Merge potential next release v0.4 (#187) Breaking Changes
* 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>
This commit is contained in:
Lorenzo
@@ -0,0 +1,286 @@
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use std::fmt::{Debug, Display};
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use std::ops::Range;
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use crate::linalg::basic::arrays::{
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Array as BaseArray, Array2, ArrayView1, ArrayView2, MutArray, MutArrayView2,
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};
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use crate::linalg::traits::cholesky::CholeskyDecomposable;
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use crate::linalg::traits::evd::EVDDecomposable;
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use crate::linalg::traits::lu::LUDecomposable;
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use crate::linalg::traits::qr::QRDecomposable;
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use crate::linalg::traits::svd::SVDDecomposable;
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use crate::numbers::basenum::Number;
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use crate::numbers::realnum::RealNumber;
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use ndarray::{s, Array, ArrayBase, ArrayView, ArrayViewMut, Ix2, OwnedRepr};
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impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
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for ArrayBase<OwnedRepr<T>, Ix2>
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{
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fn get(&self, pos: (usize, usize)) -> &T {
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&self[[pos.0, pos.1]]
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}
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fn shape(&self) -> (usize, usize) {
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(self.nrows(), self.ncols())
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}
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fn is_empty(&self) -> bool {
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self.len() > 0
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}
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fn iterator<'b>(&'b self, axis: u8) -> Box<dyn Iterator<Item = &'b T> + 'b> {
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assert!(
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axis == 1 || axis == 0,
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"For two dimensional array `axis` should be either 0 or 1"
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);
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match axis {
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0 => Box::new(self.iter()),
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_ => Box::new(
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(0..self.ncols()).flat_map(move |c| (0..self.nrows()).map(move |r| &self[[r, c]])),
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),
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}
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}
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}
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impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
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for ArrayBase<OwnedRepr<T>, Ix2>
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{
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fn set(&mut self, pos: (usize, usize), x: T) {
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self[[pos.0, pos.1]] = x
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}
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fn iterator_mut<'b>(&'b mut self, axis: u8) -> Box<dyn Iterator<Item = &'b mut T> + 'b> {
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let ptr = self.as_mut_ptr();
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let stride = self.strides();
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let (rstride, cstride) = (stride[0] as usize, stride[1] as usize);
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match axis {
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0 => Box::new(self.iter_mut()),
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_ => Box::new((0..self.ncols()).flat_map(move |c| {
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(0..self.nrows()).map(move |r| unsafe { &mut *ptr.add(r * rstride + c * cstride) })
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})),
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}
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}
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}
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impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayView<'a, T, Ix2> {
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fn get(&self, pos: (usize, usize)) -> &T {
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&self[[pos.0, pos.1]]
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}
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fn shape(&self) -> (usize, usize) {
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(self.nrows(), self.ncols())
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}
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fn is_empty(&self) -> bool {
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self.len() > 0
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}
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fn iterator<'b>(&'b self, axis: u8) -> Box<dyn Iterator<Item = &'b T> + 'b> {
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assert!(
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axis == 1 || axis == 0,
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"For two dimensional array `axis` should be either 0 or 1"
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);
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match axis {
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0 => Box::new(self.iter()),
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_ => Box::new(
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(0..self.ncols()).flat_map(move |c| (0..self.nrows()).map(move |r| &self[[r, c]])),
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),
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}
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}
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}
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impl<T: Debug + Display + Copy + Sized> Array2<T> for ArrayBase<OwnedRepr<T>, Ix2> {
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fn get_row<'a>(&'a self, row: usize) -> Box<dyn ArrayView1<T> + 'a> {
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Box::new(self.row(row))
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}
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fn get_col<'a>(&'a self, col: usize) -> Box<dyn ArrayView1<T> + 'a> {
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Box::new(self.column(col))
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}
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fn slice<'a>(&'a self, rows: Range<usize>, cols: Range<usize>) -> Box<dyn ArrayView2<T> + 'a> {
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Box::new(self.slice(s![rows, cols]))
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}
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fn slice_mut<'a>(
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&'a mut self,
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rows: Range<usize>,
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cols: Range<usize>,
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) -> Box<dyn MutArrayView2<T> + 'a>
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where
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Self: Sized,
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{
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Box::new(self.slice_mut(s![rows, cols]))
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}
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fn fill(nrows: usize, ncols: usize, value: T) -> Self {
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Array::from_elem([nrows, ncols], value)
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}
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fn from_iterator<I: Iterator<Item = T>>(iter: I, nrows: usize, ncols: usize, axis: u8) -> Self {
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let a = Array::from_iter(iter.take(nrows * ncols))
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.into_shape((nrows, ncols))
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.unwrap();
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match axis {
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0 => a,
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_ => a.reversed_axes().into_shape((nrows, ncols)).unwrap(),
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}
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}
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fn transpose(&self) -> Self {
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self.t().to_owned()
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}
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}
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impl<T: Number + RealNumber> QRDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<T: Number + RealNumber> CholeskyDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<T: Number + RealNumber> EVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<T: Number + RealNumber> LUDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<T: Number + RealNumber> SVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
