feat: refactoring, adds Result to most public API

src/decomposition/pca.rs

@@ -37,9 +37,9 @@
 //!                     &[5.2, 2.7, 3.9, 1.4],
 //!                     ]);
 //!
-//! let pca = PCA::new(&iris, 2, Default::default()); // Reduce number of features to 2
+//! let pca = PCA::fit(&iris, 2, Default::default()).unwrap(); // Reduce number of features to 2
 //!
-//! let iris_reduced = pca.transform(&iris);
+//! let iris_reduced = pca.transform(&iris).unwrap();
 //!
 //! ```
 //!
@@ -49,6 +49,7 @@ use std::fmt::Debug;
 
 use serde::{Deserialize, Serialize};
 
+use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
@@ -100,7 +101,11 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
     /// * `data` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     /// * `n_components` - number of components to keep.
     /// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.
-    pub fn new(data: &M, n_components: usize, parameters: PCAParameters) -> PCA<T, M> {
+    pub fn fit(
+        data: &M,
+        n_components: usize,
+        parameters: PCAParameters,
+    ) -> Result<PCA<T, M>, Failed> {
         let (m, n) = data.shape();
 
         let mu = data.column_mean();
@@ -117,7 +122,7 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
         let mut eigenvectors;
 
         if m > n && !parameters.use_correlation_matrix {
-            let svd = x.svd();
+            let svd = x.svd()?;
             eigenvalues = svd.s;
             for i in 0..eigenvalues.len() {
                 eigenvalues[i] = eigenvalues[i] * eigenvalues[i];
@@ -155,7 +160,7 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
                     }
                 }
 
-                let evd = cov.evd(true);
+                let evd = cov.evd(true)?;
 
                 eigenvalues = evd.d;
 
@@ -167,7 +172,7 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
                     }
                 }
             } else {
-                let evd = cov.evd(true);
+                let evd = cov.evd(true)?;
 
                 eigenvalues = evd.d;
 
@@ -189,26 +194,26 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
             }
         }
 
-        PCA {
+        Ok(PCA {
             eigenvectors: eigenvectors,
             eigenvalues: eigenvalues,
             projection: projection.transpose(),
             mu: mu,
             pmu: pmu,
-        }
+        })
     }
 
     /// Run dimensionality reduction for `x`
     /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
-    pub fn transform(&self, x: &M) -> M {
+    pub fn transform(&self, x: &M) -> Result<M, Failed> {
         let (nrows, ncols) = x.shape();
         let (_, n_components) = self.projection.shape();
         if ncols != self.mu.len() {
-            panic!(
+            return Err(Failed::transform(&format!(
                 "Invalid input vector size: {}, expected: {}",
                 ncols,
                 self.mu.len()
-            );
+            )));
         }
 
         let mut x_transformed = x.matmul(&self.projection);
@@ -217,7 +222,7 @@ impl<T: RealNumber, M: Matrix<T>> PCA<T, M> {
                 x_transformed.sub_element_mut(r, c, self.pmu[c]);
             }
         }
-        x_transformed
+        Ok(x_transformed)
     }
 }
 
@@ -372,7 +377,7 @@ mod tests {
             302.04806302399646,
         ];
 
-        let pca = PCA::new(&us_arrests, 4, Default::default());
+        let pca = PCA::fit(&us_arrests, 4, Default::default()).unwrap();
 
         assert!(pca
             .eigenvectors
@@ -383,7 +388,7 @@ mod tests {
             assert!((pca.eigenvalues[i].abs() - expected_eigenvalues[i].abs()).abs() < 1e-8);
         }
 
-        let us_arrests_t = pca.transform(&us_arrests);
+        let us_arrests_t = pca.transform(&us_arrests).unwrap();
 
         assert!(us_arrests_t
             .abs()
@@ -481,13 +486,14 @@ mod tests {
             0.1734300877298357,
         ];
 
-        let pca = PCA::new(
+        let pca = PCA::fit(
             &us_arrests,
             4,
             PCAParameters {
                 use_correlation_matrix: true,
             },
-        );
+        )
+        .unwrap();
 
         assert!(pca
             .eigenvectors
@@ -498,7 +504,7 @@ mod tests {
             assert!((pca.eigenvalues[i].abs() - expected_eigenvalues[i].abs()).abs() < 1e-8);
         }
 
-        let us_arrests_t = pca.transform(&us_arrests);
+        let us_arrests_t = pca.transform(&us_arrests).unwrap();
 
         assert!(us_arrests_t
             .abs()
@@ -530,7 +536,7 @@ mod tests {
             &[5.2, 2.7, 3.9, 1.4],
         ]);
 
-        let pca = PCA::new(&iris, 4, Default::default());
+        let pca = PCA::fit(&iris, 4, Default::default()).unwrap();
 
         let deserialized_pca: PCA<f64, DenseMatrix<f64>> =
             serde_json::from_str(&serde_json::to_string(&pca).unwrap()).unwrap();