Merge pull request #612 from onixlas/fix-refs

Fix refs
yzhao062 · Dec 20, 2024 · 75eab5f · 75eab5f
2 parents 0c53f3b + d5caaec
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diff --git a/docs/index.rst b/docs/index.rst
@@ -215,7 +215,7 @@ Proximity-Based      SOD               Subspace Outlier Detection
 Proximity-Based      ROD               Rotation-based Outlier Detection                                                                        2020   :class:`pyod.models.rod.ROD`                         :cite:`a-almardeny2020novel`
 Outlier Ensembles    IForest           Isolation Forest                                                                                        2008   :class:`pyod.models.iforest.IForest`                 :cite:`a-liu2008isolation,a-liu2012isolation`
 Outlier Ensembles    INNE              Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles                                      2018   :class:`pyod.models.inne.INNE`                       :cite:`a-bandaragoda2018isolation`
-Outlier Ensembles    DIF               Deep Isolation Forest for Anomaly Detection                                                             2023   :class:`pyod.models.dif.DIF`                         :cite:`a-Xu2023Deep`
+Outlier Ensembles    DIF               Deep Isolation Forest for Anomaly Detection                                                             2023   :class:`pyod.models.dif.DIF`                         :cite:`a-xu2023dif`
 Outlier Ensembles    FB                Feature Bagging                                                                                         2005   :class:`pyod.models.feature_bagging.FeatureBagging`  :cite:`a-lazarevic2005feature`
 Outlier Ensembles    LSCP              LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                       2019   :class:`pyod.models.lscp.LSCP`                       :cite:`a-zhao2019lscp`
 Outlier Ensembles    XGBOD             Extreme Boosting Based Outlier Detection **(Supervised)**                                               2018   :class:`pyod.models.xgbod.XGBOD`                     :cite:`a-zhao2018xgbod`

diff --git a/docs/zreferences.bib b/docs/zreferences.bib
@@ -517,4 +517,14 @@ @inproceedings{pang2019deep
   booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
   pages={353--362},
   year={2019}
-}
+}
+
+@inproceedings{hashemi2019filter,
+   title={Filtering Approaches for Dealing with Noise in Anomaly Detection},
+   url={http://dx.doi.org/10.1109/CDC40024.2019.9029258},
+   DOI={10.1109/cdc40024.2019.9029258},
+   booktitle={2019 IEEE 58th Conference on Decision and Control (CDC)},
+   publisher={IEEE},
+   author={Hashemi, Navid and German, Eduardo Verdugo and Pena Ramirez, Jonatan and Ruths, Justin},
+   year={2019},
+   month=dec, pages={5356--5361} }
diff --git a/pyod/models/ecod.py b/pyod/models/ecod.py
@@ -55,7 +55,7 @@ class ECOD(BaseDetector):
     Cumulative Distribution Functions (ECOD)
     ECOD is a parameter-free, highly interpretable outlier detection algorithm
     based on empirical CDF functions.
-    See :cite:`Li2021ecod` for details.
+    See :cite:`li2021ecod` for details.
 
     Parameters
     ----------

diff --git a/pyod/models/so_gaal.py b/pyod/models/so_gaal.py
@@ -179,10 +179,11 @@ def fit(self, X, y=None):
                 optimizer_d.step()
 
                 self.train_history['discriminator_loss'].append(d_loss.item())
+
+                trick_labels = torch.ones(batch_size, 1)
 
                 if stop == 0:
                     # Train Generator
-                    trick_labels = torch.ones(batch_size, 1)
                     g_loss = criterion(
                         self.discriminator(self.generator(noise)),
                         trick_labels)
@@ -198,6 +199,7 @@ def fit(self, X, y=None):
                         trick_labels)
                     self.train_history['generator_loss'].append(g_loss.item())
 
+
             if epoch + 1 > self.stop_epochs:
                 stop = 1