661 lines
18 KiB
Rust
661 lines
18 KiB
Rust
use std::ops::{Range, AddAssign, SubAssign, MulAssign, DivAssign};
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use std::iter::Sum;
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use nalgebra::{MatrixMN, DMatrix, Matrix, Scalar, Dynamic, U1, VecStorage};
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use crate::math::num::FloatExt;
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use crate::linalg::BaseMatrix;
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use crate::linalg::Matrix as SmartCoreMatrix;
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use crate::linalg::svd::SVDDecomposableMatrix;
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use crate::linalg::evd::EVDDecomposableMatrix;
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use crate::linalg::qr::QRDecomposableMatrix;
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impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> BaseMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>>
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{
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type RowVector = MatrixMN<T, U1, Dynamic>;
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fn from_row_vector(vec: Self::RowVector) -> Self{
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Matrix::from_rows(&[vec])
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}
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fn to_row_vector(self) -> Self::RowVector{
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self.row(0).into_owned()
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}
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fn get(&self, row: usize, col: usize) -> T {
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*self.get((row, col)).unwrap()
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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).iter().map(|v| *v).collect()
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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).iter().map(|v| *v).collect()
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}
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fn set(&mut self, row: usize, col: usize, x: T) {
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*self.get_mut((row, col)).unwrap() = x;
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}
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fn eye(size: usize) -> Self {
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DMatrix::identity(size, size)
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}
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fn zeros(nrows: usize, ncols: usize) -> Self {
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DMatrix::zeros(nrows, ncols)
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}
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fn ones(nrows: usize, ncols: usize) -> Self {
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BaseMatrix::fill(nrows, ncols, T::one())
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}
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fn to_raw_vector(&self) -> Vec<T> {
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let (nrows, ncols) = self.shape();
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let mut result = vec![T::zero(); nrows * ncols];
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for (i, row) in self.row_iter().enumerate() {
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for (j, v) in row.iter().enumerate() {
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result[i * ncols + j] = *v;
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}
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}
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result
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}
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fn fill(nrows: usize, ncols: usize, value: T) -> Self {
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let mut m = DMatrix::zeros(nrows, ncols);
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m.fill(value);
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m
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}
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fn shape(&self) -> (usize, usize) {
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self.shape()
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}
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fn v_stack(&self, other: &Self) -> Self {
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let mut columns = Vec::new();
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for r in 0..self.ncols(){
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columns.push(self.column(r));
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}
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for r in 0..other.ncols(){
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columns.push(other.column(r));
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}
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Matrix::from_columns(&columns)
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}
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fn h_stack(&self, other: &Self) -> Self {
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let mut rows = Vec::new();
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for r in 0..self.nrows(){
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rows.push(self.row(r));
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}
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for r in 0..other.nrows(){
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rows.push(other.row(r));
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}
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Matrix::from_rows(&rows)
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}
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fn dot(&self, other: &Self) -> Self {
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self * other
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}
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fn vector_dot(&self, other: &Self) -> T {
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self.dot(other)
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}
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fn slice(&self, rows: Range<usize>, cols: Range<usize>) -> Self {
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self.slice_range(rows, cols).into_owned()
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}
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fn approximate_eq(&self, other: &Self, error: T) -> bool {
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assert!(self.shape() == other.shape());
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self.iter()
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.zip(other.iter())
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.all(|(a, b)| (*a - *b).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.component_mul_assign(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.component_div_assign(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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Matrix::add_scalar_mut(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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Matrix::add_scalar_mut(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.transpose()
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}
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fn rand(nrows: usize, ncols: usize) -> Self{
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DMatrix::from_iterator(nrows, ncols, (0..nrows*ncols).map(|_| {
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T::rand()
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}))
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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 = 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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let mut res = Vec::new();
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for column in self.column_iter() {
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let mut sum = T::zero();
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let mut count = 0;
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for v in column.iter() {
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sum += *v;
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count += 1;
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}
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res.push(sum / T::from(count).unwrap());
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}
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res
