def meanshift(data, r): labels = np.zeros(len(data.T)) peaks = [] #聚集的类中心 label_no = 1 #当前label labels[0] = label_no # findpeak is called for the first index out of the loop peak = findpeak(data, 0, r) peakT = np.concatenate(peak, axis=0).T peaks.append(peakT) # Every data point is iterated through for idx in range(1, len(data.T)): # 遍历数据，寻找当前点的peak # 并实时关注当前peak是否会收敛到一个新的聚类（和已有peaks比较） # 若是，更新label_no，peaks，labels，继续 # 若不是，当前点就属于已有类，继续 ### YOUR CODE HERE

### 以下是完整的 Python 代码实现

def meanshift(data, r):
labels = np.zeros(len(data.T))
peaks = [] #聚集的类中心
label_no = 1 #当前label
labels[0] = label_no

# findpeak is called for the first index out of the loop
peak = findpeak(data, 0, r)
peakT = np.concatenate(peak, axis=0).T
peaks.append(peakT)

# Every data point is iterated through
for idx in range(1, len(data.T)):
# 遍历数据，寻找当前点的peak
peak = findpeak(data, idx, r)
peakT = np.concatenate(peak, axis=0).T

# 实时关注当前peak是否会收敛到一个新的聚类（和已有peaks比较）
found_peak = False
for i in range(len(peaks)):
# 如果当前peak已经属于一个已有聚类，就将当前点分配到该聚类
if np.linalg.norm(peakT - peaks[i]) < r:
labels[idx] = i+1
found_peak = True
break

# 如果当前peak没有收敛到任何一个已有聚类，就创建新的聚类并将当前点分配到该聚类
if not found_peak:
label_no += 1
peaks.append(peakT)
labels[idx] = label_no

return labels

### 该函数实现了 Mean Shift 算法中的聚类过程，其中 data 是输入的数据矩阵，r 是半径参数。该函数返回一个标签向量，表示每个数据点所属的聚类编号。