- recipe sparse-neighbors-search
Approximate k-nearest neighbors search on sparse datasets
- Homepage:
- License:
MIT
- Recipe:
- package sparse-neighbors-search¶
- versions:
0.7-2
,0.7-1
,0.7-0
,0.6-1
,0.6-0
,0.5-0
,0.4-1
,0.4-0
,0.3-1
,0.7-2
,0.7-1
,0.7-0
,0.6-1
,0.6-0
,0.5-0
,0.4-1
,0.4-0
,0.3-1
,0.3-0
,0.2.3-0
- depends libgcc-ng:
>=12
- depends libstdcxx-ng:
>=12
- depends numpy:
>=1.17
- depends numpy:
>=1.23.3,<2.0a0
- depends python:
>=3.10,<3.11.0a0
- depends python_abi:
3.10.* *_cp310
- depends scikit-learn:
>=0.21
- depends scipy:
>=1.3
- depends umap-learn:
- requirements:
Installation
You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install sparse-neighbors-search and update with:: mamba update sparse-neighbors-search
To create a new environment, run:
mamba create --name myenvname sparse-neighbors-search
with
myenvname
being a reasonable name for the environment (see e.g. the mamba docs for details and further options).Alternatively, use the docker container:
docker pull quay.io/biocontainers/sparse-neighbors-search:<tag> (see `sparse-neighbors-search/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an badge with the following MarkDown:
[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/sparse-neighbors-search/README.html)