951 lines
25 KiB
Rust
951 lines
25 KiB
Rust
//! # Connector for ndarray
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//!
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//! If you want to use [ndarray](https://docs.rs/ndarray) matrices and vectors with SmartCore:
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//!
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//! ```
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//! use ndarray::{arr1, arr2};
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//! use smartcore::linear::logistic_regression::*;
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//! // Enable ndarray connector
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//! use smartcore::linalg::ndarray_bindings::*;
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//!
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//! // Iris dataset
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//! let x = arr2(&[
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//! [5.1, 3.5, 1.4, 0.2],
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//! [4.9, 3.0, 1.4, 0.2],
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//! [4.7, 3.2, 1.3, 0.2],
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//! [4.6, 3.1, 1.5, 0.2],
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//! [5.0, 3.6, 1.4, 0.2],
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//! [5.4, 3.9, 1.7, 0.4],
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//! [4.6, 3.4, 1.4, 0.3],
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//! [5.0, 3.4, 1.5, 0.2],
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//! [4.4, 2.9, 1.4, 0.2],
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//! [4.9, 3.1, 1.5, 0.1],
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//! [7.0, 3.2, 4.7, 1.4],
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//! [6.4, 3.2, 4.5, 1.5],
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//! [6.9, 3.1, 4.9, 1.5],
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//! [5.5, 2.3, 4.0, 1.3],
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//! [6.5, 2.8, 4.6, 1.5],
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//! [5.7, 2.8, 4.5, 1.3],
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//! [6.3, 3.3, 4.7, 1.6],
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//! [4.9, 2.4, 3.3, 1.0],
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//! [6.6, 2.9, 4.6, 1.3],
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//! [5.2, 2.7, 3.9, 1.4],
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//! ]);
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//! let y = arr1(&[
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//! 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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//! 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.
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//! ]);
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//!
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//! let lr = LogisticRegression::fit(&x, &y).unwrap();
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//! let y_hat = lr.predict(&x).unwrap();
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//! ```
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use std::iter::Sum;
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use std::ops::AddAssign;
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use std::ops::DivAssign;
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use std::ops::MulAssign;
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use std::ops::Range;
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use std::ops::SubAssign;
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use ndarray::ScalarOperand;
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use ndarray::{s, stack, Array, ArrayBase, Axis, Ix1, Ix2, OwnedRepr};
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use crate::linalg::cholesky::CholeskyDecomposableMatrix;
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use crate::linalg::evd::EVDDecomposableMatrix;
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use crate::linalg::lu::LUDecomposableMatrix;
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use crate::linalg::qr::QRDecomposableMatrix;
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use crate::linalg::stats::MatrixStats;
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use crate::linalg::svd::SVDDecomposableMatrix;
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use crate::linalg::Matrix;
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use crate::linalg::{BaseMatrix, BaseVector};
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use crate::math::num::RealNumber;
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impl<T: RealNumber + ScalarOperand> BaseVector<T> for ArrayBase<OwnedRepr<T>, Ix1> {
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fn get(&self, i: usize) -> T {
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self[i]
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}
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fn set(&mut self, i: usize, x: T) {
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self[i] = x;
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}
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fn len(&self) -> usize {
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self.len()
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}
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fn to_vec(&self) -> Vec<T> {
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self.to_owned().to_vec()
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}
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fn zeros(len: usize) -> Self {
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Array::zeros(len)
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}
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fn ones(len: usize) -> Self {
