From d188daf2ec4f5f7047db59a10f760dddd883816d Mon Sep 17 00:00:00 2001
From: "pasquale c." <343guiltyspark@outlook.it>
Date: Tue, 2 Jul 2024 14:37:55 +0200
Subject: [PATCH] fixed typos

---
 src/utils.jl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/utils.jl b/src/utils.jl
index f4387bd..5c94b3a 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -42,7 +42,7 @@ function outdim(model::Chain)::Number
 end
 
 @doc raw""" 
-    empirical_frequency(Y_cal, sampled_distributions)
+    empirical_frequency_regression(Y_cal, sampled_distributions, n_bins=20)
 
 FOR REGRESSION MODELS.  \
 Given a calibration dataset ``(x_t, y_t)`` for ``i ∈ {1,...,T}`` and an array of predicted distributions, the function calculates the empirical frequency
@@ -75,7 +75,7 @@ function empirical_frequency_regression(Y_cal, sampled_distributions, n_bins=20)
 end
 
 @doc raw""" 
-    sharpness(sampled_distributions)
+    sharpness_regression(sampled_distributions)
 
 FOR REGRESSION MODELS.  \
 Given a calibration dataset ``(x_t, y_t)`` for ``i ∈ {1,...,T}`` and an array of predicted distributions, the function calculates the 
@@ -93,7 +93,7 @@ function sharpness_regression(sampled_distributions)
 end
 
 @doc raw""" 
-    empirical_frequency-classification(y_binary, sampled_distributions)
+    empirical_frequency_classification(y_binary, sampled_distributions)
 
 FOR BINARY CLASSIFICATION MODELS.\
 Given a calibration dataset ``(x_t, y_t)`` for ``i ∈ {1,...,T}`` let ``p_t= H(x_t)∈[0,1]`` be the forecasted probability. \