ValueError: Expected 2D array, got 1D array instead during svm recognition

Question

My code is this:

import matplotlib.pyplot as plt

from sklearn import datasets, svm

digits = datasets.load_digits()

clf = svm.SVC(gamma=0.001, C=100)

print(len(digits.data))

X,y = digits.data[:-1] , digits.target[:-1]

clf.fit(X,y)

print('Prediction:',clf.predict(digits.data[-1]))

plt.imshow(digits.images[-1],  cmap=plt.cm.gray_r, interpolation="nearest")

plt.show()

And I am getting this error:

Traceback (most recent call last):
File "E:\python programs\sklearn\sklearn 2.py", line 14, in <module>
print('Prediction:',clf.predict(digits.data[-1]))
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\svm\base.py", line 548, in predict
y = super(BaseSVC, self).predict(X)
File   "C:\Users\Rohan\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\svm\base.py", line 308, in predict
X = self._validate_for_predict(X)
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\svm\base.py", line 439, in _validate_for_predict
X = check_array(X, accept_sparse='csr', dtype=np.float64, order="C")
File "C:\Users\Rohan\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\utils\validation.py", line 441, in check_array
"if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[ 0.  0. 10. 14.  8.  1.  0.  0.  0.  2. 16. 14.   m6.  1.  0.  0.  0.  0.
 15. 15.  8. 15.  0.  0.  0.  0.  5. 16. 16. 10.  0.  0.  0.  0. 12. 15.
 15. 12.  0.  0.  0.  4. 16.  6.  4. 16.  6.  0.  0.  8. 16. 10.  8. 16.
 8.  0.  0.  1.  8. 12. 14. 12.  1.  0.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

"What should I do?"

Answer 1

In your prediction step, you are passing a 1D array of shape (1,64) as I am able to see from the sklearn digit dataset docs. Reshape input data before predicting. Use Below:

print('Prediction:',clf.predict(np.reshape(digits.data[-1], (1,-1)))