From 16939e8eaeaf2fde393add1f67620549479fb38a Mon Sep 17 00:00:00 2001
From: Benjamin Paul Jaeger <benjamin.jaeger@cern.ch>
Date: Fri, 6 Nov 2020 15:59:36 -0800
Subject: [PATCH] Update documentation
---
nnfwtbn/model.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/nnfwtbn/model.py b/nnfwtbn/model.py
index 98982a6..5a35d21 100644
--- a/nnfwtbn/model.py
+++ b/nnfwtbn/model.py
@@ -361,7 +361,7 @@ class MixedCV(CrossValidator):
def select_training_slices(self, fold_i, for_predicting = False):
"""
- Returns array of arrays with integers corresponding
+ Returns array with integers corresponding
to the data slices used in training fold_i.
If 'for_predicting' is set to True only one slice
is returned for each fold so that the folds are equally represented
@@ -390,7 +390,7 @@ class MixedCV(CrossValidator):
for sl in slices:
if sl not in exclusive_slices and sl in uniq_el(all_slices_for_folds[i:]):
exclusive_slices.append(sl)
- return exclusive_slices[fold_i]
+ return [exclusive_slices[fold_i]]
def select_training(self, df, fold_i, for_predicting = False):
"""
@@ -399,7 +399,7 @@ class MixedCV(CrossValidator):
"""
selected = np.zeros(len(df), dtype='bool')
slices = self.select_training_slices(fold_i, for_predicting = for_predicting)
- for slice_i in np.array(slices).flatten():
+ for slice_i in slices:
selected = selected | self.select_slice(df, slice_i)
return selected
--
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