`install.packages('umap')`

9,051

0.2.2.0

MIT + file LICENSE

May 13th, 2019

identity.coo

Construct an identity matrix

detect.umap.learn

adjust config depending on umap-learn version

predict.umap

project data points onto an existing umap embedding

print.umap.knn

Display summary of knn.info

optimize_epoch

run one epoch of the umap optimization

reduce.coo

Remove some entires in a coo matrix where values are zero

clip

Force (clip) a value into a finite range

check.learn.available

check whether python module is available, abort if not

dManhattan

compute Manhattan distance between two vectors

dEuclidean

compute Euclidean distance between two vectors

find.ab.params

Estimate a/b parameters

knn.from.dist

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

make.initial.spectator.embedding

Create an initial embedding for a set of spectators

dCenteredPearson

compute pearson correlation distance between two vectors

make.epochs.per.sample

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

dCosine

compute cosine dissimilarity between two vectors

make.initial.embedding

Create an initial embedding for a graph

knn.info

Compute knn information

mdManhattan

compute Manhattan distances

message.w.date

Send a message() with a prefix with a data

clip4

perform a compound transformation on a vector, including clipping

umap.learn

Create a umap embedding using python package umap-learn

mdCosine

compute cosine distances

concomp.coo

Count the number of connected components in a coo graph

knn.from.data

get information about approximate k nearest neighbors from a data matrix

naive.optimize.embedding

modify an existing embedding

make.random.embedding

Make an initial embedding with random coordinates

knn.from.data.reps

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

make.spectral.embedding

Create a spectral embedding for a connectivity graph

naive.simplicial.set.embedding

create an embedding of graph into a low-dimensional space

mdEuclidean

compute Euclidean distances

umap.learn.predict

predict embedding of new data given an existing umap object

umap.prep.input

Prep primary input as a data matrix

mdCenteredPearson

compute pearson correlation distances

stop.coo

Stop execution with a custom message

umap.small

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

multiply.coo

Multiply two coo objects element-wise

umap.naive

Create a umap embedding

umap.naive.predict

predict embedding of new data given an existing umap object

subset.coo

Subset a coo

set.global.seed

set .Random.seed to a pre-saved value

print.umap

Display a summary of a umap object

print.umap.config

Display contents of a umap configuration

smooth.knn.dist

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

umap.check.config

Validator functions for umap settings

umap.defaults

Default configuration for umap

umap.error

stop execution with a custom error message

umap.check.config.class

Validator for config class component

umap.warning

create a warning message

naive.fuzzy.simplicial.set

create a simplicial set from a distance object

spectator.knn.info

compute knn information for spectators relative to data

spectral.eigenvectors

get a set of k eigenvectors for the laplacian of x

t.coo

Transpose a coo matrix

umap

Computes a manifold approximation and projection

coo

Create a coo representation of a square matrix

check.coo

Check class for coo

check.compatible.coo

Check that two coo objects are compatible for addition, multiplication

add.coo

Add two coo objects element-wise

coo2mat

Convert from coo object into conventional matrix

center.embedding

Adjust a matrix so that each column is centered around zero

laplacian.coo

Construct a normalized Laplacian for a graph

make.coo

Helper to construct coo objects

get.global.seed

lookup .Random.seed in global environment