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Added spectral_cluster() example

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Davis King 2015-02-11 07:55:42 -05:00
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commit 5694fada70
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examples/kkmeans_ex.cpp
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@ -1,7 +1,7 @@
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/* /*
This is an example illustrating the use of the kkmeans object This is an example illustrating the use of the kkmeans object
from the dlib C++ Library. and spectral_cluster() routine from the dlib C++ Library.
The kkmeans object is an implementation of a kernelized k-means clustering The kkmeans object is an implementation of a kernelized k-means clustering
algorithm. It is implemented by using the kcentroid object to represent algorithm. It is implemented by using the kcentroid object to represent
@ -11,7 +11,8 @@
a svm classifier finds non-linear decision surfaces. a svm classifier finds non-linear decision surfaces.
This example will make points from 3 classes and perform kernelized k-means This example will make points from 3 classes and perform kernelized k-means
clustering on those points. clustering on those points. It will also do the same thing using spectral
clustering.
The classes are as follows: The classes are as follows:
- points very close to the origin - points very close to the origin
@ -141,6 +142,13 @@ int main()
cout << "num dictionary vectors for center 1: " << test.get_kcentroid(1).dictionary_size() << endl; cout << "num dictionary vectors for center 1: " << test.get_kcentroid(1).dictionary_size() << endl;
cout << "num dictionary vectors for center 2: " << test.get_kcentroid(2).dictionary_size() << endl; cout << "num dictionary vectors for center 2: " << test.get_kcentroid(2).dictionary_size() << endl;
// Finally, we can also solve the same kind of non-linear clustering problem with
// spectral_cluster(). The output is a vector that indicates which cluster each sample
// belongs to. Just like with kkmeans, it assigns each point to the correct cluster.
std::vector<unsigned long> assignments = spectral_cluster(kernel_type(0.1), samples, 3);
cout << mat(assignments) << endl;
} }