Files
smartcore/src/linalg/ndarray_bindings.rs
2020-03-31 18:19:20 -07:00

634 lines
16 KiB
Rust

use std::ops::Range;
use std::iter::Sum;
use std::ops::AddAssign;
use std::ops::SubAssign;
use std::ops::MulAssign;
use std::ops::DivAssign;
use ndarray::{Array, ArrayBase, OwnedRepr, Ix2, Ix1, Axis, stack, s};
use ndarray::ScalarOperand;
use crate::math::num::FloatExt;
use crate::linalg::BaseMatrix;
use crate::linalg::Matrix;
use crate::linalg::svd::SVDDecomposableMatrix;
use crate::linalg::evd::EVDDecomposableMatrix;
use crate::linalg::qr::QRDecomposableMatrix;
impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
{
type RowVector = ArrayBase<OwnedRepr<T>, Ix1>;
fn from_row_vector(vec: Self::RowVector) -> Self{
let vec_size = vec.len();
vec.into_shape((1, vec_size)).unwrap()
}
fn to_row_vector(self) -> Self::RowVector{
let vec_size = self.nrows() * self.ncols();
self.into_shape(vec_size).unwrap()
}
fn get(&self, row: usize, col: usize) -> T {
self[[row, col]]
}
fn get_row_as_vec(&self, row: usize) -> Vec<T> {
self.row(row).to_vec()
}
fn get_col_as_vec(&self, col: usize) -> Vec<T> {
self.column(col).to_vec()
}
fn set(&mut self, row: usize, col: usize, x: T) {
self[[row, col]] = x;
}
fn eye(size: usize) -> Self {
Array::eye(size)
}
fn zeros(nrows: usize, ncols: usize) -> Self {
Array::zeros((nrows, ncols))
}
fn ones(nrows: usize, ncols: usize) -> Self {
Array::ones((nrows, ncols))
}
fn to_raw_vector(&self) -> Vec<T> {
self.to_owned().iter().map(|v| *v).collect()
}
fn fill(nrows: usize, ncols: usize, value: T) -> Self {
Array::from_elem((nrows, ncols), value)
}
fn shape(&self) -> (usize, usize) {
(self.nrows(), self.ncols())
}
fn v_stack(&self, other: &Self) -> Self {
stack(Axis(1), &[self.view(), other.view()]).unwrap()
}
fn h_stack(&self, other: &Self) -> Self {
stack(Axis(0), &[self.view(), other.view()]).unwrap()
}
fn dot(&self, other: &Self) -> Self {
self.dot(other)
}
fn vector_dot(&self, other: &Self) -> T {
self.dot(&other.view().reversed_axes())[[0, 0]]
}
fn slice(&self, rows: Range<usize>, cols: Range<usize>) -> Self {
self.slice(s![rows, cols]).to_owned()
}
fn approximate_eq(&self, other: &Self, error: T) -> bool {
(self - other).iter().all(|v| v.abs() <= error)
}
fn add_mut(&mut self, other: &Self) -> &Self {
*self += other;
self
}
fn sub_mut(&mut self, other: &Self) -> &Self {
*self -= other;
self
}
fn mul_mut(&mut self, other: &Self) -> &Self {
*self *= other;
self
}
fn div_mut(&mut self, other: &Self) -> &Self{
*self /= other;
self
}
fn add_scalar_mut(&mut self, scalar: T) -> &Self{
*self += scalar;
self
}
fn sub_scalar_mut(&mut self, scalar: T) -> &Self{
*self -= scalar;
self
}
fn mul_scalar_mut(&mut self, scalar: T) -> &Self{
*self *= scalar;
self
}
fn div_scalar_mut(&mut self, scalar: T) -> &Self{
*self /= scalar;
self
}
fn transpose(&self) -> Self{
self.clone().reversed_axes()
}
fn rand(nrows: usize, ncols: usize) -> Self{
let values: Vec<T> = (0..nrows*ncols).map(|_| {
T::rand()
}).collect();
Array::from_shape_vec((nrows, ncols), values).unwrap()
}
fn norm2(&self) -> T{
self.iter().map(|x| *x * *x).sum::<T>().sqrt()
}
fn norm(&self, p:T) -> T {
if p.is_infinite() && p.is_sign_positive() {
self.iter().fold(T::neg_infinity(), |f, &val| {
let v = val.abs();
if f > v {
f
} else {
v
}
})
} else if p.is_infinite() && p.is_sign_negative() {
