超过 2 个维度的 Kmeans

Question

我有自己的算法来分类 kmeans

# parameter c is for how many cluster do you want
def kmeans(data, c, iter, state):
  np.random.seed(state)

  m=data.shape[0] #number of training examples 
  n=data.shape[1] #number of features 
  Centroids=np.array([]).reshape(n,0)

  for i in range(c):
    rand=np.random.randint(0,m-1)
    Centroids=np.c_[Centroids,data[rand]]
  
  result = {}

  for i in range(iter):      
      distance=np.array([]).reshape(m,0) #Euclidian distance
      for k in range(c):
          tempDist=np.sum((data-Centroids[:,k])**2,axis=1)
          distance=np.c_[distance,tempDist]
      C=np.argmin(distance,axis=1)+1
     
      Y={}
      for k in range(c):
          Y[k+1]=np.array([]).reshape(2,0)
      for i in range(m):          
          Y[C[i]]=np.c_[Y[C[i]],X[i]]     
      for k in range(c):
          Y[k+1]=Y[k+1].T
      for k in range(c):
          Centroids[:,k]=np.mean(Y[k+1],axis=0)
      result=Y

  return result

我测试了我的代码以对二维数据的 kmeans 进行分类并且它成功了

# This 2 dimensional data is just for example
Xd = [[7,0],[0,3],[3,4],[4,6],[7,1],[2,4]]
Z=kmeans(Xd,3,500,0)
print(Z)
>>>Z = {1: [[7,0],[7,1]], 
        2: [[3,4],[2,4]],
        3: [[0,3],[4,6]]}

但是当我用 784 维的变量替换 Xd 时，它在这一行显示错误：

Y[C[i]]=np.c_[Y[C[i]],X[i]]  
>>>all the input array dimensions for the concatenation axis must match exactly, 
but along dimension 0, the array at index 0 has size 2 and the array at index 1 has size 784

我该怎么办？

Answer 1

Y[k+1]=np.array([]).reshape(2,0)

而不是 2，此数组应匹配您正在连接的一个。

超过 2 个维度的 Kmeans

Kmeans on more than 2 dimensions

python

arrays

machine-learning

cluster-analysis

k-means