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Array::ones(len)
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}
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fn fill(len: usize, value: T) -> Self {
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Array::from_elem(len, value)
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}
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fn dot(&self, other: &Self) -> T {
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self.dot(other)
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}
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fn norm2(&self) -> T {
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self.iter().map(|x| *x * *x).sum::<T>().sqrt()
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}
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fn norm(&self, p: T) -> T {
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if p.is_infinite() && p.is_sign_positive() {
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self.iter().fold(T::neg_infinity(), |f, &val| {
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let v = val.abs();
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if f > v {
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f
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} else {
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v
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}
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})
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} else if p.is_infinite() && p.is_sign_negative() {
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self.iter().fold(T::infinity(), |f, &val| {
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let v = val.abs();
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if f < v {
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f
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} else {
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v
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}
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})
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} else {
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let mut norm = T::zero();
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for xi in self.iter() {
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norm += xi.abs().powf(p);
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}
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norm.powf(T::one() / p)
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}
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}
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fn div_element_mut(&mut self, pos: usize, x: T) {
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self[pos] /= x;
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}
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fn mul_element_mut(&mut self, pos: usize, x: T) {
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self[pos] *= x;
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}
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fn add_element_mut(&mut self, pos: usize, x: T) {
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self[pos] += x;
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}
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fn sub_element_mut(&mut self, pos: usize, x: T) {
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self[pos] -= x;
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}
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fn approximate_eq(&self, other: &Self, error: T) -> bool {
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(self - other).iter().all(|v| v.abs() <= error)
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}
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fn add_mut(&mut self, other: &Self) -> &Self {
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*self += other;
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self
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}
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fn sub_mut(&mut self, other: &Self) -> &Self {
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*self -= other;
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self
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}
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fn mul_mut(&mut self, other: &Self) -> &Self {
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*self *= other;
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self
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}
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fn div_mut(&mut self, other: &Self) -> &Self {
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*self /= other;
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self
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}
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fn sum(&self) -> T {
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self.sum()
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}
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fn unique(&self) -> Vec<T> {
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let mut result = self.clone().into_raw_vec();
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result.sort_by(|a, b| a.partial_cmp(b).unwrap());
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result.dedup();
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result
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}
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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type RowVector = ArrayBase<OwnedRepr<T>, Ix1>;
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fn from_row_vector(vec: Self::RowVector) -> Self {
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let vec_size = vec.len();
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vec.into_shape((1, vec_size)).unwrap()
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}
