make default params available to serde (#167)

* add seed param to search params

* make default params available to serde

* lints

* create defaults for enums

* lint

src/neighbors/knn_classifier.rs

@@ -49,16 +49,21 @@ use crate::neighbors::KNNWeightFunction;
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct KNNClassifierParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// 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: D,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// backend search algorithm. See [`knn search algorithms`](../../algorithm/neighbour/index.html). `CoverTree` is default.
     pub algorithm: KNNAlgorithmName,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// weighting function that is used to calculate estimated class value. Default function is `KNNWeightFunction::Uniform`.
     pub weight: KNNWeightFunction,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// number of training samples to consider when estimating class for new point. Default value is 3.
     pub k: usize,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// this parameter is not used
     t: PhantomData<T>,
 }
@@ -111,8 +116,8 @@ impl<T: RealNumber> Default for KNNClassifierParameters<T, Euclidian> {
     fn default() -> Self {
         KNNClassifierParameters {
             distance: Distances::euclidian(),
-            algorithm: KNNAlgorithmName::CoverTree,
-            weight: KNNWeightFunction::Uniform,
+            algorithm: KNNAlgorithmName::default(),
+            weight: KNNWeightFunction::default(),
             k: 3,
             t: PhantomData,
         }

src/neighbors/knn_regressor.rs

@@ -52,16 +52,21 @@ use crate::neighbors::KNNWeightFunction;
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct KNNRegressorParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// 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.
     distance: D,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// backend search algorithm. See [`knn search algorithms`](../../algorithm/neighbour/index.html). `CoverTree` is default.
     pub algorithm: KNNAlgorithmName,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// weighting function that is used to calculate estimated class value. Default function is `KNNWeightFunction::Uniform`.
     pub weight: KNNWeightFunction,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// number of training samples to consider when estimating class for new point. Default value is 3.
     pub k: usize,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// this parameter is not used
     t: PhantomData<T>,
 }
@@ -113,8 +118,8 @@ impl<T: RealNumber> Default for KNNRegressorParameters<T, Euclidian> {
     fn default() -> Self {
         KNNRegressorParameters {
             distance: Distances::euclidian(),
-            algorithm: KNNAlgorithmName::CoverTree,
-            weight: KNNWeightFunction::Uniform,
+            algorithm: KNNAlgorithmName::default(),
+            weight: KNNWeightFunction::default(),
             k: 3,
             t: PhantomData,
         }

src/neighbors/mod.rs

@@ -58,6 +58,12 @@ pub enum KNNWeightFunction {
     Distance,
 }
 
+impl Default for KNNWeightFunction {
+    fn default() -> Self {
+        KNNWeightFunction::Uniform
+    }
+}
+
 impl KNNWeightFunction {
     fn calc_weights<T: RealNumber>(&self, distances: Vec<T>) -> std::vec::Vec<T> {
         match *self {