self.iter().fold(T::infinity(), |f, &val| {
let v = val.abs();
if f < v {
f
} else {
v
}
})
} else {
let mut norm = T::zero();
for xi in self.iter() {
norm = norm + xi.abs().powf(p);
}
norm.powf(T::one()/p)
}
}
fn column_mean(&self) -> Vec<T> {
self.mean_axis(Axis(0)).unwrap().to_vec()
}
fn div_element_mut(&mut self, row: usize, col: usize, x: T){
self[[row, col]] = self[[row, col]] / x;
}
fn mul_element_mut(&mut self, row: usize, col: usize, x: T){
self[[row, col]] = self[[row, col]] * x;
}
fn add_element_mut(&mut self, row: usize, col: usize, x: T){
self[[row, col]] = self[[row, col]] + x;
}
fn sub_element_mut(&mut self, row: usize, col: usize, x: T){
self[[row, col]] = self[[row, col]] - x;
}
fn negative_mut(&mut self){
*self *= -T::one();
}
fn reshape(&self, nrows: usize, ncols: usize) -> Self{
self.clone().into_shape((nrows, ncols)).unwrap()
}
fn copy_from(&mut self, other: &Self){
self.assign(&other);
}
fn abs_mut(&mut self) -> &Self{
self
}
fn sum(&self) -> T{
self.sum()
}
fn max_diff(&self, other: &Self) -> T{
let mut max_diff = T::zero();
for r in 0..self.nrows() {
for c in 0..self.ncols() {
max_diff = max_diff.max((self[(r, c)] - other[(r, c)]).abs());
}
}
max_diff
}
fn softmax_mut(&mut self){
let max = self.iter().map(|x| x.abs()).fold(T::neg_infinity(), |a, b| a.max(b));
let mut z = T::zero();
for r in 0..self.nrows() {
for c in 0..self.ncols() {
let p = (self[(r, c)] - max).exp();
self.set(r, c, p);
z = z + p;
}
}
for r in 0..self.nrows() {
for c in 0..self.ncols() {
self.set(r, c, self[(r, c)] / z);
}
}
}
fn pow_mut(&mut self, p: T) -> &Self{
for r in 0..self.nrows() {
for c in 0..self.ncols() {
self.set(r, c, self[(r, c)].powf(p));
}
}
self
}
fn argmax(&self) -> Vec<usize>{
let mut res = vec![0usize; self.nrows()];
for r in 0..self.nrows() {
let mut max = T::neg_infinity();
let mut max_pos = 0usize;
for c in 0..self.ncols() {
let v = self[(r, c)];
if max < v {
max = v;
max_pos = c;
}
}
res[r] = max_pos;
}
res
}
fn unique(&self) -> Vec<T> {
let mut result = self.clone().into_raw_vec();
result.sort_by(|a, b| a.partial_cmp(b).unwrap());
result.dedup();
result
}
}
impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> EVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> QRDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> Matrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
#[cfg(test)]
mod tests {
use super::*;
use ndarray::{arr1, arr2, Array2};
#[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 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.v_stack(&a2).h_stack(&a3);
assert_eq!(result, expected);
}
#[test]
fn to_raw_vector() {
let result = arr2(&[[1., 2., 3.], [4., 5., 6.]]).to_raw_vector();
let expected = vec![1., 2., 3., 4., 5., 6.];
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 dot() {
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::dot(&a, &b);
assert_eq!(result, expected);
}
#[test]
fn vector_dot() {
let a = arr2(&[[1., 2., 3.]]);
let b = arr2(&[[1., 2., 3.]]);
assert_eq!(14., a.vector_dot(&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 sum() {
let src = arr2(&[[1., 2., 3.]]);
assert_eq!(src.sum(), 6.);
}
#[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_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 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));
}
}