# Changelog
## 1.1.6 (2024-08-22)
* [PYTHON] The package now works with *numpy* 2.0.
## 1.1.5 (2023-10-18)
* [BACKWARD INCOMPATIBILITY] [Python and R] Inequality measures
are no longer referred to as inequity measures.
* [BACKWARD INCOMPATIBILITY] [Python and R]
Some external cluster validity measures were renamed:
`adjusted_asymmetric_accuracy` -> `normalized_clustering_accuracy`,
`normalized_accuracy` -> `normalized_pivoted_accuracy`.
* [BACKWARD INCOMPATIBILITY] [Python] `compare_partitions2` has been removed,
as `compare_partitions` and other partition similarity scores
now support both pairs of label vectors `(x, y)` and confusion matrices
`(x=C, y=None)`.
* [Python and R] New parameter to `pair_sets_index`: `clipped`.
* In `normalizing_permutation` and external cluster validity measures,
the input matrices can now be of the type `double`.
* [BUGFIX] [Python] #80: Fixed adjustment for `nmslib_n_neighbors`
in small samples.
* [BUGFIX] [Python] #82: `cluster_validity` submodule not imported.
* [BUGFIX] Some external cluster validity measures
now handle NaNs better and are slightly less prone to round-off errors.
## 1.1.4 (2023-03-31)
* [Python] The GIc algorithm is no longer marked as experimental;
its description is provided in
.
## 1.1.3 (2023-01-17)
* [R] `mst.default` now throws an error if any element in the input matrix
is missing/infinite.
* [Python] The call to `mlpack.emst` that stopped working
with the new version of `mlpack` has been fixed.
## 1.1.2 (2022-09-17)
* [Python and R] `adjusted_asymmetric_accuracy`
now accepts confusion matrices with fewer columns than rows.
Such "missing" columns are now treated as if they were filled with 0s.
* [Python and R] `pair_sets_index`, and `normalized_accuracy` return
the same results for non-symmetric confusion matrices and transposes thereof.
## 1.1.1 (2022-09-15)
* [Python] #75: `nmslib` is now optional.
* [BUILD TIME]: The use of `ssize_t` was not portable.
## 1.1.0 (2022-09-05)
* [Python and R] New function: `adjusted_asymmetric_accuracy`.
* [Python and R] Implementations of the so-called internal cluster
validity measures discussed in
DOI: [10.1016/j.ins.2021.10.004](https://doi.org/10.1016/j.ins.2021.10.004);
see our (GitHub-only) [CVI](https://github.com/gagolews/optim_cvi) package
for R. In particular, the generalised Dunn indices are based on the code
originally authored by Maciej Bartoszuk. Thanks.
Functions added (`cluster_validity` module):
`calinski_harabasz_index`,
`dunnowa_index`,
`generalised_dunn_index`,
`negated_ball_hall_index`,
`negated_davies_bouldin_index`,
`negated_wcss_index`,
`silhouette_index`,
`silhouette_w_index`,
`wcnn_index`.
These cluster validity measures are discussed
in more detail at .
* [BACKWARD INCOMPATIBILITY] `normalized_confusion_matrix`
now solves the maximal assignment problem instead of applying
the somewhat primitive partial pivoting.
* [Python and R] New function: `normalizing_permutation`
* [R] New function: `normalized_confusion_matrix`.
* [Python and R] New parameter to `pair_sets_index`: `simplified`.
* [Python] New parameters to `plots.plot_scatter`:
`axis`, `title`, `xlabel`, `ylabel`, `xlim`, `ylim`.
## 1.0.1 (2022-08-08)
* [GENERAL] A paper on the `genieclust` package is now available:
M. Gagolewski, genieclust: Fast and robust hierarchical clustering,
SoftwareX 15, 100722, 2021, DOI:
[10.1016/j.softx.2021.100722](https://doi.org/10.1016/j.softx.2021.100722).
* [Python] `plots.plot_scatter` now uses a more accessible default palette
(from R 4.0.0).
* [Python and R] New function: `devergottini_index`.
## 1.0.0 (2021-04-22)
* [R] Use `mlpack` instead of `RcppMLPACK` (#72).
This package is merely suggested, not dependent upon.
## 0.9.8 (2021-01-08)
* [Python] Require Python >= 3.7 (implied by `numpy`).
* [Python] Require `nmslib`.
* [R] Use `RcppMLPACK` directly; remove dependency on `emstreeR`.
* [R] Use `tinytest` for unit testing instead of `testthat`.
## 0.9.4 (2020-07-31)
* [BUGFIX] [R] Fix build errors on Solaris.
## 0.9.3 (2020-07-25)
* [BUGFIX] [Python] Add code coverage CI. Fix some minor inconsistencies.
Automate the `bdist` build chain.
* [R] Update DESCRIPTION to meet the CRAN policies.
## 0.9.2 (2020-07-22)
* [BUGFIX] [Python] Fix broken build script for OS X with no OpenMP.
## 0.9.1 (2020-07-18)
* [GENERAL] The package has been completely rewritten.
The core functionality is now implemented in C++ (with OpenMP).
* [GENERAL] Clustering with respect to HDBSCAN*-like
mutual reachability distances is supported.
* [GENERAL] The parallelised Jarnik-Prim algorithm now supports on-the-fly
distance computations. Euclidean minimum spanning tree can be
determined with `mlpack`, which is much faster in low-dimensional spaces.
* [R] R version is now available.
* [Python] [Experimental] The GIc algorithm proposed by Anna Cena
in her 2018 PhD thesis is added.
* [Python] Approximate version based on nearest neighbour graphs produced
by `nmslib` is added.
## 0.1a2 (2018-05-23)
* [Python] Initial PyPI release.