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Ingy döt Net
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3073 changed files with 55820 additions and 4408 deletions
270
Task/Multiple-regression/Rust/multiple-regression.rs
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270
Task/Multiple-regression/Rust/multiple-regression.rs
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use std::fmt;
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#[derive(Debug, Clone)]
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struct Matrix<const R: usize, const C: usize> {
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data: [[f64; C]; R],
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}
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impl<const R: usize, const C: usize> Matrix<R, C> {
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fn new() -> Self {
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Self {
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data: [[0.0; C]; R],
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}
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}
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fn from_rows(values: &[&[f64]]) -> Self {
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let mut matrix = Self::new();
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for (i, row) in values.iter().enumerate().take(R) {
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for (j, &val) in row.iter().enumerate().take(C) {
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matrix.data[i][j] = val;
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}
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}
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matrix
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}
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fn get(&self, row: usize, col: usize) -> f64 {
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self.data[row][col]
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}
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fn set(&mut self, row: usize, col: usize, value: f64) {
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self.data[row][col] = value;
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}
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fn get_row(&self, row: usize) -> [f64; C] {
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self.data[row]
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}
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fn set_row(&mut self, row: usize, values: &[f64; C]) {
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self.data[row] = *values;
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}
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fn multiply<const D: usize>(&self, rhs: &Matrix<C, D>) -> Matrix<R, D> {
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let mut result = Matrix::<R, D>::new();
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for i in 0..R {
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for j in 0..D {
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for k in 0..C {
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let prod = self.get(i, k) * rhs.get(k, j);
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result.set(i, j, result.get(i, j) + prod);
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}
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}
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}
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result
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}
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fn transpose(&self) -> Matrix<C, R> {
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let mut trans = Matrix::<C, R>::new();
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for i in 0..R {
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for j in 0..C {
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trans.set(j, i, self.data[i][j]);
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}
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}
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trans
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}
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fn to_reduced_row_echelon_form(&mut self) {
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let mut lead = 0;
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for r in 0..R {
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if C <= lead {
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return;
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}
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let mut i = r;
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while self.get(i, lead) == 0.0 {
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i += 1;
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if R == i {
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i = r;
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lead += 1;
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if C == lead {
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return;
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}
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}
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}
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// Swap rows
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let temp = self.get_row(i);
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self.set_row(i, &self.get_row(r));
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self.set_row(r, &temp);
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if self.get(r, lead) != 0.0 {
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let div = self.get(r, lead);
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for j in 0..C {
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self.set(r, j, self.get(r, j) / div);
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}
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}
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for k in 0..R {
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if k != r {
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let mult = self.get(k, lead);
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for j in 0..C {
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let prod = self.get(r, j) * mult;
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self.set(k, j, self.get(k, j) - prod);
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}
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}
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}
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lead += 1;
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}
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}
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}
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impl<const N: usize> Matrix<N, N> {
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fn inverse(&self) -> Result<Matrix<N, N>, &'static str> {
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if N == 0 {
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return Err("Cannot invert empty matrix");
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}
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// Create augmented matrix using Vec for dynamic sizing
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let mut aug = vec![vec![0.0; 2 * N]; N];
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// Copy original matrix to left side of augmented matrix
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for i in 0..N {
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for j in 0..N {
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aug[i][j] = self.get(i, j);
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}
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// Add identity matrix to right side
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aug[i][i + N] = 1.0;
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}
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// Perform Gauss-Jordan elimination
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let mut lead = 0;
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for r in 0..N {
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if 2 * N <= lead {
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return Err("Matrix is not invertible");
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}
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let mut i = r;
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while aug[i][lead] == 0.0 {
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i += 1;
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if N == i {
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i = r;
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lead += 1;
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if 2 * N == lead {
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return Err("Matrix is not invertible");
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}
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}
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}
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// Swap rows
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aug.swap(i, r);
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if aug[r][lead] != 0.0 {
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let div = aug[r][lead];
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for j in 0..(2 * N) {
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aug[r][j] /= div;
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}
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}
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for k in 0..N {
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if k != r {
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let mult = aug[k][lead];
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for j in 0..(2 * N) {
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aug[k][j] -= aug[r][j] * mult;
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}
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}
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}
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lead += 1;
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}
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// Extract inverse from right side
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let mut inv = Matrix::<N, N>::new();
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for i in 0..N {
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for j in 0..N {
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inv.set(i, j, aug[i][j + N]);
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}
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}
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Ok(inv)
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}
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}
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impl<const R: usize, const C: usize> fmt::Display for Matrix<R, C> {
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
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for i in 0..R {
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write!(f, "[")?;
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for j in 0..C {
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if j > 0 {
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write!(f, ", ")?;
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}
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write!(f, "{}", self.get(i, j))?;
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}
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writeln!(f, "]")?;
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}
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Ok(())
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}
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}
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fn multiple_regression<const R: usize, const C: usize>(
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y: &[f64; C],
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x: &Matrix<R, C>,
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) -> Result<[f64; R], &'static str> {
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let mut tm = Matrix::<1, C>::new();
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for (i, &val) in y.iter().enumerate() {
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tm.set(0, i, val);
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}
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let cy = tm.transpose();
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let cx = x.transpose();
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let x_cx = x.multiply(&cx);
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let inv = x_cx.inverse()?;
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let result = inv.multiply(&x.multiply(&cy)).transpose();
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Ok(result.get_row(0))
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}
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fn case1() -> Result<(), &'static str> {
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let y = [1.0, 2.0, 3.0, 4.0, 5.0];
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let x = Matrix::<1, 5>::from_rows(&[&[2.0, 1.0, 3.0, 4.0, 5.0]]);
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let v = multiple_regression(&y, &x)?;
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println!("{:?}", v);
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Ok(())
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}
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fn case2() -> Result<(), &'static str> {
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let y = [3.0, 4.0, 5.0];
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let x = Matrix::<2, 3>::from_rows(&[
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&[1.0, 2.0, 1.0],
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&[1.0, 1.0, 2.0],
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]);
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let v = multiple_regression(&y, &x)?;
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println!("{:?}", v);
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Ok(())
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}
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fn case3() -> Result<(), &'static str> {
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let y = [
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52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93, 61.29,
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63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46,
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];
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let a = [
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1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63, 1.65,
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1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83,
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];
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let mut x = Matrix::<3, 15>::new();
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// First row: all ones
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for i in 0..15 {
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x.set(0, i, 1.0);
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}
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// Second row: values from array 'a'
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for (i, &val) in a.iter().enumerate() {
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x.set(1, i, val);
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}
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// Third row: squared values from array 'a'
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for (i, &val) in a.iter().enumerate() {
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x.set(2, i, val * val);
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}
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let v = multiple_regression(&y, &x)?;
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println!("{:?}", v);
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Ok(())
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}
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fn main() -> Result<(), &'static str> {
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case1()?;
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case2()?;
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case3()?;
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Ok(())
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}
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