PrologMethods/Transformation/lmnn.md

+# Large Margin Nearest Neighbors
+
+An implementation of Large Margin Nearest Neighbors (LMNN), a distance learning technique. Given a labeled dataset, this learns a transformation of the data that improves k-nearest-neighbor performance. This can be useful as a preprocessing step.
+
+# Available Predicates
+
+* [lmnn/20](/PrologMethods/Transformation/lmnn#lmnn20)
+
+---
+
+[links/resources](/PrologMethods/Transformation/lmnn#connected-linksresources)
+
+## **_lmnn/20_**
+
+Is a single predicate that initiates the lmnn model with all the given params and then performs Large Margin Nearest Neighbors metric learning on the reference data.
+
+```prolog
+%% part of the predicate definition
+lmnn(  +string,
+       +pointer(float_array), +integer, +integer,
+       +pointer(float_array), +integer,
+       +integer, 
+       +float32, +float32, +integer, +integer, +float32,
+       +integer, +integer,
+       +integer, +integer, +integer,
+       -pointer(float_array), -integer, -integer)
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| optimizer | +string | Optimizer to use; "amsgrad", "bbsgd", "sgd", or "lbfgs". | amsgrad |
+| data | +matrix | Input dataset to run LMNN on. | - |
+| labels | +vec | Labels for input dataset. | - |
+| k | +integer | Number of target neighbors to use for each datapoint. | 1 |
+| regularization | +float | Regularization for LMNN objective function. | 0.5 |
+| stepSize | +float | Step size for AMSGrad, BB_SGD and SGD (alpha). | 0.01 |
+| passes | +integer | Maximum number of full passes over dataset for AMSGrad, BB_SGD and SGD. | 50 |
+| maxIterations | +integer | Maximum number of iterations for L-BFGS (0 indicates no limit). | 100000 |
+| tolerance | +integer | Maximum tolerance for termination of AMSGrad, BB_SGD, SGD or L-BFGS. | 1e-7 |
+| center | +integer(bool) | Perform mean-centering on the dataset. It is useful when the centroid of the data is far from the origin. | (0)false |
+| shuffle | +integer(bool) |  |  |
+| batchSize | +integer | Batch size for mini-batch SGD. | 50 |
+| range | +integer | Number of iterations after which impostors needs to be recalculated. | 1 |
+| rank | +integer | Rank of distance matrix to be optimized. | 0 |
+| distance | -matrix | Output matrix for learned distance matrix. | - |
+
+---
+
+# Connected Links/Resources
+
+If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
+
+* [MLpack::lmnn_C++\_documentation](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1lmnn_1_1LMNN.html)
+* [MLpack::lmnn_Python_documentation](https://www.mlpack.org/doc/stable/python_documentation.html#lmnn)
+
+added some of the links from the python documentation
+
+* nca
+* [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
+* [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
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