Complete grid search params (#166)

* grid search draft

* hyperparam search for linear estimators

* grid search for ensembles

* support grid search for more algos

* grid search for unsupervised algos

* minor cleanup

src/cluster/dbscan.rs

@@ -109,6 +109,103 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> DBSCANParameters<T, D> {
     }
 }
 
+/// DBSCAN grid search parameters
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
+pub struct DBSCANSearchParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
+    /// a function that defines a distance between each pair of point in training data.
+    /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait.
+    /// See [`Distances`](../../math/distance/struct.Distances.html) for a list of available functions.
+    pub distance: Vec<D>,
+    /// The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.
+    pub min_samples: Vec<usize>,
+    /// The maximum distance between two samples for one to be considered as in the neighborhood of the other.
+    pub eps: Vec<T>,
+    /// KNN algorithm to use.
+    pub algorithm: Vec<KNNAlgorithmName>,
+}
+
+/// DBSCAN grid search iterator
+pub struct DBSCANSearchParametersIterator<T: RealNumber, D: Distance<Vec<T>, T>> {
+    dbscan_search_parameters: DBSCANSearchParameters<T, D>,
+    current_distance: usize,
+    current_min_samples: usize,
+    current_eps: usize,
+    current_algorithm: usize,
+}
+
+impl<T: RealNumber, D: Distance<Vec<T>, T>> IntoIterator for DBSCANSearchParameters<T, D> {
+    type Item = DBSCANParameters<T, D>;
+    type IntoIter = DBSCANSearchParametersIterator<T, D>;
+
+    fn into_iter(self) -> Self::IntoIter {
+        DBSCANSearchParametersIterator {
+            dbscan_search_parameters: self,
+            current_distance: 0,
+            current_min_samples: 0,
+            current_eps: 0,
+            current_algorithm: 0,
+        }
+    }
+}
+
+impl<T: RealNumber, D: Distance<Vec<T>, T>> Iterator for DBSCANSearchParametersIterator<T, D> {
+    type Item = DBSCANParameters<T, D>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        if self.current_distance == self.dbscan_search_parameters.distance.len()
+            && self.current_min_samples == self.dbscan_search_parameters.min_samples.len()
+            && self.current_eps == self.dbscan_search_parameters.eps.len()
+            && self.current_algorithm == self.dbscan_search_parameters.algorithm.len()
+        {
+            return None;
+        }
+
+        let next = DBSCANParameters {
+            distance: self.dbscan_search_parameters.distance[self.current_distance].clone(),
+            min_samples: self.dbscan_search_parameters.min_samples[self.current_min_samples],
+            eps: self.dbscan_search_parameters.eps[self.current_eps],
+            algorithm: self.dbscan_search_parameters.algorithm[self.current_algorithm].clone(),
+        };
+
+        if self.current_distance + 1 < self.dbscan_search_parameters.distance.len() {
+            self.current_distance += 1;
+        } else if self.current_min_samples + 1 < self.dbscan_search_parameters.min_samples.len() {
+            self.current_distance = 0;
+            self.current_min_samples += 1;
+        } else if self.current_eps + 1 < self.dbscan_search_parameters.eps.len() {
+            self.current_distance = 0;
+            self.current_min_samples = 0;
+            self.current_eps += 1;
+        } else if self.current_algorithm + 1 < self.dbscan_search_parameters.algorithm.len() {
+            self.current_distance = 0;
+            self.current_min_samples = 0;
+            self.current_eps = 0;
+            self.current_algorithm += 1;
+        } else {
+            self.current_distance += 1;
+            self.current_min_samples += 1;
+            self.current_eps += 1;
+            self.current_algorithm += 1;
+        }
+
+        Some(next)
+    }
+}
+
+impl<T: RealNumber> Default for DBSCANSearchParameters<T, Euclidian> {
+    fn default() -> Self {
+        let default_params = DBSCANParameters::default();
+
+        DBSCANSearchParameters {
+            distance: vec![default_params.distance],
+            min_samples: vec![default_params.min_samples],
+            eps: vec![default_params.eps],
+            algorithm: vec![default_params.algorithm],
+        }
+    }
+}
+
 impl<T: RealNumber, D: Distance<Vec<T>, T>> PartialEq for DBSCAN<T, D> {
     fn eq(&self, other: &Self) -> bool {
         self.cluster_labels.len() == other.cluster_labels.len()
@@ -268,6 +365,29 @@ mod tests {
     #[cfg(feature = "serde")]
     use crate::math::distance::euclidian::Euclidian;
 
+    #[test]
+    fn search_parameters() {
+        let parameters = DBSCANSearchParameters {
+            min_samples: vec![10, 100],
+            eps: vec![1., 2.],
+            ..Default::default()
+        };
+        let mut iter = parameters.into_iter();
+        let next = iter.next().unwrap();
+        assert_eq!(next.min_samples, 10);
+        assert_eq!(next.eps, 1.);
+        let next = iter.next().unwrap();
+        assert_eq!(next.min_samples, 100);
+        assert_eq!(next.eps, 1.);
+        let next = iter.next().unwrap();
+        assert_eq!(next.min_samples, 10);
+        assert_eq!(next.eps, 2.);
+        let next = iter.next().unwrap();
+        assert_eq!(next.min_samples, 100);
+        assert_eq!(next.eps, 2.);
+        assert!(iter.next().is_none());
+    }
+
     #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
     #[test]
     fn fit_predict_dbscan() {