Construct an identity matrix
adjust config depending on umap-learn version
project data points onto an existing umap embedding
Display summary of knn.info
run one epoch of the umap optimization
Remove some entires in a coo matrix where values are zero
Force (clip) a value into a finite range
check whether python module is available, abort if not
compute Manhattan distance between two vectors
compute Euclidean distance between two vectors
Estimate a/b parameters
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
compute pearson correlation distance between two vectors
Compute a value to capture how often each item contributes to layout optimization
compute cosine dissimilarity between two vectors
Create an initial embedding for a graph
Compute knn information
compute Manhattan distances
Send a message() with a prefix with a data
perform a compound transformation on a vector, including clipping
Create a umap embedding using python package umap-learn
compute cosine distances
Count the number of connected components in a coo graph
get information about approximate k nearest neighbors from a data matrix
modify an existing embedding
Make an initial embedding with random coordinates
Repeat knn.from.data multiple times, pick the best neighbors
Create a spectral embedding for a connectivity graph
naive.simplicial.set.embedding
create an embedding of graph into a low-dimensional space
compute Euclidean distances
predict embedding of new data given an existing umap object
Prep primary input as a data matrix
compute pearson correlation distances
Stop execution with a custom message
Create an embedding object compatible with package umap for very small inputs
Multiply two coo objects element-wise
Create a umap embedding
predict embedding of new data given an existing umap object
Subset a coo
set .Random.seed to a pre-saved value
Display a summary of a umap object
Display contents of a umap configuration
compute a "smooth" distance to the kth neighbor and approximate first neighbor
Validator functions for umap settings
Default configuration for umap
stop execution with a custom error message
Validator for config class component
create a warning message
naive.fuzzy.simplicial.set
create a simplicial set from a distance object
compute knn information for spectators relative to data
get a set of k eigenvectors for the laplacian of x
Transpose a coo matrix
Computes a manifold approximation and projection
Create a coo representation of a square matrix
Check class for coo
Check that two coo objects are compatible for addition, multiplication
Add two coo objects element-wise
Convert from coo object into conventional matrix
Adjust a matrix so that each column is centered around zero
Construct a normalized Laplacian for a graph
Helper to construct coo objects
lookup .Random.seed in global environment