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shugeo 330a69d4e2
Shugeo solve ls (#203) 
* lstsq op. Initial commit.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Least squares linear problem solve op (lstsq). Cpu draft implementation.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Fixed shape routine and tests.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Added test for lstsq op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Rectification for lstsq op implementation.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Corrected test to avoid numerical inconsistensy.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Added prints for check computing.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Corrected tests to use evalueate facility instead.

Signed-off-by: shugeo <sgazeos@gmail.com>

* CPU implementation of MatrixSolveLs op and tests.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Added cuda implementation for helpers with lstsq op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Refactored tests for lstsq op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Added processing for empty inputs.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Merged tests.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Refactored lstsq op for fast case.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Fixed test.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Refactored lstsq op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Fixed some issues with solve.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Fixed lstsq op to avoid erros.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Added kernel for giagonal factor

Signed-off-by: shugeo <sgazeos@gmail.com>

* lstsq wrapper and triangular_solve fixed

* Added proper processing empty inputs and test.

Signed-off-by: shugeo <sgazeos@gmail.com>

* SequenceMask test

* Build fixed

* Added proper processing of empty inputs with solve op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Mapping added

* Added check of input shapes with solve op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Added a couple of tests for lstsq op and minor changes with cuda helper for one.'

Signed-off-by: shugeo <sgazeos@gmail.com>

* Tests on

* Refactored test for lstsq op.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Fixed test

* Added another approach for lstsq op aka solve_ls.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Finished cpu part for solve_ls op helpers.

* Added helper for low triangular matrix inversion.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Refactored alternate solve_ls cpu implementation.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Removed alternate approach for solve_ls op. Added multithreading with matrix inversion.

Signed-off-by: shugeo <sgazeos@gmail.com>

* Assert fixed

* Refactored multithreading for inverse matricies.

Signed-off-by: shugeo <sgazeos@gmail.com>

Co-authored-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
 		2020-02-28 11:37:26 +03:00
..
ci Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
contrib Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
nd4j-backends Shugeo solve ls (#203) 2020-02-28 11:37:26 +03:00
nd4j-common Various fixes (#143) 2020-01-04 13:45:07 +11:00
nd4j-common-tests Unit/integration test split + test speedup (#166) 2020-01-22 22:27:01 +11:00
nd4j-jdbc Packages fix (#193) 2020-01-27 23:04:21 +03:00
nd4j-parameter-server-parent Small fixes. (#206) 2020-02-01 18:19:36 +11:00
nd4j-remote Small fixes. (#206) 2020-02-01 18:19:36 +11:00
nd4j-serde Fixes (#213) 2020-02-05 17:07:36 +11:00
nd4j-shade nd4j-jackson: exclude java.xml.stream.XML*Factory from service loader to avoid clashes with other non-shaded jackson etc on classpath 2019-12-13 21:41:28 +11:00
nd4j-tensorflow Packages fix (#193) 2020-01-27 23:04:21 +03:00
nd4j-uberjar Cleanup of multiple projects (#175) 2020-01-24 22:35:00 +03:00
.appveyor.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.codeclimate.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.gitignore Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.travis.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
LICENSE Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
README.md Various fixes (#43) 2019-11-14 19:38:20 +11:00
RaspberryPi.md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
VERSION Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildAllversions.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildmultiplescalaversions.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
pom.xml Cleanup of multiple projects (#175) 2020-01-24 22:35:00 +03:00

README.md

ND4J: Scientific Computing on the JVM

Join the chat at https://gitter.im/deeplearning4j/deeplearning4j Maven Central Javadoc

ND4J is an Apache 2.0-licensed scientific computing library for the JVM. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.

It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements.

Please search for the latest version on search.maven.org.

Or use the versions displayed in: https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml

Main Features

  • Versatile n-dimensional array object
  • Multiplatform functionality including GPUs
  • Linear algebra and signal processing functions

Specifics

  • Supports GPUs via with the CUDA backend nd4j-cuda-7.5 and Native via nd4j-native.
  • All of this is wrapped in a unifying interface.
  • The API mimics the semantics of Numpy, Matlab and scikit-learn.

Documentation

Documentation is available at deeplearning4j.org. Access the JavaDocs for more detail.

Installation

To install ND4J, there are a couple of approaches, and more information can be found on the DL4J website.

Install from Maven Central

  1. Search for nd4j in the Maven Central Repository to find the available nd4j jars.
  2. Include the appropriate dependency in your pom.xml.

Clone from the GitHub Repo

https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source

Contribute

  1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.

  2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.

  3. Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).

  4. Write a test, which shows that the bug was fixed or that the feature works as expected.

  5. Note the repository follows the Google Java style with two modifications: 120-char column wrap and 4-spaces indentation. You can format your code to this format by typing mvn formatter:format in the subproject you work on, by using the contrib/formatter.xml at the root of the repository to configure the Eclipse formatter, or by using the INtellij plugin.

  6. Send a pull request, and bug us on Gitter until it gets merged and published.