Yashwants19

cli python julia go r

Yashwants19

From b39778b192abadafa3d72e18057cd2b4a8de69b3 Mon Sep 17 00:00:00 2001
From: Yashwant <yashwantsingh.sngh@gmail.com>
Date: Mon, 21 Sep 2020 14:50:08 +0530
Subject: [PATCH] Fix Windows build.

---
 .ci/windows-steps.yaml | 28 ++--------------------------
 1 file changed, 2 insertions(+), 26 deletions(-)

diff --git a/.ci/windows-steps.yaml b/.ci/windows-steps.yaml

Yashwants19

From 1484c603ba107ef27d4239a44b4b4060de0eea97 Mon Sep 17 00:00:00 2001
From: Yashwant <yashwantsingh.sngh@gmail.com>
Date: Sat, 12 Sep 2020 17:01:34 +0530
Subject: [PATCH] Apply different approach for solving macro collision.

---
 .../bindings/R/mlpack/inst/include/mlpack.h.in       | 12 ++++++++----
 src/mlpack/bindings/R/mlpack/src/rcpp_mlpack.h       |  6 ++++--
 src/mlpack/bindings/R/r_method.cpp.in                |  3 +++
 3 files changed, 15 insertions(+), 6 deletions(-)

Yashwants19

From b998ea5b7d3a8ce937e6e6ae225e6a7ea6fae4bc Mon Sep 17 00:00:00 2001
From: Yashwant <yashwantsingh.sngh@gmail.com>
Date: Sat, 5 Sep 2020 16:22:28 +0530
Subject: [PATCH] Fix guess_file_type error.

---
 .github/workflows/main.yml         | 2 +-
 CMake/Findcereal.cmake             | 4 +++-
 src/mlpack/core/data/load_impl.hpp | 8 --------
 3 files changed, 4 insertions(+), 10 deletions(-)

Yashwants19

From 69f75f9970bf7dc66154ff69b229af458640b375 Mon Sep 17 00:00:00 2001
From: Yashwant <yashwantsingh.sngh@gmail.com>
Date: Sat, 5 Sep 2020 15:34:00 +0530
Subject: [PATCH] Apply cereal to R-bindings

---
 .ci/linux-steps.yaml                          |   2 +-
 .ci/macos-steps.yaml                          |   2 +-
 .github/workflows/main.yml                    |   4 +-
 CMake/Findcereal.cmake                        |  63 +-

Yashwants19

 src/mlpack/core/data/load_impl.hpp | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/src/mlpack/core/data/load_impl.hpp b/src/mlpack/core/data/load_impl.hpp
index ca15655e1..a5abdeb43 100644
--- a/src/mlpack/core/data/load_impl.hpp
+++ b/src/mlpack/core/data/load_impl.hpp
@@ -120,11 +120,7 @@ bool Load(const std::string& filename,
 
   if (extension == "csv" || extension == "tsv")

Yashwants19

 .ci/linux-steps.yaml                               |   2 +-
 .ci/macos-steps.yaml                               |   2 +-
 .github/workflows/main.yml                         |   4 +-
 CMake/Findcereal.cmake                             |  63 ++-
 CMake/R/ConfigureRCPP.cmake                        |   8 +-
 CMakeLists.txt                                     |   3 +-
 README.md                                          |   1 +
 src/mlpack/bindings/R/CMakeLists.txt               |  44 +-
 src/mlpack/bindings/R/mlpack/DESCRIPTION.in        |   3 +-
 .../bindings/R/mlpack/inst/include/mlpack.h.in     |  23 +-

Yashwants19

 .ci/linux-steps.yaml                               |   2 +-
 .ci/macos-steps.yaml                               |   2 +-
 .github/workflows/main.yml                         |   4 +-
 CMake/Findcereal.cmake                             |  63 ++-
 CMake/R/ConfigureRCPP.cmake                        |   8 +-
 CMakeLists.txt                                     |   3 +-
 README.md                                          |   1 +
 src/mlpack/bindings/R/CMakeLists.txt               |  44 +-
 src/mlpack/bindings/R/mlpack/DESCRIPTION.in        |   3 +-
 .../bindings/R/mlpack/inst/include/mlpack.h.in     |  23 +-

Yashwants19

#' @title Large Margin Nearest Neighbors (LMNN)
#'
#' @description
#' 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.
#'
#' @param input Input dataset to run LMNN on (numeric matrix).
#' @param batch_size Batch size for mini-batch SGD.  Default value "50"

Yashwants19

#' @title AdaBoost
#'
#' @description
#' This program implements the AdaBoost (or Adaptive Boosting) algorithm. The
#' variant of AdaBoost implemented here is AdaBoost.MH. It uses a weak learner,
#' either decision stumps or perceptrons, and over many iterations, creates a
#' strong learner that is a weighted ensemble of weak learners. It runs these
#' iterations until a tolerance value is crossed for change in the value of the
#' weighted training error.
#'