André Anjos
--- a/bob/measure/credible_region.py

+ 7

− 5
+++ b/bob/measure/credible_region.py

+ 7

− 5
 @@ -21,6 +21,8 @@ methods.
   For a disambiguation with `Confidence Interval <confidence-interval_>`_ (the
   frequentist approach), read `Credible Regions or Intervals
   <credible-interval_>`_.
+
+.. include:: ../links.rst
 """

 import numbers
 @@ -183,7 +185,7 @@ def comparef1score(tp1, fp1, tn1, fn1, tp2, fp2, tn2, fn2, lambda_, nbsamples):
    Returns the probability that the F1-score from 1 system is bigger than the F1-score of a second system

    This implementation is based on [GOUTTE-2005]_.
-    
+

    Parameters
    ----------
 @@ -211,7 +213,7 @@ def comparef1score(tp1, fp1, tn1, fn1, tp2, fp2, tn2, fn2, lambda_, nbsamples):

    Returns
    -------
-    
+
    f1_score : float
        A number between 0.0 and 1.0 that describes the probability that the first system is bigger than the second

 @@ -232,7 +234,7 @@ def f1score(tp, fp, tn, fn, lambda_, coverage, nbsample):
    region of the F1 score.

    This implementation is based on [GOUTTE-2005]_.
-    
+

    Parameters
    ----------
 @@ -266,7 +268,7 @@ def f1score(tp, fp, tn, fn, lambda_, coverage, nbsample):

    Returns
    -------
-    
+
    f1_score : (float, float, float, float)
        F1, mean, mode and credible intervals (95% CI). See `F1-score
        <https://en.wikipedia.org/wiki/F1_score>`_.  It corresponds
 @@ -291,7 +293,7 @@ def f1score(tp, fp, tn, fn, lambda_, coverage, nbsample):
    upper = sortedScores[upperIndex - 1]
    return numpy.mean(F1scores), scipy.stats.mode(F1scores)[0][0], lower, upper

-    
+
 def measures(tp, fp, tn, fn, lambda_, coverage):
    """Calculates mean and mode from true/false positive and negative counts
    with credible regions