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make default params available to serde (#167)

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* add seed param to search params

* make default params available to serde

* lints

* create defaults for enums

* lint
This commit is contained in:
Montana Low
2022-09-21 19:48:31 -07:00
committed by GitHub
parent 403d3f2348
commit 764309e313
22 changed files with 175 additions and 18 deletions
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src/cluster/dbscan.rs
+10 -1
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@@ -65,17 +65,22 @@ pub struct DBSCAN<T: RealNumber, D: Distance<Vec<T>, T>> {
eps: T,
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
/// DBSCAN clustering algorithm parameters
pub struct DBSCANParameters<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))]
/// The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.
pub min_samples: usize,
#[cfg_attr(feature = "serde", serde(default))]
/// The maximum distance between two samples for one to be considered as in the neighborhood of the other.
pub eps: T,
#[cfg_attr(feature = "serde", serde(default))]
/// KNN algorithm to use.
pub algorithm: KNNAlgorithmName,
}
@@ -113,14 +118,18 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> DBSCANParameters<T, D> {
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct DBSCANSearchParameters<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: Vec<D>,
#[cfg_attr(feature = "serde", serde(default))]
/// 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>,
#[cfg_attr(feature = "serde", serde(default))]
/// The maximum distance between two samples for one to be considered as in the neighborhood of the other.
pub eps: Vec<T>,
#[cfg_attr(feature = "serde", serde(default))]
/// KNN algorithm to use.
pub algorithm: Vec<KNNAlgorithmName>,
}
@@ -221,7 +230,7 @@ impl<T: RealNumber> Default for DBSCANParameters<T, Euclidian> {
distance: Distances::euclidian(),
min_samples: 5,
eps: T::half(),
algorithm: KNNAlgorithmName::CoverTree,
algorithm: KNNAlgorithmName::default(),
}
}
}