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fix: formatting

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This commit is contained in:
Volodymyr Orlov
2020-08-29 20:20:36 -07:00
parent fa0918cee3
commit f7c229f167
2 changed files with 7 additions and 5 deletions
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src/linear/logistic_regression.rs
+3 -1
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@@ -58,7 +58,9 @@ impl<T: RealNumber, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
} }
} }
impl<'a, T: RealNumber, 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 { fn f(&self, w_bias: &M) -> T {
let mut f = T::zero(); let mut f = T::zero();
let (n, _) = self.x.shape(); let (n, _) = self.x.shape();
src/math/distance/mod.rs
+4 -4
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@@ -1,5 +1,5 @@
//! # Collection of Distance Functions //! # Collection of Distance Functions
//! //!
//! Many algorithms in machine learning require a measure of distance between data points. Distance metric (or metric) is a function that defines a distance between a pair of point elements of a set. //! Many algorithms in machine learning require a measure of distance between data points. Distance metric (or metric) is a function that defines a distance between a pair of point elements of a set.
//! Formally, the distance can be any metric measure that is defined as \\( d(x, y) \geq 0\\) and follows three conditions: //! Formally, the distance can be any metric measure that is defined as \\( d(x, y) \geq 0\\) and follows three conditions:
//! 1. \\( d(x, y) = 0 \\) if and only \\( x = y \\), positive definiteness //! 1. \\( d(x, y) = 0 \\) if and only \\( x = y \\), positive definiteness
@@ -7,9 +7,9 @@
//! 1. \\( d(x, y) \leq d(x, z) + d(z, y) \\), subadditivity or triangle inequality //! 1. \\( d(x, y) \leq d(x, z) + d(z, y) \\), subadditivity or triangle inequality
//! //!
//! for all \\(x, y, z \in Z \\) //! for all \\(x, y, z \in Z \\)
//! //!
//! A good distance metric helps to improve the performance of classification, clustering and information retrieval algorithms significantly. //! A good distance metric helps to improve the performance of classification, clustering and information retrieval algorithms significantly.
//! //!
//! <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script> //! <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_CHTML"></script>
/// Euclidean Distance is the straight-line distance between two points in Euclidean spacere that presents the shortest distance between these points. /// Euclidean Distance is the straight-line distance between two points in Euclidean spacere that presents the shortest distance between these points.