Release 0.7.1

Linfa's 0.7.1 release mainly consists of fixes to existing algorithms and the overall crate. The Random Projection algorithm has also been added to linfa-reduction.

Improvements and fixes

• add serde support to linfa-clustering
• add accessors for classes in linfa-logistics
• add accessors for Pca attributes in linfa-reduction
• add wasm-bindgenfeature to use linfa in the browser
• fix covariance update for GaussianMixtureModel in linfa-clustering
• bump ndarray-linalg to 0.16 and argmin to 0.9.0
• bump MSRV to 1.71.1

New algorithms

Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes.

The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset.

See also sklearn.random_projection