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wannesm/dtaidistance v2.0.0

Wannes Meert; Kilian Hendrickx; Toon Van Craenendonck


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3981067", 
  "title": "wannesm/dtaidistance v2.0.0", 
  "issued": {
    "date-parts": [
      [
        2020, 
        8, 
        12
      ]
    ]
  }, 
  "abstract": "<p>New in v2:</p>\n\n<ul>\n\t<li>Numpy is now an optional dependency, also to compile the C library (only Cython is required).</li>\n\t<li>Small optimizations throughout the C code to improve speed.</li>\n\t<li>The consistent use of <code>size_t</code> instead of <code>int</code> allows for larger data structures on 64 bit machines and be more compatible with Numpy.</li>\n\t<li>The parallelization is now implemented directly in C (included if OpenMP is installed).</li>\n\t<li>The <code>max_dist</code> argument turned out to be similar to Silva and Batista&#39;s work on PrunedDTW [7]. The toolbox now implements a version that is equal to PrunedDTW since it prunes more partial distances. Additionally, a <code>use_pruning</code> argument is added to automatically set <code>max_dist</code> to the Euclidean distance, as suggested by Silva and Batista, to speed up the computation (a new method <code>ub_euclidean</code> is available).</li>\n\t<li>Support in the C library for multi-dimensional sequences in the <code>dtaidistance.dtw_ndim</code> package.</li>\n</ul>", 
  "author": [
    {
      "family": "Wannes Meert"
    }, 
    {
      "family": "Kilian Hendrickx"
    }, 
    {
      "family": "Toon Van Craenendonck"
    }
  ], 
  "version": "v2.0.0", 
  "type": "article", 
  "id": "3981067"
}
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