Version 0.2¶
Changelog¶
Adds core set of data descriptors, basic feature preprocessors and first regressor, thoroughly revised api.
New algorithms¶
data descriptors:
ALP
CD
EIF (wrapper requiring optional eif dependency
IF (wrapper for scikit-learn implementation)
LNND
LOF
MD
NND
SVM (wrapper for scikit-learn implementation)
feature preprocessors:
LinearNormaliser
IQRNormaliser
MaxAbsNormaliser
RangeNormaliser
Standardiser
SAE (requires optional tensorflow dependency)
VectorSizeNormaliser
regressors:
FRNN
API changes¶
Uniform ModelFactory pattern: callable algorithms that create callable models.
Preprocessors can be included at initialisation and are applied automatically.
Algorithms are presented no longer by submodule (neighbours, trees, etc), but by type (classifiers, feature preprocessors, etc)
Many changes and additions to secondary functions that can be used to parametrise the main algorithms.