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- add documentation for LshIndexParams

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This commit is contained in:
Vincent Rabaud
2011-08-31 17:56:33 +00:00
parent 15b1b4e232
commit ec6636a664
1 changed files with 21 additions and 2 deletions
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19
modules/flann/doc/flann_fast_approximate_nearest_neighbor_search.rst
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@@ -86,8 +86,27 @@ The method constructs a fast search structure from a set of features using the s
float cb_index = 0.2 ); float cb_index = 0.2 );
}; };
*
**LshIndexParams** When using a parameters object of this type the index created uses multi-probe LSH (by ``Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search`` by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) ::
struct LshIndexParams : public IndexParams
{
LshIndexParams(
unsigned int table_number,
unsigned int key_size,
unsigned int multi_probe_level );
};
.. ..
* **table_number** the number of hash tables to use (between 10 and 30 usually).
* **key_size** the size of the hash key in bits (between 10 and 20 usually).
* **multi_probe_level** the number of bits to shift to check for neighboring buckets (0 is regular LSH, 2 is recommended).
* *
**AutotunedIndexParams** When passing an object of this type the index created is automatically tuned to offer the best performance, by choosing the optimal index type (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. :: **AutotunedIndexParams** When passing an object of this type the index created is automatically tuned to offer the best performance, by choosing the optimal index type (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. ::