From e17407720ab426e2a1ca081280e9130aa7a95616 Mon Sep 17 00:00:00 2001
From: MartinuzziFrancesco <martinuzzi.francesco@gmail.com>
Date: Fri, 10 Jan 2025 20:44:19 +0100
Subject: [PATCH] docstring fixes

---
 docs/src/api/training.md       |  4 ++++
 src/train/linear_regression.jl | 43 +---------------------------------
 2 files changed, 5 insertions(+), 42 deletions(-)

diff --git a/docs/src/api/training.md b/docs/src/api/training.md
index f0106121..160b7420 100644
--- a/docs/src/api/training.md
+++ b/docs/src/api/training.md
@@ -2,6 +2,10 @@
 
 ## Linear Models
 
+```@docs
+    StandardRidge
+```
+
 ## Gaussian Regression
 
 Currently, v0.10, is unavailable.
diff --git a/src/train/linear_regression.jl b/src/train/linear_regression.jl
index a6b0e014..c66ff104 100644
--- a/src/train/linear_regression.jl
+++ b/src/train/linear_regression.jl
@@ -15,46 +15,6 @@ The equations for ridge regression are as follows:
    there's usually no need to tweak this
  - `reg`: regularization coefficient. Default is set to 0.0 (linear regression).
 
-# Examples
-```jldoctest
-julia> ridge_reg = StandardRidge()
-StandardRidge(0.0)
-
-julia> ol = train(ridge_reg, rand(Float32, 10, 10), rand(Float32, 10, 10))
-OutputLayer successfully trained with output size: 10
-
-julia> ol.output_matrix #visualize output matrix
-10×10 Matrix{Float32}:
-  0.456574   -0.0407612   0.121963   …   0.859327   -0.127494    0.0572494
-  0.133216   -0.0337922   0.0185378      0.24077     0.0297829   0.31512
-  0.379672   -1.24541    -0.444314       1.02269    -0.0446086   0.482282
-  1.18455    -0.517971   -0.133498       0.84473     0.31575     0.205857
- -0.119345    0.563294    0.747992       0.0102919   1.509      -0.328005
- -0.0716812   0.0976365   0.628654   …  -0.516041    2.4309     -0.113402
-  0.0153872  -0.52334     0.0526867      0.729326    2.98958     1.32703
-  0.154027    0.6013      1.05548       -0.0840203   0.991182   -0.328555
-  1.11007    -0.0371736  -0.0529418      0.186796   -1.21815     0.204838
-  0.282996   -0.263799    0.132079       0.875417    0.497951    0.273423
-
-julia> ridge_reg = StandardRidge(0.001) #passing a value 
-StandardRidge(0.001)
-
-julia> ol = train(ridge_reg, rand(Float16, 10, 10), rand(Float16, 10, 10))
-OutputLayer successfully trained with output size: 10
-
-julia> ol.output_matrix
-10×10 Matrix{Float16}:
- -1.251     3.074   -1.566     -0.10297  …   0.3823   1.341    -1.77    -0.445
-  0.11017  -2.027    0.8975     0.872       -0.643    0.02615   1.083    0.615
-  0.2634    3.514   -1.168     -1.532        1.486    0.1255   -1.795   -0.06555
-  0.964     0.9463  -0.006855  -0.519        0.0743  -0.181    -0.433    0.06793
- -0.389     1.887   -0.702     -0.8906       0.221    1.303    -1.318    0.2634
- -0.1337   -0.4453  -0.06866    0.557    …  -0.322    0.247     0.2554   0.5933
- -0.6724    0.906   -0.547      0.697       -0.2664   0.809    -0.6836   0.2358
-  0.8843   -3.664    1.615      1.417       -0.6094  -0.59      1.975    0.4785
-  1.266    -0.933    0.0664    -0.4497      -0.0759  -0.03897   1.117    0.3152
-  0.6353    1.327   -0.6978    -1.053        0.8037   0.6577   -0.7246   0.07336
-
 ```
 """
 struct StandardRidge
@@ -84,8 +44,7 @@ function train(sr::StandardRidge, states::AbstractArray, target_data::AbstractAr
         ))
     end
 
-    T = eltype(states)
-    output_layer = Matrix(((states * states' + T(sr.reg) * I) \
+    output_layer = Matrix(((states * states' + sr.reg * I) \
                            (states * target_data'))')
     return OutputLayer(sr, output_layer, size(target_data, 1), target_data[:, end])
 end