Man pages for donelsonsmith/umap_R
Uniform Manifold Approximation and Projection
| add.coo | Add two coo objects element-wise |
| center.embedding | Adjust a matrix so that each column is centered around zero |
| check.compatible.coo | Check that two coo objects are compatible for addition,... |
| check.coo | Check class for coo |
| check.learn.available | check whether python module is available, abort if not |
| clip | Force (clip) a value into a finite range |
| clip4 | perform a compound transformation on a vector, including... |
| concomp.coo | Count the number of connected components in a coo graph |
| coo | Create a coo representation of a square matrix |
| coo2mat | Convert from coo object into conventional matrix |
| dCenteredPearson | compute pearson correlation distance between two vectors |
| dCosine | compute cosine dissimilarity between two vectors |
| detect.umap.learn | adjust config depending on umap-learn version |
| dEuclidean | compute Euclidean distance between two vectors |
| dManhattan | compute Manhattan distance between two vectors |
| find.ab.params | Estimate a/b parameters |
| get.global.seed | lookup .Random.seed in global environment |
| identity.coo | Construct an identity matrix |
| knn.from.data | get information about approximate k nearest neighbors from a... |
| knn.from.data.reps | Repeat knn.from.data multiple times, pick the best neighbors |
| knn.from.dist | get information about k nearest neighbors from a distance... |
| knn.info | Compute knn information |
| laplacian.coo | Construct a normalized Laplacian for a graph |
| make.coo | Helper to construct coo objects |
| make.epochs.per.sample | Compute a value to capture how often each item contributes to... |
| make.initial.embedding | Create an initial embedding for a graph |
| make.initial.spectator.embedding | Create an initial embedding for a set of spectators |
| make.random.embedding | Make an initial embedding with random coordinates |
| make.spectral.embedding | Create a spectral embedding for a connectivity graph |
| mdCenteredPearson | compute pearson correlation distances |
| mdCosine | compute cosine distances |
| mdEuclidean | compute Euclidean distances |
| mdManhattan | compute Manhattan distances |
| message.w.date | Send a message() with a prefix with a data |
| multiply.coo | Multiply two coo objects element-wise |
| naive.fuzzy.simplicial.set | create a simplicial set from a distance object |
| naive.optimize.embedding | modify an existing embedding |
| naive.simplicial.set.embedding | create an embedding of graph into a low-dimensional space |
| optimize_epoch | run one epoch of the umap optimization |
| predict.umap | project data points onto an existing umap embedding |
| print.umap | Display a summary of a umap object |
| print.umap.config | Display contents of a umap configuration |
| print.umap.knn | Display summary of knn.info |
| reduce.coo | Remove some entires in a coo matrix where values are zero |
| set.global.seed | set .Random.seed to a pre-saved value |
| smooth.knn.dist | compute a "smooth" distance to the kth neighbor and... |
| spectator.knn.info | compute knn information for spectators relative to data |
| spectral.eigenvectors | get a set of k eigenvectors for the laplacian of x |
| stop.coo | Stop execution with a custom message |
| subset.coo | Subset a coo |
| t.coo | Transpose a coo matrix |
| umap | Computes a manifold approximation and projection |
| umap.check.config | Validator functions for umap settings |
| umap.check.config.class | Validator for config class component |
| umap.defaults | Default configuration for umap |
| umap.error | stop execution with a custom error message |
| umap.learn | Create a umap embedding using python package umap-learn |
| umap.learn.predict | predict embedding of new data given an existing umap object |
| umap.naive | Create a umap embedding |
| umap.naive.predict | predict embedding of new data given an existing umap object |
| umap.prep.input | Prep primary input as a data matrix |
| umap.small | Create an embedding object compatible with package umap for... |
| umap.warning | create a warning message |
