coco
44 lines
1.7 KiB
C++
44 lines
1.7 KiB
C++
// Copyright (C) 2015 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_SPECTRAL_CLUSTEr_ABSTRACT_H_
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#ifdef DLIB_SPECTRAL_CLUSTEr_ABSTRACT_H_
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#include <vector>
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namespace dlib
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{
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template <
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typename kernel_type,
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typename vector_type
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>
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std::vector<unsigned long> spectral_cluster (
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const kernel_type& k,
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const vector_type& samples,
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const unsigned long num_clusters
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);
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/*!
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requires
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- samples must be something with an interface compatible with std::vector.
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- The following expression must evaluate to a double or float:
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k(samples[i], samples[j])
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- num_clusters > 0
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ensures
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- Performs the spectral clustering algorithm described in the paper:
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On spectral clustering: Analysis and an algorithm by Ng, Jordan, and Weiss.
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and returns the results.
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- This function clusters the input data samples into num_clusters clusters and
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returns a vector that indicates which cluster each sample falls into. In
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particular, we return an array A such that:
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- A.size() == samples.size()
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- A[i] == the cluster assignment of samples[i].
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- for all valid i: 0 <= A[i] < num_clusters
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- The "similarity" of samples[i] with samples[j] is given by
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k(samples[i],samples[j]). This means that k() should output a number >= 0
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and the number should be larger for samples that are more similar.
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!*/
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}
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#endif // DLIB_SPECTRAL_CLUSTEr_ABSTRACT_H_
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