ValueError：在运行sklearn adaboost时设置带有序列的数组元素

Question

我是新手和python，并尝试使用机器学习。但是我的代码不起作用。我的数据集非常简单，它包含七个图像（没有固定大小）。我尝试进行细分并运行adaboost算法但得到此错误。

Error:        
   array = np.array(array, dtype=dtype, order=order, copy=copy)
   **ValueError: setting an array element with a sequence.**

我的代码

from skimage import data
import glob
import numpy as np
from sklearn import cluster
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier

initDataset = []
samplesListFiles = glob.glob("/home/bv/Downloads/sampleimage/*.*")
i = 0
for fileC in samplesListFiles:
img = data.imread(fileC, as_grey=True)
array = np.reshape(img, (np.product(img.shape), 1))
model = cluster.KMeans(n_clusters=3, random_state=25)
results = model.fit(array[::1])
X = array[::1]
X = results.predict(X)
initDataset.append(X)

dataset = np.array(initDataset)
C = 1.0
X=dataset
y=[[0.],
   [0.],
   [1.],
   [0.],
   [1.],
   [1.],
   [0.]]
rez = []
for i in range(0, len(y)):
   rez.append(y[i][0])
svc = GaussianNB()
clf = AdaBoostClassifier(n_estimators=100, 
base_estimator=svc,learning_rate=1)
X = X.reshape((7,-1))
clf.fit(X,rez)


print "TEST"

cancerListFiles = glob.glob("/home/bv/Downloads/cancerimage/*.*")
for fileC in samplesListFiles:
   img = data.imread(fileC, as_grey=True)
   array = np.reshape(img, (np.product(img.shape), 1))
   model = cluster.KMeans(n_clusters=3, random_state=25)
   results = model.fit(array[::1])
   X = array[::1]
   X = results.predict(X)
   print clf.predict(X)

Befor call .fit X是

  [[array([0, 0, 0, ..., 0, 0, 0], dtype=int32)]
  [array([0, 0, 0, ..., 0, 0, 0], dtype=int32)]
  [array([1, 1, 1, ..., 1, 1, 1], dtype=int32)]
  [array([0, 0, 0, ..., 0, 0, 0], dtype=int32)]
  [array([2, 2, 2, ..., 2, 2, 2], dtype=int32)]
  [array([0, 0, 0, ..., 0, 0, 0], dtype=int32)]
  [array([2, 2, 0, ..., 0, 0, 0], dtype=int32)]]