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feat: integrates with nalgebra

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This commit is contained in:
Volodymyr Orlov
2020-04-06 19:16:37 -07:00
parent eb0c36223f
commit b068295dac
6 changed files with 66 additions and 18 deletions
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src/linalg/mod.rs
+1
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@@ -3,6 +3,7 @@ pub mod qr;
pub mod svd;
pub mod evd;
pub mod ndarray_bindings;
pub mod nalgebra_bindings;
use std::ops::Range;
use std::fmt::{Debug, Display};
src/linalg/ndarray_bindings.rs
+11
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@@ -213,6 +213,9 @@ impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign
}
fn abs_mut(&mut self) -> &Self{
for v in self.iter_mut(){
*v = v.abs()
}
self
}
@@ -631,4 +634,12 @@ mod tests {
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);
}
}
src/linear/linear_regression.rs
+46 -13
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@@ -27,9 +27,10 @@ impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
pub fn fit(x: &M, y: &M, solver: LinearRegressionSolver) -> LinearRegression<T, M>{
pub fn fit(x: &M, y: &M::RowVector, solver: LinearRegressionSolver) -> LinearRegression<T, M>{
let b = y.transpose();
let y_m = M::from_row_vector(y.clone());
let b = y_m.transpose();
let (x_nrows, num_attributes) = x.shape();
let (y_nrows, _) = b.shape();
@@ -63,13 +64,46 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::naive::dense_matrix::*;
mod tests {
use super::*;
use nalgebra::{DMatrix, RowDVector};
use crate::linalg::naive::dense_matrix::*;
#[test]
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 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_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],
@@ -88,15 +122,14 @@ mod tests {
&[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
&[554.894, 400.7, 282.7, 130.081, 1962., 70.551]]);
let y = DenseMatrix::from_array(&[&[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: 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 = DenseMatrix::from_row_vector(LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x));
let y_hat_svd = DenseMatrix::from_row_vector(LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x));
assert!(y.approximate_eq(&y_hat_qr, 5.));
assert!(y.approximate_eq(&y_hat_svd, 5.));
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));
}
@@ -120,7 +153,7 @@ mod tests {
&[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
&[554.894, 400.7, 282.7, 130.081, 1962., 70.551]]);
let y = DenseMatrix::from_array(&[&[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 = 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);
src/linear/logistic_regression.rs
+6 -4
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@@ -272,7 +272,7 @@ impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
mod tests {
use super::*;
use crate::linalg::naive::dense_matrix::*;
use ndarray::{arr1, arr2};
use ndarray::{arr1, arr2, Array1};
#[test]
fn multiclass_objective_f() {
@@ -443,13 +443,15 @@ mod tests {
[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 = arr1(&[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]);
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);
let y_hat = lr.predict(&x);
let y_hat = lr.predict(&x);
let error: f64 = y.into_iter().zip(y_hat.into_iter()).map(|(&a, &b)| (a - b).abs()).sum();
assert_eq!(y_hat, arr1(&[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]));
assert!(error <= 1.0);
}
src/math/num.rs
+1 -1
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@@ -2,7 +2,7 @@ use std::fmt::{Debug, Display};
use num_traits::{Float, FromPrimitive};
use rand::prelude::*;
pub trait FloatExt: Float + FromPrimitive + Debug + Display {
pub trait FloatExt: Float + FromPrimitive + Debug + Display + Copy {
fn copysign(self, sign: Self) -> Self;