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fn to_row_vector(self) -> Self::RowVector {
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let vec_size = self.nrows() * self.ncols();
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self.into_shape(vec_size).unwrap()
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}
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fn get(&self, row: usize, col: usize) -> T {
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self[[row, col]]
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}
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fn get_row_as_vec(&self, row: usize) -> Vec<T> {
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self.row(row).to_vec()
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}
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fn get_row(&self, row: usize) -> Self::RowVector {
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self.row(row).to_owned()
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}
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fn copy_row_as_vec(&self, row: usize, result: &mut Vec<T>) {
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for (r, e) in self.row(row).iter().enumerate() {
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result[r] = *e;
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}
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}
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fn get_col_as_vec(&self, col: usize) -> Vec<T> {
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self.column(col).to_vec()
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}
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fn copy_col_as_vec(&self, col: usize, result: &mut Vec<T>) {
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for (c, e) in self.column(col).iter().enumerate() {
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result[c] = *e;
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}
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}
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fn set(&mut self, row: usize, col: usize, x: T) {
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self[[row, col]] = x;
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}
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fn eye(size: usize) -> Self {
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Array::eye(size)
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}
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fn zeros(nrows: usize, ncols: usize) -> Self {
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Array::zeros((nrows, ncols))
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}
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fn ones(nrows: usize, ncols: usize) -> Self {
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Array::ones((nrows, ncols))
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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 shape(&self) -> (usize, usize) {
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(self.nrows(), self.ncols())
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}
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fn h_stack(&self, other: &Self) -> Self {
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stack(Axis(1), &[self.view(), other.view()]).unwrap()
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}
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fn v_stack(&self, other: &Self) -> Self {
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stack(Axis(0), &[self.view(), other.view()]).unwrap()
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}
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fn matmul(&self, other: &Self) -> Self {
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self.dot(other)
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}
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fn dot(&self, other: &Self) -> T {
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self.dot(&other.view().reversed_axes())[[0, 0]]
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}
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fn slice(&self, rows: Range<usize>, cols: Range<usize>) -> Self {
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self.slice(s![rows, cols]).to_owned()
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}
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fn approximate_eq(&self, other: &Self, error: T) -> bool {
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(self - other).iter().all(|v| v.abs() <= error)
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}
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fn add_mut(&mut self, other: &Self) -> &Self {
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*self += other;
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self
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}
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fn sub_mut(&mut self, other: &Self) -> &Self {
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*self -= other;
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self
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}
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fn mul_mut(&mut self, other: &Self) -> &Self {
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*self *= other;
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self
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}
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fn div_mut(&mut self, other: &Self) -> &Self {
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*self /= other;
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self
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}
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fn add_scalar_mut(&mut self, scalar: T) -> &Self {
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*self += scalar;
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self
