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#!/usr/bin/python

The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt

This is an example illustrating the use of a binary SVM classifier tool from

the dlib C++ Library. In this example, we will create a simple test dataset

and show how to learn a classifier from it.

COMPILING/INSTALLING THE DLIB PYTHON INTERFACE

You can install dlib using the command:

pip install dlib

Alternatively, if you want to compile dlib yourself then go into the dlib

root folder and run:

python setup.py install

Compiling dlib should work on any operating system so long as you have

CMake installed. On Ubuntu, this can be done easily by running the

command:

sudo apt-get install cmake

import dlib try: import cPickle as pickle except ImportError: import pickle

x = dlib.vectors() y = dlib.array()

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)

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)