grid search (#154)

* grid search draft
* hyperparam search for linear estimators

src/model_selection/mod.rs

@@ -91,8 +91,8 @@
 //!
 //! let results = cross_validate(LogisticRegression::fit,   //estimator
 //!                                 &x, &y,                 //data
-//!                                 Default::default(),     //hyperparameters
-//!                                 cv,                     //cross validation split
+//!                                 &Default::default(),     //hyperparameters
+//!                                 &cv,                     //cross validation split
 //!                                 &accuracy).unwrap();    //metric
 //!
 //! println!("Training accuracy: {}, test accuracy: {}",
@@ -201,8 +201,8 @@ pub fn cross_validate<T, M, H, E, K, F, S>(
     fit_estimator: F,
     x: &M,
     y: &M::RowVector,
-    parameters: H,
-    cv: K,
+    parameters: &H,
+    cv: &K,
     score: S,
 ) -> Result<CrossValidationResult<T>, Failed>
 where
@@ -281,6 +281,7 @@ mod tests {
 
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
+
     use crate::metrics::{accuracy, mean_absolute_error};
     use crate::model_selection::kfold::KFold;
     use crate::neighbors::knn_regressor::KNNRegressor;
@@ -362,8 +363,15 @@ mod tests {
             ..KFold::default()
         };
 
-        let results =
-            cross_validate(BiasedEstimator::fit, &x, &y, NoParameters {}, cv, &accuracy).unwrap();
+        let results = cross_validate(
+            BiasedEstimator::fit,
+            &x,
+            &y,
+            &NoParameters {},
+            &cv,
+            &accuracy,
+        )
+        .unwrap();
 
         assert_eq!(0.4, results.mean_test_score());
         assert_eq!(0.4, results.mean_train_score());
@@ -404,8 +412,8 @@ mod tests {
             KNNRegressor::fit,
             &x,
             &y,
-            Default::default(),
-            cv,
+            &Default::default(),
+            &cv,
             &mean_absolute_error,
         )
         .unwrap();