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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.get_mut((row, col)).unwrap() = *self.get((row, col)).unwrap() / 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.get_mut((row, col)).unwrap() = *self.get((row, col)).unwrap() * 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.get_mut((row, col)).unwrap() = *self.get((row, col)).unwrap() + 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.get_mut((row, col)).unwrap() = *self.get((row, col)).unwrap() - 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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DMatrix::from_row_slice(nrows, ncols, &self.to_raw_vector())
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}
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fn copy_from(&mut self, other: &Self){
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Matrix::copy_from(self, 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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let mut sum = T::zero();
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for v in self.iter(){
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sum += *v;
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}
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sum
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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.iter().map(|x| x.abs()).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 = 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 v in self.iter_mut(){
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*v = v.powf(p)
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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: Vec<T> = self.iter().map(|v| *v).collect();
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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: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> SVDDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
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impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> EVDDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
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impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> QRDecomposableMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
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impl<T: FloatExt + Scalar + AddAssign + SubAssign + MulAssign + DivAssign + Sum + 'static> SmartCoreMatrix<T> for Matrix<T, Dynamic, Dynamic, VecStorage<T, Dynamic, Dynamic>> {}
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#[cfg(test)]
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mod tests {
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use super::*;
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use nalgebra::{Matrix2x3, DMatrix, RowDVector};
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#[test]
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fn get_set_dynamic() {
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let mut m = DMatrix::from_row_slice(
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2,
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3,
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&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
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);
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let expected = Matrix2x3::new(1., 2., 3., 4.,
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10., 6.);
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m.set(1, 1, 10.);
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assert_eq!(m, expected);
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assert_eq!(10., BaseMatrix::get(&m, 1, 1));
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}
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#[test]
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fn zeros() {
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let expected = DMatrix::from_row_slice(
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2,
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2,
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&[0., 0., 0., 0.],
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);
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let m:DMatrix<f64> = BaseMatrix::zeros(2, 2);
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assert_eq!(m, expected);
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}
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#[test]
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fn ones() {
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let expected = DMatrix::from_row_slice(
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2,
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2,
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&[1., 1., 1., 1.],
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);
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let m:DMatrix<f64> = BaseMatrix::ones(2, 2);
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assert_eq!(m, expected);
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}
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#[test]
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fn eye(){
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let expected = DMatrix::from_row_slice(3, 3, &[1., 0., 0., 0., 1., 0., 0., 0., 1.]);
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let m: DMatrix<f64> = BaseMatrix::eye(3);
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assert_eq!(m, expected);
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}
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#[test]
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fn shape() {
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let m:DMatrix<f64> = BaseMatrix::zeros(5, 10);
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let (nrows, ncols) = m.shape();
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assert_eq!(nrows, 5);
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assert_eq!(ncols, 10);
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}
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#[test]
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fn scalar_add_sub_mul_div(){
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let mut m = DMatrix::from_row_slice(
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2,
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3,
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&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
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);
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let expected = DMatrix::from_row_slice(
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2,
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3,
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&[0.6, 0.8, 1., 1.2, 1.4, 1.6],
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);
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m.add_scalar_mut(3.0);
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m.sub_scalar_mut(1.0);
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m.mul_scalar_mut(2.0);
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m.div_scalar_mut(10.0);
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assert_eq!(m, expected);
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}
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#[test]
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fn add_sub_mul_div(){
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let mut m = DMatrix::from_row_slice(
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2,
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2,
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&[1.0, 2.0, 3.0, 4.0],
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);
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let a = DMatrix::from_row_slice(
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2,
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2,
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&[1.0, 2.0, 3.0, 4.0],
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);
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let b: DMatrix<f64> = BaseMatrix::fill(2, 2, 10.);
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let expected = DMatrix::from_row_slice(
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2,
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2,
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&[0.1, 0.6, 1.5, 2.8],
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);
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m.add_mut(&a);
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m.mul_mut(&a);
