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