Sklearn svm predict same value

Question

This is my implementation for ocr for number.

#mix the dataset
dataset=np.delete(dataset,0,0)
lable=np.delete(lable,0)
X=dataset.shape[0]
l=range(X)
np.random.shuffle(l)


sampleing=np.zeros(32*32)
lableing=np.zeros(1)

for x in l:
    sampleing=np.vstack((sampleing,dataset[x]))
    lableing=np.hstack((lableing,lable[x]))

sampleing=np.delete(sampleing,0,0)
lableing=np.delete(lableing,0)
x=sampleing.shape[0]
train=sampleing[0:int(x*0.8)]
train_lableing=lableing[0:int(x*0.8)]
test=sampleing[int(x*0.8):]
test_lableing=lableing[int(x*0.8):]

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

print clf.fit(train,train_lableing)
predict=clf.predict(test)
print classification_report(test_lableing,predict)

print predict

This is my output

     precision    recall  f1-score   support

      0       0.00      0.00      0.00         9
      1       0.00      0.00      0.00        14
      2       0.00      0.00      0.00         6
      3       0.00      0.00      0.00         5
      4       0.00      0.00      0.00         4
      5       0.00      0.00      0.00         3
      6       0.00      0.00      0.00        10
      7       0.00      0.00      0.00        12
      8       0.17      1.00      0.29        15
      9       0.00      0.00      0.00        10

avg / total 0.03 0.17 0.05 88

['8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8' '8']

Why am i getting same predict value.I am passing random training and testing data I did try to debug but i did not find any problem in my code.please help.

Answer 1

This could because of few number of dataset(feature) .SVC work well with large data set.Try using LinerSVC this should solve the problem.