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}
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fn sub_scalar_mut(&mut self, scalar: T) -> &Self {
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*self -= scalar;
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self
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}
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fn mul_scalar_mut(&mut self, scalar: T) -> &Self {
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*self *= scalar;
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self
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}
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fn div_scalar_mut(&mut self, scalar: T) -> &Self {
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*self /= scalar;
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self
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}
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fn transpose(&self) -> Self {
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self.clone().reversed_axes()
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}
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fn rand(nrows: usize, ncols: usize) -> Self {
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let values: Vec<T> = (0..nrows * ncols).map(|_| T::rand()).collect();
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Array::from_shape_vec((nrows, ncols), values).unwrap()
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}
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fn norm2(&self) -> T {
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self.iter().map(|x| *x * *x).sum::<T>().sqrt()
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}
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fn norm(&self, p: T) -> T {
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if p.is_infinite() && p.is_sign_positive() {
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self.iter().fold(T::neg_infinity(), |f, &val| {
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let v = val.abs();
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if f > v {
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f
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} else {
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v
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}
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})
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} else if p.is_infinite() && p.is_sign_negative() {
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self.iter().fold(T::infinity(), |f, &val| {
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let v = val.abs();
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if f < v {
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f
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} else {
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v
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}
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})
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} else {
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let mut norm = T::zero();
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for xi in self.iter() {
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norm += xi.abs().powf(p);
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}
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norm.powf(T::one() / p)
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}
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}
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fn column_mean(&self) -> Vec<T> {
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self.mean_axis(Axis(0)).unwrap().to_vec()
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}
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fn div_element_mut(&mut self, row: usize, col: usize, x: T) {
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self[[row, col]] /= x;
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}
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fn mul_element_mut(&mut self, row: usize, col: usize, x: T) {
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self[[row, col]] *= x;
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}
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fn add_element_mut(&mut self, row: usize, col: usize, x: T) {
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self[[row, col]] += x;
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}
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fn sub_element_mut(&mut self, row: usize, col: usize, x: T) {
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self[[row, col]] -= x;
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}
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fn negative_mut(&mut self) {
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*self *= -T::one();
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}
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fn reshape(&self, nrows: usize, ncols: usize) -> Self {
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self.clone().into_shape((nrows, ncols)).unwrap()
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}
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fn copy_from(&mut self, other: &Self) {
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self.assign(&other);
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}
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fn abs_mut(&mut self) -> &Self {
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for v in self.iter_mut() {
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*v = v.abs()
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}
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self
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}
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fn sum(&self) -> T {
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self.sum()
