feat: refactoring, adds Result to most public API

src/linear/linear_regression.rs

@@ -47,9 +47,9 @@
 //!
 //! let lr = LinearRegression::fit(&x, &y, LinearRegressionParameters {
 //!                        solver: LinearRegressionSolverName::QR, // or SVD
-//!          });
+//!          }).unwrap();
 //!
-//! let y_hat = lr.predict(&x);
+//! let y_hat = lr.predict(&x).unwrap();
 //! ```
 //!
 //! ## References:
@@ -64,6 +64,7 @@ use std::fmt::Debug;
 
 use serde::{Deserialize, Serialize};
 
+use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
@@ -115,39 +116,41 @@ impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M> {
         x: &M,
         y: &M::RowVector,
         parameters: LinearRegressionParameters,
-    ) -> LinearRegression<T, M> {
+    ) -> Result<LinearRegression<T, M>, Failed> {
         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();
 
         if x_nrows != y_nrows {
-            panic!("Number of rows of X doesn't match number of rows of Y");
+            return Err(Failed::fit(&format!(
+                "Number of rows of X doesn't match number of rows of Y"
+            )));
         }
 
         let a = x.h_stack(&M::ones(x_nrows, 1));
 
         let w = match parameters.solver {
-            LinearRegressionSolverName::QR => a.qr_solve_mut(b),
-            LinearRegressionSolverName::SVD => a.svd_solve_mut(b),
+            LinearRegressionSolverName::QR => a.qr_solve_mut(b)?,
+            LinearRegressionSolverName::SVD => a.svd_solve_mut(b)?,
         };
 
         let wights = w.slice(0..num_attributes, 0..1);
 
-        LinearRegression {
+        Ok(LinearRegression {
             intercept: w.get(num_attributes, 0),
             coefficients: wights,
             solver: parameters.solver,
-        }
+        })
     }
 
     /// Predict target values from `x`
     /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
-    pub fn predict(&self, x: &M) -> M::RowVector {
+    pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
         let (nrows, _) = x.shape();
         let mut y_hat = x.matmul(&self.coefficients);
         y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
-        y_hat.transpose().to_row_vector()
+        Ok(y_hat.transpose().to_row_vector())
     }
 
     /// Get estimates regression coefficients
@@ -199,9 +202,12 @@ mod tests {
                 solver: LinearRegressionSolverName::QR,
             },
         )
-        .predict(&x);
+        .and_then(|lr| lr.predict(&x))
+        .unwrap();
 
-        let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
+        let y_hat_svd = LinearRegression::fit(&x, &y, Default::default())
+            .and_then(|lr| lr.predict(&x))
+            .unwrap();
 
         assert!(y
             .iter()
@@ -239,7 +245,7 @@ mod tests {
             114.2, 115.7, 116.9,
         ];
 
-        let lr = LinearRegression::fit(&x, &y, Default::default());
+        let lr = LinearRegression::fit(&x, &y, Default::default()).unwrap();
 
         let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> =
             serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();