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fix: cargo fmt

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This commit is contained in:
Volodymyr Orlov
2020-06-05 17:52:03 -07:00
parent 685be04488
commit a2784d6345
52 changed files with 3342 additions and 2829 deletions
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src/linear/linear_regression.rs
+98 -82
View File
@@ -1,34 +1,32 @@
use std::fmt::Debug;
use serde::{Serialize, Deserialize};
use serde::{Deserialize, Serialize};
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::math::num::FloatExt;
#[derive(Serialize, Deserialize, Debug)]
pub enum LinearRegressionSolver {
QR,
SVD
SVD,
}
#[derive(Serialize, Deserialize, Debug)]
pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
coefficients: M,
intercept: T,
solver: LinearRegressionSolver
solver: LinearRegressionSolver,
}
impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
fn eq(&self, other: &Self) -> bool {
self.coefficients == other.coefficients &&
(self.intercept - other.intercept).abs() <= T::epsilon()
self.coefficients == other.coefficients
&& (self.intercept - other.intercept).abs() <= T::epsilon()
}
}
impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
pub fn fit(x: &M, y: &M::RowVector, solver: LinearRegressionSolver) -> LinearRegression<T, M>{
pub fn fit(x: &M, y: &M::RowVector, solver: LinearRegressionSolver) -> LinearRegression<T, M> {
let y_m = M::from_row_vector(y.clone());
let b = y_m.transpose();
let (x_nrows, num_attributes) = x.shape();
@@ -37,20 +35,20 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
if x_nrows != y_nrows {
panic!("Number of rows of X doesn't match number of rows of Y");
}
let a = x.v_stack(&M::ones(x_nrows, 1));
let w = match solver {
LinearRegressionSolver::QR => a.qr_solve_mut(b),
LinearRegressionSolver::SVD => a.svd_solve_mut(b)
LinearRegressionSolver::SVD => a.svd_solve_mut(b),
};
let wights = w.slice(0..num_attributes, 0..1);
let wights = w.slice(0..num_attributes, 0..1);
LinearRegression {
intercept: w.get(num_attributes, 0),
coefficients: wights,
solver: solver
solver: solver,
}
}
@@ -60,81 +58,54 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
y_hat.transpose().to_row_vector()
}
}
#[cfg(test)]
mod tests {
use super::*;
use super::*;
use crate::linalg::naive::dense_matrix::*;
use nalgebra::{DMatrix, RowDVector};
use crate::linalg::naive::dense_matrix::*;
#[test]
fn ols_fit_predict() {
fn ols_fit_predict() {
let x = DMatrix::from_row_slice(
16,
6,
&[
234.289, 235.6, 159.0, 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,
165.0, 110.929, 1950., 61.187, 328.975, 209.9, 309.9, 112.075, 1951., 63.221,
346.999, 193.2, 359.4, 113.270, 1952., 63.639, 365.385, 187.0, 354.7, 115.094,
1953., 64.989, 363.112, 357.8, 335.0, 116.219, 1954., 63.761, 397.469, 290.4,
304.8, 117.388, 1955., 66.019, 419.180, 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, 121.950,
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 x = DMatrix::from_row_slice(16, 6, &[
234.289, 235.6, 159.0, 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, 165.0, 110.929, 1950., 61.187,
328.975, 209.9, 309.9, 112.075, 1951., 63.221,
346.999, 193.2, 359.4, 113.270, 1952., 63.639,
365.385, 187.0, 354.7, 115.094, 1953., 64.989,
363.112, 357.8, 335.0, 116.219, 1954., 63.761,
397.469, 290.4, 304.8, 117.388, 1955., 66.019,
419.180, 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, 121.950, 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: RowDVector<f64> = RowDVector::from_vec(vec!(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: RowDVector<f64> = RowDVector::from_vec(vec![
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_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
assert!(y.iter().zip(y_hat_qr.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
assert!(y.iter().zip(y_hat_svd.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
assert!(y
.iter()
.zip(y_hat_qr.iter())
.all(|(&a, &b)| (a - b).abs() <= 5.0));
assert!(y
.iter()
.zip(y_hat_svd.iter())
.all(|(&a, &b)| (a - b).abs() <= 5.0));
}
#[test]
fn ols_fit_predict_nalgebra() {
let x = DenseMatrix::from_array(&[
&[234.289, 235.6, 159.0, 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, 165.0, 110.929, 1950., 61.187],
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
&[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
&[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[419.180, 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, 121.950, 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: Vec<f64> = vec!(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_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
assert!(y.iter().zip(y_hat_qr.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
assert!(y.iter().zip(y_hat_svd.iter()).all(|(&a, &b)| (a - b).abs() <= 5.0));
}
#[test]
fn serde(){
fn ols_fit_predict_nalgebra() {
let x = DenseMatrix::from_array(&[
&[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
&[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
@@ -151,14 +122,59 @@ mod tests {
&[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 = vec!(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);
&[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
]);
let y: Vec<f64> = vec![
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_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);
let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
assert!(y
.iter()
.zip(y_hat_qr.iter())
.all(|(&a, &b)| (a - b).abs() <= 5.0));
assert!(y
.iter()
.zip(y_hat_svd.iter())
.all(|(&a, &b)| (a - b).abs() <= 5.0));
}
#[test]
fn serde() {
let x = DenseMatrix::from_array(&[
&[234.289, 235.6, 159.0, 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, 165.0, 110.929, 1950., 61.187],
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
&[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
&[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[419.180, 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, 121.950, 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 = vec![
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 lr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR);
let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
assert_eq!(lr, deserialized_lr);
let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> =
serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
assert_eq!(lr, deserialized_lr);
}
}
}