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}
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fn max(&self) -> T {
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self.iter().fold(T::neg_infinity(), |a, b| a.max(*b))
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}
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fn min(&self) -> T {
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self.iter().fold(T::infinity(), |a, b| a.min(*b))
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}
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fn max_diff(&self, other: &Self) -> T {
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let mut max_diff = T::zero();
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for r in 0..self.nrows() {
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for c in 0..self.ncols() {
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max_diff = max_diff.max((self[(r, c)] - other[(r, c)]).abs());
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}
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}
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max_diff
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}
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fn softmax_mut(&mut self) {
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let max = self
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.iter()
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.map(|x| x.abs())
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.fold(T::neg_infinity(), |a, b| a.max(b));
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let mut z = T::zero();
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for r in 0..self.nrows() {
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for c in 0..self.ncols() {
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let p = (self[(r, c)] - max).exp();
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self.set(r, c, p);
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z += p;
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}
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}
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for r in 0..self.nrows() {
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for c in 0..self.ncols() {
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self.set(r, c, self[(r, c)] / z);
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}
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}
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}
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fn pow_mut(&mut self, p: T) -> &Self {
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for r in 0..self.nrows() {
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for c in 0..self.ncols() {
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self.set(r, c, self[(r, c)].powf(p));
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}
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}
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self
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}
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fn argmax(&self) -> Vec<usize> {
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let mut res = vec![0usize; self.nrows()];
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for r in 0..self.nrows() {
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let mut max = T::neg_infinity();
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let mut max_pos = 0usize;
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for c in 0..self.ncols() {
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let v = self[(r, c)];
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if max < v {
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max = v;
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max_pos = c;
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}
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}
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res[r] = max_pos;
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}
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res
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}
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fn unique(&self) -> Vec<T> {
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let mut result = self.clone().into_raw_vec();
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result.sort_by(|a, b| a.partial_cmp(b).unwrap());
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result.dedup();
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result
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}
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fn cov(&self) -> Self {
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panic!("Not implemented");
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}
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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EVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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QRDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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LUDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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CholeskyDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum>
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MatrixStats<T> for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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impl<T: RealNumber + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> Matrix<T>
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for ArrayBase<OwnedRepr<T>, Ix2>
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{
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::ensemble::random_forest_regressor::*;
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use crate::linear::logistic_regression::*;
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use crate::metrics::mean_absolute_error;
