fix: renames FloatExt to RealNumber

src/linear/logistic_regression.rs

@@ -4,21 +4,21 @@ use std::marker::PhantomData;
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::Matrix;
-use crate::math::num::FloatExt;
+use crate::math::num::RealNumber;
 use crate::optimization::first_order::lbfgs::LBFGS;
 use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
 use crate::optimization::line_search::Backtracking;
 use crate::optimization::FunctionOrder;
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct LogisticRegression<T: FloatExt, M: Matrix<T>> {
+pub struct LogisticRegression<T: RealNumber, M: Matrix<T>> {
     weights: M,
     classes: Vec<T>,
     num_attributes: usize,
     num_classes: usize,
 }
 
-trait ObjectiveFunction<T: FloatExt, M: Matrix<T>> {
+trait ObjectiveFunction<T: RealNumber, M: Matrix<T>> {
     fn f(&self, w_bias: &M) -> T;
     fn df(&self, g: &mut M, w_bias: &M);
 
@@ -33,13 +33,13 @@ trait ObjectiveFunction<T: FloatExt, M: Matrix<T>> {
     }
 }
 
-struct BinaryObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
+struct BinaryObjectiveFunction<'a, T: RealNumber, M: Matrix<T>> {
     x: &'a M,
     y: Vec<usize>,
     phantom: PhantomData<&'a T>,
 }
 
-impl<T: FloatExt, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
+impl<T: RealNumber, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
     fn eq(&self, other: &Self) -> bool {
         if self.num_classes != other.num_classes
             || self.num_attributes != other.num_attributes
@@ -58,7 +58,7 @@ impl<T: FloatExt, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
     }
 }
 
-impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveFunction<'a, T, M> {
+impl<'a, T: RealNumber, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveFunction<'a, T, M> {
     fn f(&self, w_bias: &M) -> T {
         let mut f = T::zero();
         let (n, _) = self.x.shape();
@@ -88,14 +88,14 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveF
     }
 }
 
-struct MultiClassObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
+struct MultiClassObjectiveFunction<'a, T: RealNumber, M: Matrix<T>> {
     x: &'a M,
     y: Vec<usize>,
     k: usize,
     phantom: PhantomData<&'a T>,
 }
 
-impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M>
+impl<'a, T: RealNumber, M: Matrix<T>> ObjectiveFunction<T, M>
     for MultiClassObjectiveFunction<'a, T, M>
 {
     fn f(&self, w_bias: &M) -> T {
@@ -147,7 +147,7 @@ impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M>
     }
 }
 
-impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
+impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
     pub fn fit(x: &M, y: &M::RowVector) -> LogisticRegression<T, M> {
         let y_m = M::from_row_vector(y.clone());
         let (x_nrows, num_attributes) = x.shape();