Dtaidistance

Latest version: v2.3.12

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2.3.10

- Improved compilation for M1/M2, and Py3.7
- Improved plotting warping paths
- Improved kmeans clustering for ndim

2.3.5

- Improved visualisation (single axis, provide Figure)
- Fixed compilation with OpenMP for macos Monterey
- Unified arguments across different methods by probberechts

2.3.4

- Additional clustering techniques. E.g. DTW Barycenter Averaging for clustering, k-medoids.
- Subsequence search and local concurrences.
- Improved Windows compatibility.
- Anaconda release (by m-rossi )
- Improved visualisations

2.0.0

New in v2:

- Numpy is now an optional dependency, also to compile the C library
(only Cython is required).
- Small optimizations throughout the C code to improve speed.
- The consistent use of `size_t` instead of `int` allows for larger data structures on 64 bit
machines and be more compatible with Numpy.
- The parallelization is now implemented directly in C (included if OpenMP is installed).
- The `max_dist` argument turned out to be similar to Silva and Batista's work
on PrunedDTW [7]. The toolbox now implements a version that is equal to PrunedDTW
since it prunes more partial distances. Additionally, a `use_pruning` argument
is added to automatically set `max_dist` to the Euclidean distance, as suggested
by Silva and Batista, to speed up the computation (a new method `ub_euclidean` is available).
- Support in the C library for multi-dimensional sequences in the `dtaidistance.dtw_ndim`
package.

1.2.2

1.1.2

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