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 a python package |

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... |

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