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/naive_bayes/bernoulli.rs

@@ -114,10 +114,13 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for BernoulliNBDistributi
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct BernoulliNBParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Option<Vec<T>>,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
     pub binarize: Option<T>,
 }
@@ -154,10 +157,13 @@ impl<T: RealNumber> Default for BernoulliNBParameters<T> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct BernoulliNBSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: Vec<T>,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Vec<Option<Vec<T>>>,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
     pub binarize: Vec<Option<T>>,
 }

src/naive_bayes/categorical.rs

@@ -243,6 +243,7 @@ impl<T: RealNumber> CategoricalNBDistribution<T> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct CategoricalNBParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,
 }
@@ -265,6 +266,7 @@ impl<T: RealNumber> Default for CategoricalNBParameters<T> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct CategoricalNBSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: Vec<T>,
 }

src/naive_bayes/gaussian.rs

@@ -78,6 +78,7 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for GaussianNBDistributio
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct GaussianNBParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Option<Vec<T>>,
 }
@@ -100,6 +101,7 @@ impl<T: RealNumber> Default for GaussianNBParameters<T> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct GaussianNBSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Vec<Option<Vec<T>>>,
 }

src/naive_bayes/multinomial.rs

@@ -86,8 +86,10 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for MultinomialNBDistribu
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct MultinomialNBParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Option<Vec<T>>,
 }
@@ -118,8 +120,10 @@ impl<T: RealNumber> Default for MultinomialNBParameters<T> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct MultinomialNBSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: Vec<T>,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Vec<Option<Vec<T>>>,
 }