* First draft of the new n-dimensional arrays + NB use case
* Improves default implementation of multiple Array methods
* Refactors tree methods
* Adds matrix decomposition routines
* Adds matrix decomposition methods to ndarray and nalgebra bindings
* Refactoring + linear regression now uses array2
* Ridge & Linear regression
* LBFGS optimizer & logistic regression
* LBFGS optimizer & logistic regression
* Changes linear methods, metrics and model selection methods to new n-dimensional arrays
* Switches KNN and clustering algorithms to new n-d array layer
* Refactors distance metrics
* Optimizes knn and clustering methods
* Refactors metrics module
* Switches decomposition methods to n-dimensional arrays
* Linalg refactoring - cleanup rng merge (#172)
* Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure.
* Exclude AUC metrics. Needs reimplementation
* Improve developers walkthrough
New traits system in place at `src/numbers` and `src/linalg`
Co-authored-by: Lorenzo <tunedconsulting@gmail.com>
* Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021
* Implement getters to use as_ref() in src/neighbors
* Implement getters to use as_ref() in src/naive_bayes
* Implement getters to use as_ref() in src/linear
* Add Clone to src/naive_bayes
* Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021
* Implement ndarray-bindings. Remove FloatNumber from implementations
* Drop nalgebra-bindings support (as decided in conf-call to go for ndarray)
* Remove benches. Benches will have their own repo at smartcore-benches
* Implement SVC
* Implement SVC serialization. Move search parameters in dedicated module
* Implement SVR. Definitely too slow
* Fix compilation issues for wasm (#202)
Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
* Fix tests (#203)
* Port linalg/traits/stats.rs
* Improve methods naming
* Improve Display for DenseMatrix
Co-authored-by: Montana Low <montanalow@users.noreply.github.com>
Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
* grid search draft
* hyperparam search for linear estimators
* grid search for ensembles
* support grid search for more algos
* grid search for unsupervised algos
* minor cleanup
* feat: Add interface to build `Matrix` from rows.
* feat: Add option to derive `RealNumber` from string.
To construct a `Matrix` from csv, and therefore from string, I need to be able to deserialize a generic `RealNumber` from string.
* feat: Implement `Matrix::read_csv`.
* docs: Fix typo in doc for categorical transformer.
* feat: Add option to take a column from Matrix.
I created the method `Matrix::take_column` that uses the `Matrix::take`-interface to extract a single column from a matrix. I need that feature in the implementation of `StandardScaler`.
* feat: Add `StandardScaler`.
Authored-by: titoeb <timtoebrock@googlemail.com>
Prior to this change, the find function implementation for the
CoverTree class could have potentially returned the wrong result
in cases where there were multiple points in the dataset
equidistant from p. For example, the current test passed for k=3
but failed to produce the correct result for k=4 (it claimed that
3, 4, 5, and 7 were the 4 closest points to 5 in the dataset
rather than 3, 4, 5, and 6). Sorting the neighbors vector before
collecting the first k values from it resolved this issue.
* test: run tests also in wasm targets
* fix: install rand with wasm-bindgen por wasm targets
* fix: use actual usize size to access buffer.
* fix: do not run functions that create files in wasm.
* test: do not run in wasm test that panics.
Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
* feat: Add getters for GaussianNB
* Add classes getter to BernoulliNB
Add classes getter to CategoricalNB
Add classes getter to MultinomialNB
* Add feature_log_prob getter to MultinomialNB
* Add class_count to NB structs
* Add n_features getter for NB
* Add feature_count to MultinomialNB and BernoulliNB
* Add n_categories to CategoricalNB
* Implement feature_log_prob and category_count getter for CategoricalNB
* Implement feature_log_prob for BernoulliNB
