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Diffstat (limited to 'ml/dlib/dlib/clustering/spectral_cluster_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/clustering/spectral_cluster_abstract.h | 43 |
1 files changed, 0 insertions, 43 deletions
diff --git a/ml/dlib/dlib/clustering/spectral_cluster_abstract.h b/ml/dlib/dlib/clustering/spectral_cluster_abstract.h deleted file mode 100644 index 880ad80af..000000000 --- a/ml/dlib/dlib/clustering/spectral_cluster_abstract.h +++ /dev/null @@ -1,43 +0,0 @@ -// 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_ - - |