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.gitattributes

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 *tfevents* filter=lfs diff=lfs merge=lfs -text
 rl_7agents_apprentissage.png filter=lfs diff=lfs merge=lfs -text
 tableau_de_bord_complet.png filter=lfs diff=lfs merge=lfs -text
+data_cleaning_avant_apres.png filter=lfs diff=lfs merge=lfs -text
+dc1_synthese_anomalies.png filter=lfs diff=lfs merge=lfs -text
+eda_correlations.png filter=lfs diff=lfs merge=lfs -text
+precision_recall_modeles.png filter=lfs diff=lfs merge=lfs -text
+shap_beeswarm.png filter=lfs diff=lfs merge=lfs -text
+tsne_anomalies.png filter=lfs diff=lfs merge=lfs -text

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kpi_reseau.json

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performance_qualitative_modeles.csv

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-GMM,4.45,27.4,0.53,0.0,94.7,5.8,t4_%,52.98
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-LOF,3.99,9.2,0.12,0.0011,98.8,2.9,t4_%,47.62
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+GMM,6.23,24.0,0.01,0.8908,100.0,50.9,t4_%,64.52
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rapport_optimisation.json

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@@ -131,8 +134,8 @@
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       "reference": "Auer et al. (2002)",
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@@ -167,8 +170,8 @@
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@@ -176,8 +179,8 @@
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-  "top_eco_agent": "Q-Learning",
-  "n_episodes": 100,
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+  "meilleur_agent": "Q-Learning UCB",
+  "top_eco_agent": "Q-Learning UCB",
+  "n_episodes": 1000,
+  "generated_at": "2026-05-21T13:56:19.813317"
 }

resultats_anomalie.json

@@ -1,58 +1,58 @@
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