fix: renames FloatExt to RealNumber

src/neighbors/knn_classifier.rs

@@ -36,7 +36,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::linalg::{row_iter, Matrix};
 use crate::math::distance::Distance;
-use crate::math::num::FloatExt;
+use crate::math::num::RealNumber;
 use crate::neighbors::{KNNAlgorithm, KNNAlgorithmName, KNNWeightFunction};
 
 /// `KNNClassifier` parameters. Use `Default::default()` for default values.
@@ -52,7 +52,7 @@ pub struct KNNClassifierParameters {
 
 /// K Nearest Neighbors Classifier
 #[derive(Serialize, Deserialize, Debug)]
-pub struct KNNClassifier<T: FloatExt, D: Distance<Vec<T>, T>> {
+pub struct KNNClassifier<T: RealNumber, D: Distance<Vec<T>, T>> {
     classes: Vec<T>,
     y: Vec<usize>,
     knn_algorithm: KNNAlgorithm<T, D>,
@@ -70,7 +70,7 @@ impl Default for KNNClassifierParameters {
     }
 }
 
-impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNClassifier<T, D> {
+impl<T: RealNumber, D: Distance<Vec<T>, T>> PartialEq for KNNClassifier<T, D> {
     fn eq(&self, other: &Self) -> bool {
         if self.classes.len() != other.classes.len()
             || self.k != other.k
@@ -93,7 +93,7 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNClassifier<T, D> {
     }
 }
 
-impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
+impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
     /// Fits KNN classifier to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data
     /// * `y` - vector with target values (classes) of length N

src/neighbors/knn_regressor.rs

@@ -38,7 +38,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::linalg::{row_iter, BaseVector, Matrix};
 use crate::math::distance::Distance;
-use crate::math::num::FloatExt;
+use crate::math::num::RealNumber;
 use crate::neighbors::{KNNAlgorithm, KNNAlgorithmName, KNNWeightFunction};
 
 /// `KNNRegressor` parameters. Use `Default::default()` for default values.
@@ -54,7 +54,7 @@ pub struct KNNRegressorParameters {
 
 /// K Nearest Neighbors Regressor
 #[derive(Serialize, Deserialize, Debug)]
-pub struct KNNRegressor<T: FloatExt, D: Distance<Vec<T>, T>> {
+pub struct KNNRegressor<T: RealNumber, D: Distance<Vec<T>, T>> {
     y: Vec<T>,
     knn_algorithm: KNNAlgorithm<T, D>,
     weight: KNNWeightFunction,
@@ -71,7 +71,7 @@ impl Default for KNNRegressorParameters {
     }
 }
 
-impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNRegressor<T, D> {
+impl<T: RealNumber, D: Distance<Vec<T>, T>> PartialEq for KNNRegressor<T, D> {
     fn eq(&self, other: &Self) -> bool {
         if self.k != other.k || self.y.len() != other.y.len() {
             return false;
@@ -86,7 +86,7 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNRegressor<T, D> {
     }
 }
 
-impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNRegressor<T, D> {
+impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNRegressor<T, D> {
     /// Fits KNN regressor to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data
     /// * `y` - vector with real values

src/neighbors/mod.rs

@@ -34,7 +34,7 @@
 use crate::algorithm::neighbour::cover_tree::CoverTree;
 use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
 use crate::math::distance::Distance;
-use crate::math::num::FloatExt;
+use crate::math::num::RealNumber;
 use serde::{Deserialize, Serialize};
 
 /// K Nearest Neighbors Classifier
@@ -62,13 +62,13 @@ pub enum KNNWeightFunction {
 }
 
 #[derive(Serialize, Deserialize, Debug)]
-enum KNNAlgorithm<T: FloatExt, D: Distance<Vec<T>, T>> {
+enum KNNAlgorithm<T: RealNumber, D: Distance<Vec<T>, T>> {
     LinearSearch(LinearKNNSearch<Vec<T>, T, D>),
     CoverTree(CoverTree<Vec<T>, T, D>),
 }
 
 impl KNNWeightFunction {
-    fn calc_weights<T: FloatExt>(&self, distances: Vec<T>) -> std::vec::Vec<T> {
+    fn calc_weights<T: RealNumber>(&self, distances: Vec<T>) -> std::vec::Vec<T> {
         match *self {
             KNNWeightFunction::Distance => {
                 // if there are any points that has zero distance from one or more training points,
@@ -88,7 +88,7 @@ impl KNNWeightFunction {
 }
 
 impl KNNAlgorithmName {
-    fn fit<T: FloatExt, D: Distance<Vec<T>, T>>(
+    fn fit<T: RealNumber, D: Distance<Vec<T>, T>>(
         &self,
         data: Vec<Vec<T>>,
         distance: D,
@@ -102,7 +102,7 @@ impl KNNAlgorithmName {
     }
 }
 
-impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNAlgorithm<T, D> {
+impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNAlgorithm<T, D> {
     fn find(&self, from: &Vec<T>, k: usize) -> Vec<(usize, T)> {
         match *self {
             KNNAlgorithm::LinearSearch(ref linear) => linear.find(from, k),