add n_bins as argument to functions

src/utils.jl

@@ -51,13 +51,14 @@ cumulative distribution function of the predicted distribution targeting y_t.
 Source: https://arxiv.org/abs/1807.00263
 
 Inputs:
-    - Y_cal: a vector of values y_t
-    - sampled_distributions: an array of sampled distributions F(x_t) stacked column-wise.
+    - 'Y_cal': a vector of values y_t
+    - 'sampled_distributions': an array of sampled distributions F(x_t) stacked column-wise.
+    - 'n_bins': number of equally spaced bins to use.
 Outputs:
     - counts: an array cointaining the empirical frequencies for each quantile interval.
 """
-function empirical_frequency_regression(Y_cal, sampled_distributions)
-    quantiles = collect(0:0.05:1)
+function empirical_frequency_regression(Y_cal, sampled_distributions, n_bins)
+    quantiles = collect(range(0; stop=1, length=n_bins + 1))
     quantiles_matrix = hcat(
         [quantile(samples, quantiles) for samples in sampled_distributions]...
     )
@@ -101,16 +102,17 @@ Inputs:
     - y_binary: the array of outputs y_t numerically coded: 1 for the target class, 0 for the null class.
     - sampled_distributions: an array of sampled distributions stacked column-wise so that in the first row 
         there is the probability for the target class y_1 and in the second row the probability for the null class y_0.
+    - 'n_bins': number of equally spaced bins to use.
 Outputs:
     - num_p_per_interval: array with the number of probabilities falling within interval
     - emp_avg: array with the observed empirical average per interval
     - bin_centers: array with the centers of the bins
 
 """
-function empirical_frequency_binary_classification(y_binary, sampled_distributions)
+function empirical_frequency_binary_classification(y_binary, sampled_distributions, n_bins)
 
     # Number of bins
-    n_bins = 20
+    n_bins = n_bins
     #intervals boundaries
     int_bds = collect(range(0; stop=1, length=n_bins + 1))
     #bin centers