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m.sub_mut(&a);
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m.div_mut(&b);
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assert_eq!(m, expected);
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}
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#[test]
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fn to_from_row_vector(){
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let v = RowDVector::from_vec(vec!(1., 2., 3., 4.));
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let expected = v.clone();
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let m: DMatrix<f64> = BaseMatrix::from_row_vector(v);
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assert_eq!(m.to_row_vector(), expected);
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}
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#[test]
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fn get_row_col_as_vec(){
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let m = DMatrix::from_row_slice(
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3,
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3,
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&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
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);
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assert_eq!(m.get_row_as_vec(1), vec!(4., 5., 6.));
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assert_eq!(m.get_col_as_vec(1), vec!(2., 5., 8.));
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}
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#[test]
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fn to_raw_vector(){
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let m = DMatrix::from_row_slice(
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2,
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3,
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&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
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);
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assert_eq!(m.to_raw_vector(), vec!(1., 2., 3., 4., 5., 6.));
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}
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#[test]
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fn element_add_sub_mul_div(){
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let mut m = DMatrix::from_row_slice(
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2,
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2,
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&[1.0, 2.0, 3.0, 4.0],
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);
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let expected = DMatrix::from_row_slice(
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2,
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2,
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&[4., 1., 6., 0.4],
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);
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m.add_element_mut(0, 0, 3.0);
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m.sub_element_mut(0, 1, 1.0);
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m.mul_element_mut(1, 0, 2.0);
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m.div_element_mut(1, 1, 10.0);
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assert_eq!(m, expected);
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}
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#[test]
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fn vstack_hstack() {
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let m1 = DMatrix::from_row_slice(2, 3, &[1., 2., 3., 4., 5., 6.]);
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let m2 = DMatrix::from_row_slice(2, 1, &[ 7., 8.]);
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let m3 = DMatrix::from_row_slice(1, 4, &[9., 10., 11., 12.]);
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let expected = DMatrix::from_row_slice(3, 4, &[1., 2., 3., 7., 4., 5., 6., 8., 9., 10., 11., 12.]);
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let result = m1.v_stack(&m2).h_stack(&m3);
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assert_eq!(result, expected);
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}
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#[test]
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fn dot() {
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let a = DMatrix::from_row_slice(2, 3, &[1., 2., 3., 4., 5., 6.]);
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let b = DMatrix::from_row_slice(3, 2, &[1., 2., 3., 4., 5., 6.]);
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let expected = DMatrix::from_row_slice(2, 2, &[22., 28., 49., 64.]);
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let result = BaseMatrix::dot(&a, &b);
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assert_eq!(result, expected);
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}
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#[test]
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fn vector_dot() {
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let a = DMatrix::from_row_slice(1, 3, &[1., 2., 3.]);
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let b = DMatrix::from_row_slice(1, 3, &[1., 2., 3.]);
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assert_eq!(14., a.vector_dot(&b));
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}
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#[test]
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fn slice() {
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let a = DMatrix::from_row_slice(3, 5, &[1., 2., 3., 1., 2., 4., 5., 6., 3., 4., 7., 8., 9., 5., 6.]);
|
|
let expected = DMatrix::from_row_slice(2, 2, &[2., 3., 5., 6.]);
|
|
let result = BaseMatrix::slice(&a, 0..2, 1..3);
|
|
assert_eq!(result, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn approximate_eq() {
|
|
let a = DMatrix::from_row_slice(3, 3, &[1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
|
let noise = DMatrix::from_row_slice(3, 3, &[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 negative_mut() {
|
|
let mut v = DMatrix::from_row_slice(1, 3, &[3., -2., 6.]);
|
|
v.negative_mut();
|
|
assert_eq!(v, DMatrix::from_row_slice(1, 3, &[-3., 2., -6.]));
|
|
}
|
|
|
|
#[test]
|
|
fn transpose() {
|
|
let m = DMatrix::from_row_slice(2, 2, &[1.0, 3.0, 2.0, 4.0]);
|
|
let expected = DMatrix::from_row_slice(2, 2, &[1.0, 2.0, 3.0, 4.0]);
|
|
let m_transposed = m.transpose();
|
|
assert_eq!(m_transposed, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn rand() {
|
|
let m: DMatrix<f64> = BaseMatrix::rand(3, 3);
|
|
for c in 0..3 {
|
|
for r in 0..3 {
|
|
assert!(*m.get((r, c)).unwrap() != 0f64);
|
|
}
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn norm() {
|
|
let v = DMatrix::from_row_slice(1, 3, &[3., -2., 6.]);
|
|
assert_eq!(BaseMatrix::norm(&v, 1.), 11.);
|
|
assert_eq!(BaseMatrix::norm(&v, 2.), 7.);
|
|
assert_eq!(BaseMatrix::norm(&v, std::f64::INFINITY), 6.);
|
|
assert_eq!(BaseMatrix::norm(&v, std::f64::NEG_INFINITY), 2.);
|
|
}
|
|
|
|
#[test]
|
|
fn col_mean(){
|
|
let a = DMatrix::from_row_slice(3, 3, &[1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
|
let res = BaseMatrix::column_mean(&a);
|
|
assert_eq!(res, vec![4., 5., 6.]);
|
|
}
|
|
|
|
#[test]
|
|
fn reshape() {
|
|
let m_orig = DMatrix::from_row_slice(1, 6, &[1., 2., 3., 4., 5., 6.]);
|
|
let m_2_by_3 = m_orig.reshape(2, 3);
|
|
let m_result = m_2_by_3.reshape(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 = DMatrix::from_row_slice(1, 3, &[1., 2., 3.]);
|
|
let dst = BaseMatrix::zeros(1, 3);
|
|
src.copy_from(&dst);
|
|
assert_eq!(src, dst);
|
|
}
|
|
|
|
#[test]
|
|
fn abs_mut() {
|
|
let mut a = DMatrix::from_row_slice(2, 2, &[1., -2., 3., -4.]);
|
|
let expected = DMatrix::from_row_slice(2, 2, &[1., 2., 3., 4.]);
|
|
a.abs_mut();
|
|
assert_eq!(a, expected);
|
|
}
|
|
|
|
#[test]
|
|
fn sum() {
|
|
let a = DMatrix::from_row_slice(1, 3, &[1., 2., 3.]);
|
|
assert_eq!(a.sum(), 6.);
|
|
}
|
|
|
|
#[test]
|
|
fn max_diff() {
|
|
let a1 = DMatrix::from_row_slice(2, 3, &[1., 2., 3., 4., -5., 6.]);
|
|
let a2 = DMatrix::from_row_slice(2, 3, &[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: DMatrix<f64> = DMatrix::from_row_slice(1, 3, &[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 = DMatrix::from_row_slice(1, 3, &[1., 2., 3.]);
|
|
a.pow_mut(3.);
|
|
assert_eq!(a, DMatrix::from_row_slice(1, 3, &[1., 8., 27.]));
|
|
}
|
|
|
|
#[test]
|
|
fn argmax(){
|
|
let a = DMatrix::from_row_slice(3, 3, &[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 = DMatrix::from_row_slice(3, 3, &[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.]);
|
|
}
|
|
|
|
} |