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
Adjust a matrix so that each column is centered around zero
compute pearson correlation distance between two vectors
Convert from coo object into conventional matrix
Create an initial embedding for a graph
Estimate a/b parameters
lookup .Random.seed in global environment
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 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
adjust config depending on umap-learn version
modify an existing embedding
compute a "smooth" distance to the kth neighbor and approximate first neighbor
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
Create a spectral embedding for a connectivity graph
compute knn information for spectators relative to data
run a series of epochs of the umap optimization
compute Euclidean distance between two vectors
get a set of k eigenvectors for the laplacian of x
compute cosine distances
compute pearson correlation distances
run one epoch of the umap optimization
Validator for config class component
Transpose a coo matrix
Validator functions for umap settings
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
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
Subset a coo
Send a message() with a prefix with a data
Create a umap embedding using python package umap-learn
Compute a value to capture how often each item contributes to layout optimization
Multiply two coo objects element-wise
Display summary of knn.info
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
predict embedding of new data given an existing umap object
perform a compound transformation on a vector, including clipping
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