feat: integrates with nalgebra

Cargo.toml

@@ -6,6 +6,7 @@ edition = "2018"
 
 [dependencies]
 ndarray = "0.13"
+nalgebra = "0.20.0"
 num-traits = "0.2.11"
 num = "0.2.1"
 rand = "0.7.3"

src/linalg/mod.rs

@@ -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

@@ -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

@@ -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();
 
@@ -65,11 +66,44 @@ impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
 #[cfg(test)]
 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_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);                        
 
-            let y_hat_svd = DenseMatrix::from_row_vector(LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x));
+            let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
-
-            assert!(y.approximate_eq(&y_hat_qr, 5.));
-            assert!(y.approximate_eq(&y_hat_svd, 5.));
             
+            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

@@ -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);           
         
-        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]));
+        let error: f64 = y.into_iter().zip(y_hat.into_iter()).map(|(&a, &b)| (a - b).abs()).sum();
+
+        assert!(error <= 1.0);
         
     }
 

src/math/num.rs

@@ -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;