mirror of https://github.com/davisking/dlib.git
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
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#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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#
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# This is an example illustrating the use of a binary SVM classifier tool from
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# the dlib C++ Library. In this example, we will create a simple test dataset
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# and show how to learn a classifier from it.
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#
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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# or
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# python setup.py install --yes USE_AVX_INSTRUCTIONS
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# if you have a CPU that supports AVX instructions, since this makes some
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# things run faster.
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake and boost-python installed. On Ubuntu, this can be done easily by
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# running the command:
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# sudo apt-get install libboost-python-dev cmake
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#
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import dlib
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import pickle
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x = dlib.vectors()
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y = dlib.array()
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# Make a training dataset. Here we have just two training examples. Normally
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# you would use a much larger training dataset, but for the purpose of example
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# this is plenty. For binary classification, the y labels should all be either +1 or -1.
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x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
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y.append(+1)
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x.append(dlib.vector([-1, -2, -3, 1, 2, 3]))
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y.append(-1)
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# Now make a training object. This object is responsible for turning a
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# training dataset into a prediction model. This one here is a SVM trainer
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# that uses a linear kernel. If you wanted to use a RBF kernel or histogram
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# intersection kernel you could change it to one of these lines:
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# svm = dlib.svm_c_trainer_histogram_intersection()
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# svm = dlib.svm_c_trainer_radial_basis()
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svm = dlib.svm_c_trainer_linear()
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svm.be_verbose = True
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svm.set_c(10)
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# Now train the model. The return value is the trained model capable of making predictions.
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classifier = svm.train(x, y)
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# Now run the model on our data and look at the results.
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print("prediction for first sample: {}".format(classifier(x[0])))
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print("prediction for second sample: {}".format(classifier(x[1])))
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# classifier models can also be pickled in the same was as any other python object.
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with open('saved_model.pickle', 'wb') as handle:
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pickle.dump(classifier, handle)
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