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use ndarray::{arr1, arr2, Array1, Array2};
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#[test]
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fn vec_get_set() {
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let mut result = arr1(&[1., 2., 3.]);
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let expected = arr1(&[1., 5., 3.]);
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result.set(1, 5.);
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assert_eq!(result, expected);
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assert_eq!(5., BaseVector::get(&result, 1));
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}
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#[test]
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fn vec_len() {
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let v = arr1(&[1., 2., 3.]);
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assert_eq!(3, v.len());
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}
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|
|
#[test]
|
|
fn vec_to_vec() {
|
|
let v = arr1(&[1., 2., 3.]);
|
|
assert_eq!(vec![1., 2., 3.], v.to_vec());
|
|
}
|
|
|
|
#[test]
|
|
fn vec_dot() {
|
|
let v1 = arr1(&[1., 2., 3.]);
|
|
let v2 = arr1(&[4., 5., 6.]);
|
|
assert_eq!(32.0, BaseVector::dot(&v1, &v2));
|
|
}
|
|
|
|
#[test]
|
|
fn vec_approximate_eq() {
|
|
let a = arr1(&[1., 2., 3.]);
|
|
let noise = arr1(&[1e-5, 2e-5, 3e-5]);
|
|
assert!(a.approximate_eq(&(&noise + &a), 1e-4));
|
|
assert!(!a.approximate_eq(&(&noise + &a), 1e-5));
|
|
}
|
|
|
|
#[test]
|
|
fn from_to_row_vec() {
|
|
let vec = arr1(&[1., 2., 3.]);
|
|
assert_eq!(Array2::from_row_vector(vec.clone()), arr2(&[[1., 2., 3.]]));
|
|
assert_eq!(
|
|
Array2::from_row_vector(vec.clone()).to_row_vector(),
|
|
arr1(&[1., 2., 3.])
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn col_matrix_to_row_vector() {
|
|
let m: Array2<f64> = BaseMatrix::zeros(10, 1);
|
|
assert_eq!(m.to_row_vector().len(), 10)
|
|
}
|
|
|
|
#[test]
|
|
fn add_mut() {
|
|
let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let a2 = a1.clone();
|
|
let a3 = a1.clone() + a2.clone();
|
|
a1.add_mut(&a2);
|
|
|
|
assert_eq!(a1, a3);
|
|
}
|
|
|
|
#[test]
|
|
fn sub_mut() {
|
|
let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let a2 = a1.clone();
|
|
let a3 = a1.clone() - a2.clone();
|
|
a1.sub_mut(&a2);
|
|
|
|
assert_eq!(a1, a3);
|
|
}
|
|
|
|
#[test]
|
|
fn mul_mut() {
|
|
let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let a2 = a1.clone();
|
|
let a3 = a1.clone() * a2.clone();
|
|
a1.mul_mut(&a2);
|
|
|
|
assert_eq!(a1, a3);
|
|
}
|
|
|
|
#[test]
|
|
fn div_mut() {
|
|
let mut a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let a2 = a1.clone();
|
|
let a3 = a1.clone() / a2.clone();
|
|
a1.div_mut(&a2);
|
|
|
|
assert_eq!(a1, a3);
|
|
}
|
|
|
|
#[test]
|
|
fn div_element_mut() {
|
|
let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
a.div_element_mut(1, 1, 5.);
|
|
|
|
assert_eq!(BaseMatrix::get(&a, 1, 1), 1.);
|
|
}
|
|
|
|
#[test]
|
|
fn mul_element_mut() {
|
|
let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
a.mul_element_mut(1, 1, 5.);
|
|
|
|
assert_eq!(BaseMatrix::get(&a, 1, 1), 25.);
|
|
}
|
|
|
|
#[test]
|
|
fn add_element_mut() {
|
|
let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
a.add_element_mut(1, 1, 5.);
|
|
|
|
assert_eq!(BaseMatrix::get(&a, 1, 1), 10.);
|
|
}
|
|
|
|
#[test]
|
|
fn sub_element_mut() {
|
|
let mut a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
a.sub_element_mut(1, 1, 5.);
|
|
|
|
assert_eq!(BaseMatrix::get(&a, 1, 1), 0.);
|
|
}
|
|
|
|
#[test]
|
|
fn vstack_hstack() {
|
|
let a1 = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let a2 = arr2(&[[7.], [8.]]);
|
|
|
|
let a3 = arr2(&[[9., 10., 11., 12.]]);
|
|
|
|
let expected = arr2(&[[1., 2., 3., 7.], [4., 5., 6., 8.], [9., 10., 11., 12.]]);
|
|
|
|
let result = a1.h_stack(&a2).v_stack(&a3);
|
|
|
|
assert_eq!(result, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn get_set() {
|
|
let mut result = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let expected = arr2(&[[1., 2., 3.], [4., 10., 6.]]);
|
|
|
|
result.set(1, 1, 10.);
|
|
|
|
assert_eq!(result, expected);
|
|
assert_eq!(10., BaseMatrix::get(&result, 1, 1));
|
|
}
|
|
|
|
#[test]
|
|
fn matmul() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
let b = arr2(&[[1., 2.], [3., 4.], [5., 6.]]);
|
|
let expected = arr2(&[[22., 28.], [49., 64.]]);
|
|
let result = BaseMatrix::matmul(&a, &b);
|
|
assert_eq!(result, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn dot() {
|
|
let a = arr2(&[[1., 2., 3.]]);
|
|
let b = arr2(&[[1., 2., 3.]]);
|
|
assert_eq!(14., BaseMatrix::dot(&a, &b));
|
|
}
|
|
|
|
#[test]
|
|
fn slice() {
|
|
let a = arr2(&[
|
|
[1., 2., 3., 1., 2.],
|
|
[4., 5., 6., 3., 4.],
|
|
[7., 8., 9., 5., 6.],
|
|
]);
|
|
let expected = arr2(&[[2., 3.], [5., 6.]]);
|
|
let result = BaseMatrix::slice(&a, 0..2, 1..3);
|
|
assert_eq!(result, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn scalar_ops() {
|
|
let a = arr2(&[[1., 2., 3.]]);
|
|
assert_eq!(&arr2(&[[2., 3., 4.]]), a.clone().add_scalar_mut(1.));
|
|
assert_eq!(&arr2(&[[0., 1., 2.]]), a.clone().sub_scalar_mut(1.));
|
|
assert_eq!(&arr2(&[[2., 4., 6.]]), a.clone().mul_scalar_mut(2.));
|
|
assert_eq!(&arr2(&[[0.5, 1., 1.5]]), a.clone().div_scalar_mut(2.));
|
|
}
|
|
|
|
#[test]
|
|
fn transpose() {
|
|
let m = arr2(&[[1.0, 3.0], [2.0, 4.0]]);
|
|
let expected = arr2(&[[1.0, 2.0], [3.0, 4.0]]);
|
|
let m_transposed = m.transpose();
|
|
assert_eq!(m_transposed, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn norm() {
|
|
let v = arr2(&[[3., -2., 6.]]);
|
|
assert_eq!(v.norm(1.), 11.);
|
|
assert_eq!(v.norm(2.), 7.);
|
|
assert_eq!(v.norm(std::f64::INFINITY), 6.);
|
|
assert_eq!(v.norm(std::f64::NEG_INFINITY), 2.);
|
|
}
|
|
|
|
#[test]
|
|
fn negative_mut() {
|
|
let mut v = arr2(&[[3., -2., 6.]]);
|
|
v.negative_mut();
|
|
assert_eq!(v, arr2(&[[-3., 2., -6.]]));
