feat: + builders for algorithm parameters

src/cluster/dbscan.rs

@@ -53,14 +53,32 @@ pub struct DBSCAN<T: RealNumber, D: Distance<Vec<T>, T>> {
 #[derive(Debug, Clone)]
 /// DBSCAN clustering algorithm parameters
 pub struct DBSCANParameters<T: RealNumber> {
-    /// Maximum number of iterations of the k-means algorithm for a single run.
+    /// The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.
     pub min_samples: usize,
-    /// The number of samples in a neighborhood for a point to be considered as a core point.
+    /// The maximum distance between two samples for one to be considered as in the neighborhood of the other.
     pub eps: T,
     /// KNN algorithm to use.
     pub algorithm: KNNAlgorithmName,
 }
 
+impl<T: RealNumber> DBSCANParameters<T> {
+    /// The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.
+    pub fn with_min_samples(mut self, min_samples: usize) -> Self {
+        self.min_samples = min_samples;
+        self
+    }
+    /// The maximum distance between two samples for one to be considered as in the neighborhood of the other.
+    pub fn with_eps(mut self, eps: T) -> Self {
+        self.eps = eps;
+        self
+    }
+    /// KNN algorithm to use.
+    pub fn with_algorithm(mut self, algorithm: KNNAlgorithmName) -> Self {
+        self.algorithm = algorithm;
+        self
+    }
+}
+
 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()