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umap (version 0.2.4.1)

Uniform Manifold Approximation and Projection

Description

Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by McInnes and Healy (2018) in . This package provides an interface for two implementations. One is written from scratch, including components for nearest-neighbor search and for embedding. The second implementation is a wrapper for 'python' package 'umap-learn' (requires separate installation, see vignette for more details).

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install.packages('umap')

Monthly Downloads

10,552

Version

0.2.4.1

License

MIT + file LICENSE

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Repository

https://github.com/tkonopka/umap

Maintainer

Tomasz Konopka

Last Published

January 8th, 2020

Functions in umap (0.2.4.1)

check.learn.available

check whether python module is available, abort if not

get information about approximate k nearest neighbors from a data matrix

Add two coo objects element-wise

Create a coo representation of a square matrix

center.embedding

Adjust a matrix so that each column is centered around zero

dCenteredPearson

compute pearson correlation distance between two vectors

Convert from coo object into conventional matrix

make.initial.embedding

Create an initial embedding for a graph

Estimate a/b parameters

get.global.seed

lookup .Random.seed in global environment

knn.from.data.reps

Repeat knn.from.data multiple times, pick the best neighbors

get information about k nearest neighbors from a distance object or from a matrix with distances

Check class for coo

make.random.embedding

Make an initial embedding with random coordinates

compute Manhattan distance between two vectors

make.initial.spectator.embedding

Create an initial embedding for a set of spectators

naive.simplicial.set.embedding

create an embedding of graph into a low-dimensional space

detect.umap.learn

adjust config depending on umap-learn version

naive.optimize.embedding

modify an existing embedding

smooth.knn.dist

compute a "smooth" distance to the kth neighbor and approximate first neighbor

set.global.seed

set .Random.seed to a pre-saved value

compute cosine dissimilarity between two vectors

naive.fuzzy.simplicial.set

create a simplicial set from a distance object

make.spectral.embedding

Create a spectral embedding for a connectivity graph

spectator.knn.info

compute knn information for spectators relative to data

optimize_embedding

run a series of epochs of the umap optimization

compute Euclidean distance between two vectors

spectral.eigenvectors

get a set of k eigenvectors for the laplacian of x

compute cosine distances

mdCenteredPearson

compute pearson correlation distances

run one epoch of the umap optimization

umap.check.config.class

Validator for config class component

Transpose a coo matrix

umap.check.config

Validator functions for umap settings

umap.prep.input

Prep primary input as a data matrix

Helper to construct coo objects

Create an embedding object compatible with package umap for very small inputs

Computes a manifold approximation and projection

Compute knn information

project data points onto an existing umap embedding

Stop execution with a custom message

compute Euclidean distances

stop execution with a custom error message

print.umap.config

Display contents of a umap configuration

Construct a normalized Laplacian for a graph

Display a summary of a umap object

Default configuration for umap

compute Manhattan distances

Send a message() with a prefix with a data

Create a umap embedding using python package umap-learn

make.epochs.per.sample

Compute a value to capture how often each item contributes to layout optimization

Multiply two coo objects element-wise

Display summary of knn.info

umap.learn.predict

predict embedding of new data given an existing umap object

create a warning message

Remove some entires in a coo matrix where values are zero

Create a umap embedding

umap.naive.predict

predict embedding of new data given an existing umap object

perform a compound transformation on a vector, including clipping

check.compatible.coo

Check that two coo objects are compatible for addition, multiplication

Count the number of connected components in a coo graph

Force (clip) a value into a finite range

deterministically produce random-like integers for each column in a dataset

Construct an identity matrix