|
|
}
|
|
|
|
#[test]
|
|
fn reshape() {
|
|
let m_orig = arr2(&[[1., 2., 3., 4., 5., 6.]]);
|
|
let m_2_by_3 = BaseMatrix::reshape(&m_orig, 2, 3);
|
|
let m_result = BaseMatrix::reshape(&m_2_by_3, 1, 6);
|
|
assert_eq!(BaseMatrix::shape(&m_2_by_3), (2, 3));
|
|
assert_eq!(BaseMatrix::get(&m_2_by_3, 1, 1), 5.);
|
|
assert_eq!(BaseMatrix::get(&m_result, 0, 1), 2.);
|
|
assert_eq!(BaseMatrix::get(&m_result, 0, 3), 4.);
|
|
}
|
|
|
|
#[test]
|
|
fn copy_from() {
|
|
let mut src = arr2(&[[1., 2., 3.]]);
|
|
let dst = Array2::<f64>::zeros((1, 3));
|
|
src.copy_from(&dst);
|
|
assert_eq!(src, dst);
|
|
}
|
|
|
|
#[test]
|
|
fn min_max_sum() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.]]);
|
|
assert_eq!(21., a.sum());
|
|
assert_eq!(1., a.min());
|
|
assert_eq!(6., a.max());
|
|
}
|
|
|
|
#[test]
|
|
fn max_diff() {
|
|
let a1 = arr2(&[[1., 2., 3.], [4., -5., 6.]]);
|
|
let a2 = arr2(&[[2., 3., 4.], [1., 0., -12.]]);
|
|
assert_eq!(a1.max_diff(&a2), 18.);
|
|
assert_eq!(a2.max_diff(&a2), 0.);
|
|
}
|
|
|
|
#[test]
|
|
fn softmax_mut() {
|
|
let mut prob: Array2<f64> = arr2(&[[1., 2., 3.]]);
|
|
prob.softmax_mut();
|
|
assert!((BaseMatrix::get(&prob, 0, 0) - 0.09).abs() < 0.01);
|
|
assert!((BaseMatrix::get(&prob, 0, 1) - 0.24).abs() < 0.01);
|
|
assert!((BaseMatrix::get(&prob, 0, 2) - 0.66).abs() < 0.01);
|
|
}
|
|
|
|
#[test]
|
|
fn pow_mut() {
|
|
let mut a = arr2(&[[1., 2., 3.]]);
|
|
a.pow_mut(3.);
|
|
assert_eq!(a, arr2(&[[1., 8., 27.]]));
|
|
}
|
|
|
|
#[test]
|
|
fn argmax() {
|
|
let a = arr2(&[[1., 2., 3.], [-5., -6., -7.], [0.1, 0.2, 0.1]]);
|
|
let res = a.argmax();
|
|
assert_eq!(res, vec![2, 0, 1]);
|
|
}
|
|
|
|
#[test]
|
|
fn unique() {
|
|
let a = arr2(&[[1., 2., 2.], [-2., -6., -7.], [2., 3., 4.]]);
|
|
let res = a.unique();
|
|
assert_eq!(res.len(), 7);
|
|
assert_eq!(res, vec![-7., -6., -2., 1., 2., 3., 4.]);
|
|
}
|
|
|
|
#[test]
|
|
fn get_row_as_vector() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
|
|
let res = a.get_row_as_vec(1);
|
|
assert_eq!(res, vec![4., 5., 6.]);
|
|
}
|
|
|
|
#[test]
|
|
fn get_row() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
|
|
assert_eq!(arr1(&[4., 5., 6.]), a.get_row(1));
|
|
}
|
|
|
|
#[test]
|
|
fn get_col_as_vector() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
|
|
let res = a.get_col_as_vec(1);
|
|
assert_eq!(res, vec![2., 5., 8.]);
|
|
}
|
|
|
|
#[test]
|
|
fn copy_row_col_as_vec() {
|
|
let m = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
|
|
let mut v = vec![0f32; 3];
|
|
|
|
m.copy_row_as_vec(1, &mut v);
|
|
assert_eq!(v, vec!(4., 5., 6.));
|
|
m.copy_col_as_vec(1, &mut v);
|
|
assert_eq!(v, vec!(2., 5., 8.));
|
|
}
|
|
|
|
#[test]
|
|
fn col_mean() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
|
|
let res = a.column_mean();
|
|
assert_eq!(res, vec![4., 5., 6.]);
|
|
}
|
|
|
|
#[test]
|
|
fn eye() {
|
|
let a = arr2(&[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]);
|
|
let res: Array2<f64> = BaseMatrix::eye(3);
|
|
assert_eq!(res, a);
|
|
}
|
|
|
|
#[test]
|
|
fn rand() {
|
|
let m: Array2<f64> = BaseMatrix::rand(3, 3);
|
|
for c in 0..3 {
|
|
for r in 0..3 {
|
|
assert!(m[[r, c]] != 0f64);
|
|
}
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn approximate_eq() {
|
|
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
|
|
let noise = arr2(&[[1e-5, 2e-5, 3e-5], [4e-5, 5e-5, 6e-5], [7e-5, 8e-5, 9e-5]]);
|
|
assert!(a.approximate_eq(&(&noise + &a), 1e-4));
|
|
assert!(!a.approximate_eq(&(&noise + &a), 1e-5));
|
|
}
|
|
|
|
#[test]
|
|
fn abs_mut() {
|
|
let mut a = arr2(&[[1., -2.], [3., -4.]]);
|
|
let expected = arr2(&[[1., 2.], [3., 4.]]);
|
|
a.abs_mut();
|
|
assert_eq!(a, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn lr_fit_predict_iris() {
|
|
let x = arr2(&[
|
|
[5.1, 3.5, 1.4, 0.2],
|
|
[4.9, 3.0, 1.4, 0.2],
|
|
[4.7, 3.2, 1.3, 0.2],
|
|
[4.6, 3.1, 1.5, 0.2],
|
|
[5.0, 3.6, 1.4, 0.2],
|
|
[5.4, 3.9, 1.7, 0.4],
|
|
[4.6, 3.4, 1.4, 0.3],
|
|
[5.0, 3.4, 1.5, 0.2],
|
|
[4.4, 2.9, 1.4, 0.2],
|
|
[4.9, 3.1, 1.5, 0.1],
|
|
[7.0, 3.2, 4.7, 1.4],
|
|
[6.4, 3.2, 4.5, 1.5],
|
|
[6.9, 3.1, 4.9, 1.5],
|
|
[5.5, 2.3, 4.0, 1.3],
|
|
[6.5, 2.8, 4.6, 1.5],
|
|
[5.7, 2.8, 4.5, 1.3],
|
|
[6.3, 3.3, 4.7, 1.6],
|
|
[4.9, 2.4, 3.3, 1.0],
|
|
[6.6, 2.9, 4.6, 1.3],
|
|
[5.2, 2.7, 3.9, 1.4],
|
|
]);
|
|
let y: Array1<f64> = arr1(&[
|
|
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
|
|
]);
|
|
|
|
let lr = LogisticRegression::fit(&x, &y).unwrap();
|
|
|
|
let y_hat = lr.predict(&x).unwrap();
|
|
|
|
let error: f64 = y
|
|
.into_iter()
|
|
.zip(y_hat.into_iter())
|
|
.map(|(&a, &b)| (a - b).abs())
|
|
.sum();
|
|
|
|
assert!(error <= 1.0);
|
|
}
|
|
|
|
#[test]
|
|
fn my_fit_longley_ndarray() {
|
|
let x = arr2(&[
|
|
[234.289, 235.6, 159., 107.608, 1947., 60.323],
|
|
[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
|
|
[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
|
|
[284.599, 335.1, 165., 110.929, 1950., 61.187],
|
|
[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
|
|
[346.999, 193.2, 359.4, 113.27, 1952., 63.639],
|
|
[365.385, 187., 354.7, 115.094, 1953., 64.989],
|
|
[363.112, 357.8, 335., 116.219, 1954., 63.761],
|
|
[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
|
|
[419.18, 282.2, 285.7, 118.734, 1956., 67.857],
|
|
[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
|
|
[444.546, 468.1, 263.7, 121.95, 1958., 66.513],
|
|
[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
|
|
[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
|
|
[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
|
|
[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
|
|
]);
|
|
let y = arr1(&[
|
|
83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
|
|
114.2, 115.7, 116.9,
|
|
]);
|
|
|
|
let y_hat = RandomForestRegressor::fit(
|
|
&x,
|
|
&y,
|
|
RandomForestRegressorParameters {
|
|
max_depth: None,
|
|
min_samples_leaf: 1,
|
|
min_samples_split: 2,
|
|
n_trees: 1000,
|
|
m: Option::None,
|
|
},
|
|
)
|
|
.unwrap()
|
|
.predict(&x)
|
|
.unwrap();
|
|
|
|
assert!(mean_absolute_error(&y, &y_hat) < 1.0);
|
|
}
|
|
}
|