这是可视化SVM决策边界的代码。 我无法发现错误。 请看一看

Question

**NOTE : The variable x contains 30 tuples of feature vector of 5 dimension. These values of x are transferred to x_train.x can be imagined to be the form of x = [[1.0 , 2.0 , 3,0 , 4.0 , 5.0 ], [11.0 , 12.0 , 13.0 , 14.0 , 15.0], [21.0 , 22.0 , 23.0 , 24.0 , 25,0], .. .. ..] and y = labels =[1,1,1 , 2,2,2 , 3,3,3...] I wish to apply PCA on x and reduce to two dimensions and then plot decision boundaries. I am able to plot the points but unable to plot to decision boundary **

x_train = x 
y_train =labels 
pca = PCA(n_components=2).fit(x_train) 
pca_2d = pca.transform(x_train)
clf = svm.SVC(kernel='linear',C = 3)
clf.fit(pca_2d, y_train)
for i in range(1, pca_2d.shape[0]):
    if y_train[i] == 1:
     c1 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    s=50,marker='+')
    elif y_train[i] == 2:
     c2 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    s=50,marker='.')
    elif y_train[i] == 3:
     c3 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    s=50,marker=',')
    elif y_train[i] == 4:
     c4 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    s=50,marker='^')
    elif y_train[i] == 5:
     c5 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    s=50,marker='v')
    elif y_train[i] == 6:

    x_min, x_max = pca_2d[:, 0].min() - 1,   pca_2d[:,0].max() + 1
    y_min, y_max = pca_2d[:, 1].min() - 1,   pca_2d[:, 1].max() + 1

    xx, yy = np.meshgrid(np.arange(x_min, x_max,  .01),np.arange(y_min,y_max, .01))  
    #************ ERROR ******#
    Z = clf.predict(np.c_[xx.ravel(),  yy.ravel()]) 
    #************ ERROR ******#
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
    pl.title('Support Vector Machine Decision Surface')
    pl.axis('off')
    pl.show()





##  The error shown is :
    Traceback (most recent call last):
    File "D:\New folder_previous.2 - Copy.right\main_pos.py", line 354, in   <module>
    Z = clf.predict(np.c_[xx.ravel(),  yy.ravel()])
    File "C:\Python27\lib\site-packages\numpy\lib\index_tricks.py", line   338, in __getitem__
    res = _nx.concatenate(tuple(objs), axis=self.axis)
    MemoryError

Answer 1

The error is pretty straigth forward, you are trying to allocate too much memory. Use bigger step size than 0.1, investigate the size of your matrix, probably you generated huge one in xx,yy. Furthermore - why is everything in the loop over samples? You seem to be calling this 30 times, which does not seem like a reasonable idea.