0.5.12

Added vector_to_formula for inverse featurizing

0.5.4

v0.5.4

Made numba fail gracefully if not present

0.5.3

Added the fast EMD implementation, accessible through metric="fast". This is based on the method described here: https://arxiv.org/pdf/1804.01947.pdf

0.5

Merges in the optimimizations made by @dwiddo

Full Featurizer

Adds a full_feature_vector() method to the ElMD() class which returns a complete featurization vector (n=8076) for each composition. Returns the mean, weighted mean, min, max, range, and std. deviation of all available elemental feature lookup tables (excluding permutations of one hot encoded atomic scales).

Fixes pip bugs that occurred in development for versions 0.4.16-18 due to mismatching version numbers in setup.py and init.py

0.4.17

Added full_featurize() to give a complete desciptor utilizing all featurizing dictionaries. Returned features take the weighted mean, mean, min, max, range, std deviation of the featurized elements in each composition

0.4.15

v0.4.15

Reintroduced njit

0.4.7

Merge pull request #11 from lrcfmd/clean_emd_func

Clean emd func and ratio vectors for non mod_petti metrics

0.4.2

Added caching when reading elemental dicts from disk to speed up parsing

0.3.19

Merge pull request #6 from SurgeArrester/master

Reduced IO operations