一个点集合X
for i in range(0, len(shift_points)):
if not still_shifting[i]: #标志位，如果没有漂移过
continue
p_new = shift_points[i]
p_new_start = p_new
#这个点已经漂移完了
p_new = self._shift_point(p_new, points, kernel_bandwidth)
#计算漂移的欧氏距离
dist = ms_utils.euclidean_dist(p_new, p_new_start)
#一轮轮得到最小的欧氏距离
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)
#此处一个基站只有一个密度中心,所以求shift_points 的均值
print "iter:"
print iteration_number
return self.getcenterpoint_from_array(shift_points)
def cluster(self, points, kernel_bandwidth, iteration_callback=None):
if (iteration_callback):
iteration_callback(points, 0)
#将这些点的集合变成np的数组
shift_points = np.array(points)
#最大最小距离？
max_min_dist = 1
iteration_number = 0
#shape[0] 代表行数，[true........] 多少个点就多少个，这就是个标志位
still_shifting = [True] * points.shape[0]
#应该是这个最小的欧氏距离小到一定的程度，我就停止移位我的点
while max_min_dist > MIN_DISTANCE:
# print max_min_dist
max_min_dist = 0
iteration_number += 1
#这个小循环遍历每一个点集合X
for i in range(0, len(shift_points)):
if not still_shifting[i]: #标志位，如果没有漂移过
continue
p_new = shift_points[i]
p_new_start = p_new
#这个点已经漂移完了
p_new = self._shift_point(p_new, points, kernel_bandwidth)
#计算漂移的欧氏距离
dist = ms_utils.euclidean_dist(p_new, p_new_start)
#一轮轮得到最小的欧氏距离
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)