PrologMethods/Misc.-other/nmf.md

+# Non-negative Matrix Factorization
+
+An implementation of non-negative matrix factorization. This can be used to decompose an input dataset into two low-rank non-negative components.
+
+# Available Predicates
+
+* [nmf/13](/PrologMethods/Misc.%20other/nmf#nmf13)
+
+---
+
+[links/resources](/PrologMethods/Misc.%20other/nmf#connected-linksresources)
+
+## **_nmf/13_**
+
+Initilizes the nmf model and applies it to the given data.
+
+```prolog
+%% part of the predicate definition
+nmf(  +string, 
+      +integer, +float32,
+      +pointer(float_array), +integer, +integer,
+      +integer,
+      -pointer(float_array), -integer, -integer,
+      -pointer(float_array), -integer, -integer).
+```
+
+### Parameters
+| Name | Type | Description | Default |
+|------|------|-------------|---------|
+| updateRule | +string | Update rules for each iteration:  "multdist", "multdiv", "als" . | multdist |
+| maxIterations | +integer | Number of iterations before NMF terminates (0 runs until convergence. | 10000 |
+| minResidue | +float | The minimum root mean square residue allowed for each iteration, below which the program terminates. | 1e-5 |
+| data | +matrix | Input matrix to be factorized. | - |
+| rank | +integer | Rank r of the factorization. | - |
+| W | -matrix | Basis matrix to be output. | - |
+| H | -matrix | Encoding matrix to output. | - |
+
+---
+
+# 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::nmf_C++\_documentation](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1amf_1_1AMF.html)
+* [MLpack::nmf_Python_documentation](https://www.mlpack.org/doc/stable/python_documentation.html#nmf)
+
+added some of the links from the python documentation
+
+* cf
+* [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
+* [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
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