feat: refactors packages layout

2020-03-13 14:30:45 -07:00
parent 4f8318e933
commit 87b6fab795
12 changed files with 10 additions and 27 deletions
@@ -0,0 +1,91 @@
+use crate::linalg::Matrix;
+use std::fmt::Debug;
+
+#[derive(Debug)]
+pub enum LinearRegressionSolver {
+    QR,
+    SVD
+}
+
+#[derive(Debug)]
+pub struct LinearRegression<M: Matrix> {    
+    coefficients: M,
+    intercept: f64,
+    solver: LinearRegressionSolver
+}
+
+impl<M: Matrix> LinearRegression<M> {
+
+    pub fn fit(x: &M, y: &M, solver: LinearRegressionSolver) -> LinearRegression<M>{
+
+        let b = y.transpose();
+        let (x_nrows, num_attributes) = x.shape();
+        let (y_nrows, _) = b.shape();
+
+        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)
+        };
+
+        let wights = w.slice(0..num_attributes, 0..1);  
+
+        LinearRegression {
+            intercept: w.get(num_attributes, 0),
+            coefficients: wights,
+            solver: solver
+        }
+    }
+
+    pub fn predict(&self, x: &M) -> M::RowVector {
+        let (nrows, _) = x.shape();
+        let mut y_hat = x.dot(&self.coefficients);
+        y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
+        y_hat.transpose().to_row_vector()
+    }
+
+}
+
+#[cfg(test)]
+mod tests {    
+    use super::*; 
+    use crate::linalg::naive::dense_matrix::*;
+
+    #[test]
+    fn ols_fit_predict() { 
+
+            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 = 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_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.));
+
+
+    }
+}