feat: consolidates API

src/decomposition/svd.rs

@@ -34,7 +34,7 @@
 //!                     &[5.2, 2.7, 3.9, 1.4],
 //!                     ]);
 //!
-//! let svd = SVD::fit(&iris, 2, Default::default()).unwrap(); // Reduce number of features to 2
+//! let svd = SVD::fit(&iris, SVDParameters::default().with_n_components(2)).unwrap(); // Reduce number of features to 2
 //!
 //! let iris_reduced = svd.transform(&iris).unwrap();
 //!
@@ -47,6 +47,7 @@ use std::marker::PhantomData;
 
 use serde::{Deserialize, Serialize};
 
+use crate::api::{Transformer, UnsupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
@@ -67,11 +68,34 @@ impl<T: RealNumber, M: Matrix<T>> PartialEq for SVD<T, M> {
 
 #[derive(Debug, Clone)]
 /// SVD parameters
-pub struct SVDParameters {}
+pub struct SVDParameters {
+    /// Number of components to keep.
+    pub n_components: usize,
+}
 
 impl Default for SVDParameters {
     fn default() -> Self {
-        SVDParameters {}
+        SVDParameters { n_components: 2 }
+    }
+}
+
+impl SVDParameters {
+    /// Number of components to keep.
+    pub fn with_n_components(mut self, n_components: usize) -> Self {
+        self.n_components = n_components;
+        self
+    }
+}
+
+impl<T: RealNumber, M: Matrix<T>> UnsupervisedEstimator<M, SVDParameters> for SVD<T, M> {
+    fn fit(x: &M, parameters: SVDParameters) -> Result<Self, Failed> {
+        SVD::fit(x, parameters)
+    }
+}
+
+impl<T: RealNumber, M: Matrix<T>> Transformer<M> for SVD<T, M> {
+    fn transform(&self, x: &M) -> Result<M, Failed> {
+        self.transform(x)
     }
 }
 
@@ -80,10 +104,10 @@ impl<T: RealNumber, M: Matrix<T>> SVD<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 fit(x: &M, n_components: usize, _: SVDParameters) -> Result<SVD<T, M>, Failed> {
+    pub fn fit(x: &M, parameters: SVDParameters) -> Result<SVD<T, M>, Failed> {
         let (_, p) = x.shape();
 
-        if n_components >= p {
+        if parameters.n_components >= p {
             return Err(Failed::fit(&format!(
                 "Number of components, n_components should be < number of attributes ({})",
                 p
@@ -92,7 +116,7 @@ impl<T: RealNumber, M: Matrix<T>> SVD<T, M> {
 
         let svd = x.svd()?;
 
-        let components = svd.V.slice(0..p, 0..n_components);
+        let components = svd.V.slice(0..p, 0..parameters.n_components);
 
         Ok(SVD {
             components,
@@ -189,7 +213,7 @@ mod tests {
             &[197.28420365, -11.66808306],
             &[293.43187394, 1.91163633],
         ]);
-        let svd = SVD::fit(&x, 2, Default::default()).unwrap();
+        let svd = SVD::fit(&x, Default::default()).unwrap();
 
         let x_transformed = svd.transform(&x).unwrap();
 
@@ -225,7 +249,7 @@ mod tests {
             &[5.2, 2.7, 3.9, 1.4],
         ]);
 
-        let svd = SVD::fit(&iris, 2, Default::default()).unwrap();
+        let svd = SVD::fit(&iris, Default::default()).unwrap();
 
         let deserialized_svd: SVD<f64, DenseMatrix<f64>> =
             serde_json::from_str(&serde_json::to_string(&svd).unwrap()).unwrap();