grid search (#154)

* grid search draft
* hyperparam search for linear estimators

src/linear/linear_regression.rs

@@ -71,7 +71,7 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone)]
+#[derive(Debug, Clone, Eq, PartialEq)]
 /// Approach to use for estimation of regression coefficients. QR is more efficient but SVD is more stable.
 pub enum LinearRegressionSolverName {
     /// QR decomposition, see [QR](../../linalg/qr/index.html)
@@ -113,6 +113,60 @@ impl Default for LinearRegressionParameters {
     }
 }
 
+/// Linear Regression grid search parameters
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
+pub struct LinearRegressionSearchParameters {
+    /// Solver to use for estimation of regression coefficients.
+    pub solver: Vec<LinearRegressionSolverName>,
+}
+
+/// Linear Regression grid search iterator
+pub struct LinearRegressionSearchParametersIterator {
+    linear_regression_search_parameters: LinearRegressionSearchParameters,
+    current_solver: usize,
+}
+
+impl IntoIterator for LinearRegressionSearchParameters {
+    type Item = LinearRegressionParameters;
+    type IntoIter = LinearRegressionSearchParametersIterator;
+
+    fn into_iter(self) -> Self::IntoIter {
+        LinearRegressionSearchParametersIterator {
+            linear_regression_search_parameters: self,
+            current_solver: 0,
+        }
+    }
+}
+
+impl Iterator for LinearRegressionSearchParametersIterator {
+    type Item = LinearRegressionParameters;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        if self.current_solver == self.linear_regression_search_parameters.solver.len() {
+            return None;
+        }
+
+        let next = LinearRegressionParameters {
+            solver: self.linear_regression_search_parameters.solver[self.current_solver].clone(),
+        };
+
+        self.current_solver += 1;
+
+        Some(next)
+    }
+}
+
+impl Default for LinearRegressionSearchParameters {
+    fn default() -> Self {
+        let default_params = LinearRegressionParameters::default();
+
+        LinearRegressionSearchParameters {
+            solver: vec![default_params.solver],
+        }
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
     fn eq(&self, other: &Self) -> bool {
         self.coefficients == other.coefficients
@@ -200,6 +254,20 @@ mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
 
+    #[test]
+    fn search_parameters() {
+        let parameters = LinearRegressionSearchParameters {
+            solver: vec![
+                LinearRegressionSolverName::QR,
+                LinearRegressionSolverName::SVD,
+            ],
+        };
+        let mut iter = parameters.into_iter();
+        assert_eq!(iter.next().unwrap().solver, LinearRegressionSolverName::QR);
+        assert_eq!(iter.next().unwrap().solver, LinearRegressionSolverName::SVD);
+        assert!(iter.next().is_none());
+    }
+
     #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
     #[test]
     fn ols_fit_predict() {