Python ValueError:找到包含1个样本（形状=（1，2））的数组，而聚合群集至少需要2个样本

我试图预测看不见的数据

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

当我试图预测看不见的数据时

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

输入收入（K$）90 输入支出分数（1-100）90 (1, 2) [[90. 90.]]' 'ValueError回溯（最近一次调用上次） 我试图预测看不见的数据

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

当我试图预测看不见的数据时

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

enter code here`
   from sklearn.cluster import AgglomerativeClustering
   hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
   y = hc.fit(X)

   X.shape
   (200,2)

   a = int(input('Enter Income (K$)'))
   b = int(input('Enter spending score (1-100)'))
   c = np.zeros((1,2))
   c[0,0] = a
   c[0,1] = b
   print(c.shape)
   print(c)
   print(type(c))
   o = y.fit_predict(c)
   if o == 4:
       Category = 'Sensible Clients'
   elif o == 3:
       Category = 'Careless Client'
   elif o == 2:
       Category = 'Target Client'
   elif o == 1:
       Category = 'Standard Client'
   else:
       Category = 'Careful Client'
   print('The client belongs to {} Category'.format(Category))

输入收入（K$）90 输入支出分数（1-100）90 (1, 2) [[90. 90.]]' 'ValueError回溯（最近一次调用上次） 在里面 7印刷品（c） 8印刷品（c类） ---->9 o=hc.拟合度（c）

c:\users\user\appdata\local\programs\python37\lib\site packages\sklearn\base.py in fit\u predict（self，X，y） 445#非优化默认实现；当一个更好的 446#方法适用于给定的聚类算法 -->447自适配（X） 448返回自助标签_ 449

c:\users\user\appdata\local\programs\python37\lib\site packages\sklearn\cluster\hierarchy.py（self，X，y） 803“它在0.20版中已被弃用，将被删除” 804'已在0.22'中删除，弃用警告） -->805 X=检查数组（X，确保最小样本数=2，估计器=self） 806内存=检查内存（自内存） 807

检查数组中的c:\users\user\appdata\local\programs\python37\lib\site packages\sklearn\utils\validation.py（数组，接受稀疏，接受大稀疏，数据类型，顺序，复制，强制所有有限，确保二维，允许，确保最小样本，确保最小特征，警告数据类型，估算器） 548“至少需要%d个%s。” 549%（n个样本，数组。形状，确保最小样本， -->（上下文） 551 552如果确保_min_features>0且array.ndim==2：

ValueError:找到包含1个样本（形状=（1，2））的数组，而AggregativeClustering's至少需要2个样本