Showing 6 of total 6 results (show query)
rcurtin
mlpack:'Rcpp' Integration for the 'mlpack' Library
A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.
Maintained by Ryan Curtin. Last updated 3 months ago.
63.3 match 3.71 score 20 scripts 8 dependents
gagolews
genieclust:Fast and Robust Hierarchical Clustering with Noise Points Detection
A retake on the Genie algorithm (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>), which is a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). It is now faster and more memory efficient; determining the whole cluster hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only a minute or so. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of inequality indices (e.g., Gini and Bonferroni), external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.
Maintained by Marek Gagolewski. Last updated 4 days ago.
cluster-analysisclusteringclustering-algorithmdata-analysisdata-miningdata-sciencegeniehdbscanhierarchical-clusteringhierarchical-clustering-algorithmmachine-learningmachine-learning-algorithmsmlpacknmslibpythonpython3sparsecppopenmp
11.0 match 61 stars 7.29 score 13 scripts 5 dependents
eddelbuettel
naarma:Connect nanoarrow with (Rcpp)Armadillo
The nanoarrow package offers C-level functionality to work with Arrow object, along with a small amount of C++ integration. This package uses it to interact with Armadillo objects. Some auxiliary testing facility from the nanoarrow package is included here as well.
Maintained by Dirk Eddelbuettel. Last updated 3 months ago.
11.0 match 2.00 score 4 scripts
paithiov909
baritsu:Wrappers for 'mlpack'
A collection of wrappers for the 'mlpack' package that allows passing formula as their argument.
Maintained by Akiru Kato. Last updated 21 days ago.
6.1 match 3 stars 3.08 score 1 scripts
coatless-rpkg
RcppEnsmallen:Header-Only C++ Mathematical Optimization Library for 'Armadillo'
'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later.
Maintained by James Joseph Balamuta. Last updated 3 months ago.
armadillocpp11ensmallenoptimizationrcpprcpparmadilloopenblascppopenmp
0.5 match 31 stars 7.67 score 1 scripts 14 dependents
allanvc
emstreeR:Tools for Fast Computing and Visualizing Euclidean Minimum Spanning Trees
Fast and easily computes an Euclidean Minimum Spanning Tree (EMST) from data, relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). 'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles.
Maintained by Allan Quadros. Last updated 1 years ago.
0.5 match 7 stars 4.23 score 16 scripts 1 dependents