# 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 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 No Results!