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impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'a, T, Ix2> {}
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impl<'a, T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
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for ArrayViewMut<'a, T, Ix2>
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{
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fn get(&self, pos: (usize, usize)) -> &T {
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&self[[pos.0, pos.1]]
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}
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fn shape(&self) -> (usize, usize) {
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(self.nrows(), self.ncols())
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}
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fn is_empty(&self) -> bool {
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self.len() > 0
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}
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fn iterator<'b>(&'b self, axis: u8) -> Box<dyn Iterator<Item = &'b T> + 'b> {
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assert!(
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axis == 1 || axis == 0,
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"For two dimensional array `axis` should be either 0 or 1"
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);
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match axis {
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0 => Box::new(self.iter()),
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_ => Box::new(
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(0..self.ncols()).flat_map(move |c| (0..self.nrows()).map(move |r| &self[[r, c]])),
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),
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}
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}
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}
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impl<'a, T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
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for ArrayViewMut<'a, T, Ix2>
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{
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fn set(&mut self, pos: (usize, usize), x: T) {
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self[[pos.0, pos.1]] = x
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}
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fn iterator_mut<'b>(&'b mut self, axis: u8) -> Box<dyn Iterator<Item = &'b mut T> + 'b> {
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let ptr = self.as_mut_ptr();
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let stride = self.strides();
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let (rstride, cstride) = (stride[0] as usize, stride[1] as usize);
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match axis {
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0 => Box::new(self.iter_mut()),
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_ => Box::new((0..self.ncols()).flat_map(move |c| {
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(0..self.nrows()).map(move |r| unsafe { &mut *ptr.add(r * rstride + c * cstride) })
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})),
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}
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}
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}
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impl<'a, T: Debug + Display + Copy + Sized> MutArrayView2<T> for ArrayViewMut<'a, T, Ix2> {}
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impl<'a, T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayViewMut<'a, T, Ix2> {}
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#[cfg(test)]
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mod tests {
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use super::*;
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use ndarray::{arr2, Array2 as NDArray2};
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#[test]
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fn test_get_set() {
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let mut a = arr2(&[[1, 2, 3], [4, 5, 6]]);
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assert_eq!(*BaseArray::get(&a, (1, 1)), 5);
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a.set((1, 1), 9);
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assert_eq!(a, arr2(&[[1, 2, 3], [4, 9, 6]]));
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}
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#[test]
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fn test_iterator() {
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let a = arr2(&[[1, 2, 3], [4, 5, 6]]);
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let v: Vec<i32> = a.iterator(0).map(|&v| v).collect();
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assert_eq!(v, vec!(1, 2, 3, 4, 5, 6));
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}
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#[test]
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fn test_mut_iterator() {
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let mut a = arr2(&[[1, 2, 3], [4, 5, 6]]);
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a.iterator_mut(0).enumerate().for_each(|(i, v)| *v = i);
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assert_eq!(a, arr2(&[[0, 1, 2], [3, 4, 5]]));
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a.iterator_mut(1).enumerate().for_each(|(i, v)| *v = i);
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assert_eq!(a, arr2(&[[0, 2, 4], [1, 3, 5]]));
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}
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#[test]
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fn test_slice() {
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let x = arr2(&[[1, 2, 3], [4, 5, 6]]);
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let x_slice = Array2::slice(&x, 0..2, 1..2);
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assert_eq!((2, 1), x_slice.shape());
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let v: Vec<i32> = x_slice.iterator(0).map(|&v| v).collect();
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assert_eq!(v, [2, 5]);
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}
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#[test]
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fn test_slice_iter() {
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let x = arr2(&[[1, 2, 3], [4, 5, 6]]);
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let x_slice = Array2::slice(&x, 0..2, 0..3);
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assert_eq!(
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x_slice.iterator(0).map(|&v| v).collect::<Vec<i32>>(),
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vec![1, 2, 3, 4, 5, 6]
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);
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assert_eq!(
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x_slice.iterator(1).map(|&v| v).collect::<Vec<i32>>(),
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vec![1, 4, 2, 5, 3, 6]
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);
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}
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#[test]
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fn test_slice_mut_iter() {
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let mut x = arr2(&[[1, 2, 3], [4, 5, 6]]);
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{
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let mut x_slice = Array2::slice_mut(&mut x, 0..2, 0..3);
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x_slice
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.iterator_mut(0)
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.enumerate()
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.for_each(|(i, v)| *v = i);
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}
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assert_eq!(x, arr2(&[[0, 1, 2], [3, 4, 5]]));
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{
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let mut x_slice = Array2::slice_mut(&mut x, 0..2, 0..3);
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x_slice
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.iterator_mut(1)
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.enumerate()
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.for_each(|(i, v)| *v = i);
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}
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assert_eq!(x, arr2(&[[0, 2, 4], [1, 3, 5]]));
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}
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#[test]
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fn test_c_from_iterator() {
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let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
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let a: NDArray2<i32> = Array2::from_iterator(data.clone().into_iter(), 4, 3, 0);
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println!("{}", a);
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let a: NDArray2<i32> = Array2::from_iterator(data.into_iter(), 4, 3, 1);
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println!("{}", a);
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}
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}
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