Python 3.x Python内存'；空白'；Jupyter笔记本

首先，我在Ubuntu上运行，并在Jupyter笔记本中使用Anaconda作为内核

我一直得到以下错误。我相信这表明我的内存不足，但我不知道如何解决这个问题。有人建议卸载python332并安装python364，但这在卸载python后立即破坏了我的Ubuntu安装（哎呀），我不得不重新安装

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-25-8b765500013f> in <module>()
----> 1 _, S, _ = np.linalg.svd(features)
      2 print("Condition number from the SVD: {0:.1f}".format(np.max(S)/np.min(S)))
      3 print("Condition number from cond:    {0:.1f}".format(np.linalg.cond(features)))

/home/n/anaconda/lib/python3.5/site-packages/numpy/linalg/linalg.py in svd(a, full_matrices, compute_uv)
   1387 
   1388         signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-> 1389         u, s, vt = gufunc(a, signature=signature, extobj=extobj)
   1390         u = u.astype(result_t, copy=False)
   1391         s = s.astype(_realType(result_t), copy=False)

MemoryError:

import pandas as pd
import numpy as np
import matplotlib.pylab as plt
pd.set_option('display.max_columns',180)

dfk = pd.read_csv('data/kick.csv')dfk = pd.read_csv('data/kick.csv')

response =  dfk.state
features = dfk
features.drop('state', axis=1, inplace=True)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, response, 
                                                    random_state=321)

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

logreg = LogisticRegression()
logreg.fit(X_train,y_train)
ypred_lr = logreg.predict(X_test)

print("Accuracy on the test set: {0:.3f}".format(accuracy_score(ypred_lr, y_test)))


_, S, _ = np.linalg.svd(features) #fails here
print("Condition number from the SVD: {0:.1f}".format(np.max(S)/np.min(S)))
print("Condition number from cond:    {0:.1f}".format(np.linalg.cond(features)))