在Jupyter笔记本上运行相同的代码，得到一条内存信息。

Question

一个月前我在Jupyter笔记本上运行了与SVM相关的代码。当时，成绩非常好。我最近检查了一些东西，这些东西都按原样运行了代码，但是有内存错误。我不知道原因。你知道吗

我试着重新启动电脑，重新安装康达。你知道吗

代码：

x = dataset['x']
y = dataset['y']
ss0_train = ss0['train']
ss0_test = ss0['test']

training_image_array, training_label_array = x[ss0_train], y[ss0_train]
test_image_array, test_label_array = x[ss0_test], y[ss0_test]

ori = training_image_array
bat = np.zeros((144338,133))
cat = np.hstack([ori,bat])
training_image_array = cat

ori2 = test_image_array
bat2 = np.zeros((16037,133))
cat2 = np.hstack([ori2,bat2])
test_image_array = cat2

train_X, train_y, test_X, test_y = training_image_array, training_label_array, test_image_array, test_label_array


from sklearn.svm import LinearSVC
from sklearn.calibration import CalibratedClassifierCV

cclf = CalibratedClassifierCV(base_estimator=LinearSVC(penalty='l2', dual=False), cv=5)
cclf.fit(train_X,train_y_arg)

错误：

 MemoryError                               Traceback (most recent call last)
  <ipython-input-10-85ee7435c48f> in <module>()
  3 
  4 cclf = CalibratedClassifierCV(base_estimator=LinearSVC(penalty='l2', dual=False), cv=5)
  ----> 5 cclf.fit(train_X,train_y_arg)

  D:\Users\GIL\Anaconda3\lib\site-packages\sklearn\calibration.py in fit(self, X, y, sample_weight)
  179              
                        sample_weight=base_estimator_sample_weight[train])
  180                 else:
--> 181                     this_estimator.fit(X[train], y[train])
  182 
  183                 calibrated_classifier = _CalibratedClassifier(

  MemoryError: