umap v0.2.2.0


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Uniform Manifold Approximation and Projection

Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by McInnes and Healy (2018) in <arXiv:1802.03426>. 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).

Functions in umap

Name Description
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
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 Compute knn information
mdManhattan compute Manhattan distances 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 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 Repeat 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 .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 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 lookup .Random.seed in global environment
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Vignettes of umap

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License MIT + file LICENSE
LinkingTo Rcpp
LazyData true
VignetteBuilder knitr
RoxygenNote 6.1.1
NeedsCompilation yes
Packaged 2019-05-13 05:38:11 UTC; tkonopka
Repository CRAN
Date/Publication 2019-05-13 06:20:03 UTC

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