Man pages for umap
Uniform Manifold Approximation and Projection

add.cooAdd two coo objects element-wise
center.embeddingAdjust a matrix so that each column is centered around zero
check.compatible.cooCheck that two coo objects are compatible for addition,...
check.cooCheck class for coo
check.learn.availablecheck whether python module is available, abort if not
clipForce (clip) a value into a finite range
clip4perform a compound transformation on a vector, including...
concomp.cooCount the number of connected components in a coo graph
cooCreate a coo representation of a square matrix
coo2matConvert from coo object into conventional matrix
dCenteredPearsoncompute pearson correlation distance between two vectors
dCosinecompute cosine dissimilarity between two vectors
detect.umap.learnadjust config depending on umap-learn version
dEuclideancompute Euclidean distance between two vectors
dManhattancompute Manhattan distance between two vectors
find.ab.paramsEstimate a/b parameters
get.global.seedlookup .Random.seed in global environment
identity.cooConstruct an identity matrix
knn.from.dataget information about approximate k nearest neighbors from a...
knn.from.data.repsRepeat knn.from.data multiple times, pick the best neighbors
knn.from.distget information about k nearest neighbors from a distance...
knn.infoCompute knn information
laplacian.cooConstruct a normalized Laplacian for a graph
make.cooHelper to construct coo objects
make.epochs.per.sampleCompute a value to capture how often each item contributes to...
make.initial.embeddingCreate an initial embedding for a graph
make.initial.spectator.embeddingCreate an initial embedding for a set of spectators
make.random.embeddingMake an initial embedding with random coordinates
make.spectral.embeddingCreate a spectral embedding for a connectivity graph
mdCenteredPearsoncompute pearson correlation distances
mdCosinecompute cosine distances
mdEuclideancompute Euclidean distances
mdManhattancompute Manhattan distances
message.w.dateSend a message() with a prefix with a data
multiply.cooMultiply two coo objects element-wise
naive.fuzzy.simplicial.setcreate a simplicial set from a distance object
naive.optimize.embeddingmodify an existing embedding
naive.simplicial.set.embeddingcreate an embedding of graph into a low-dimensional space
optimize_epochrun one epoch of the umap optimization
predict.umapproject data points onto an existing umap embedding
print.umapDisplay a summary of a umap object
print.umap.configDisplay contents of a umap configuration
print.umap.knnDisplay summary of knn.info
reduce.cooRemove some entires in a coo matrix where values are zero
set.global.seedset .Random.seed to a pre-saved value
smooth.knn.distcompute a "smooth" distance to the kth neighbor and...
spectator.knn.infocompute knn information for spectators relative to data
spectral.eigenvectorsget a set of k eigenvectors for the laplacian of x
stop.cooStop execution with a custom message
subset.cooSubset a coo
t.cooTranspose a coo matrix
umapComputes a manifold approximation and projection
umap.check.configValidator functions for umap settings
umap.check.config.classValidator for config class component
umap.defaultsDefault configuration for umap
umap.errorstop execution with a custom error message
umap.learnCreate a umap embedding using a python package
umap.learn.predictpredict embedding of new data given an existing umap object
umap.naiveCreate a umap embedding
umap.naive.predictpredict embedding of new data given an existing umap object
umap.prep.inputPrep primary input as a data matrix
umap.smallCreate an embedding object compatible with package umap for...
umap documentation built on July 26, 2018, 9:01 a.m.