# Make a training dataset. Here we have just two training examples. Normally
# you would use a much larger training dataset, but for the purpose of example
# this is plenty. For binary classification, the y labels should all be either +1 or -1.
x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
y.append(+1)
# Now make a training object. This object is responsible for turning a
# training dataset into a prediction model. This one here is a SVM trainer
# that uses a linear kernel. If you wanted to use a RBF kernel or histogram
# intersection kernel you could change it to one of these lines:
# svm = dlib.svm_c_trainer_histogram_intersection()
# svm = dlib.svm_c_trainer_radial_basis()
svm = dlib.svm_c_trainer_linear()
svm.be_verbose()
svm.set_c(10)
# Now train the model. The return value is the trained model capable of making predictions.
classifier = svm.train(x, y)
# Now run the model on our data and look at the results.
print("prediction for first sample: {}".format(classifier(x[0])))
print("prediction for second sample: {}".format(classifier(x[1])))
# classifier models can also be pickled in the same was as any other python object.
with open('saved_model.pickle', 'wb') as handle:
pickle.dump(classifier, handle, 2)
