CLAM: Clustered Learning of Approximate Manifolds (v0.11.1)

CLAM is a Rust/Python library for learning approximate manifolds from data. It is designed to be fast, memory-efficient, easy to use, and scalable for big data applications.

CLAM provides utilities for fast search (CAKES) and anomaly detection (CHAODA).

Installation

Python

> python3 -m pip install abd_clam

Rust

> cargo add abd_clam

Usage

Python

from abd_clam.search import CAKES
from abd_clam.utils import synthetic_datasets

# Get the data.
data, _ = synthetic_datasets.bullseye()
# data is a numpy.ndarray in this case but it could just as easily be a numpy.memmap if your data do fit in RAM.
# We used numpy memmaps for the research, though they impose file-IO costs.

model = CAKES(data, 'euclidean')
# The Search class provides the functionality described in our [CHESS paper](https://arxiv.org/abs/1908.08551).

model.build(max_depth=50)
# Build the search tree to depth of 50.
# This method can be called again with a higher depth, if needed.

query, radius = data[0], 0.5

rnn_results = model.rnn_search(query, radius)
# This is how we perform ranged nearest neighbors search with radius 0.5 around the query.
# The results are returned as a dictionary whose keys are indices into the data array and whose values are the distance to the query.

knn_results = model.knn_search(query, 10)
# This is how we perform k-nearest neighbors search for the 10 nearest neighbors of query.

# TODO: Provide snippets for using CHAODA

Contributing

Pull requests and bug reports are welcome. For major changes, please open an issue to discuss what you would like to change.

License

MIT

Citation

TODO