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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

# or

# python setup.py install --yes USE_AVX_INSTRUCTIONS

# if you have a CPU that supports AVX instructions, since this makes some

# things run faster.

#

# 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)