Release 0.5.0

Linfa's 0.5.0 release adds initial support for the OPTICS algorithm, multinomials logistic regression, and the family of nearest neighbor algorithms. Furthermore, we have improved documentation and introduced hyperparameter checking to all algorithms.

New algorithms

OPTICS is an algorithm for finding density-based clusters. It can produce reachability-plots, hierarchical structure of clusters. Analysing data without prior assumption of any distribution is a common use-case. The algorithm is added to linfa-clustering and an example can be found at linfa-clustering/examples/optics.rs.

Extending logistic regression to the multinomial distribution generalizes it to multiclass problems. This release adds support for multinomial logistic regression to linfa-logistic, you can experiment with the example at linfa-logistic/examples/winequality_multi.rs.

Nearest neighbor search finds the set of neighborhood points to a given sample. It appears in numerous fields of applications as a distance metric provider. (e.g. clustering) This release adds a family of nearest neighbor algorithms, namely Ball tree, K-d tree and naive linear search. You can find an example in the next section.

Improvements

• use least-square solver from ndarray-linalg in linfa-linear
• make clustering algorithms generic over distance metrics
• bump ndarray to 0.15
• introduce ParamGuard trait for explicit and implicit parameter checking (read more in the CONTRIBUTE.md)
• improve documentation in various places

Nearest Neighbors

You can now choose from a growing list of NN implementations. The family provides efficient distance metrics to KMeans, DBSCAN etc. The example shows how to use KDTree nearest neighbor to find all the points in a set of observations that are within a certain range of a candidate point.

You can query nearest points explicitly:

// create a KDTree index consisting of all the points in the observations, using Euclidean distance
let kdtree = CommonNearestNeighbour::KdTree.from_batch(observations, L2Dist)?;
let candidate = observations.row(2);
let points = kdtree.within_range(candidate.view(), range)?;

Or use one of the distance metrics implicitly, here demonstrated for KMeans:

use linfa_nn::distance::LInfDist;

let model = KMeans::params_with(3, rng, LInfDist)
    .max_n_iterations(200)
    .tolerance(1e-5)
    .fit(&dataset)?;