| // Copyright (C) 2015 Davis E. King (davis@dlib.net) | |
| // License: Boost Software License See LICENSE.txt for the full license. | |
| 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. | |
| !*/ | |
| } | |