From b65c84629d9be8e4cfed76e9c277777219cdb166 Mon Sep 17 00:00:00 2001
From: Adrien <adrien.payen@student.uclouvain.be>
Date: Sun, 19 May 2024 20:29:19 +0200
Subject: [PATCH] update files
---
.gitignore | 3 ++-
plot.py | 6 +++---
validation.py | 4 ++--
3 files changed, 7 insertions(+), 6 deletions(-)
diff --git a/.gitignore b/.gitignore
index 1cdbf95..20b5e10 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
*.pyc
*.pyd
*.pyo
-__pycache__
\ No newline at end of file
+__pycache__
+*.DS_Store
\ No newline at end of file
diff --git a/plot.py b/plot.py
index 2dce8ea..e0222e9 100644
--- a/plot.py
+++ b/plot.py
@@ -90,7 +90,7 @@ def plot_state_based_comparison_once(num_games : int):
if __name__ == '__main__':
- ##### Paramètres #####
+ ##### Parametres #####
# Define the layout of the game board
layout = [0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 1, 0]
@@ -98,8 +98,8 @@ if __name__ == '__main__':
# All the layout for the comparison
#classiclayout = [0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 1, 0]
#layout = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 1, 0]
- #layouttpslowlane = [0, 0, 3, 0, 2, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0] # layout with a trapped slowlane
- #layouttpfastlane = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 1, 0] # layout with a trapped fastlane
+ #layoutslowlanetp = [0, 0, 3, 0, 2, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0] # layout with a trapped slowlane
+ #layoutfastlanetp = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 1, 0] # layout with a trapped fastlane
#zerolayout = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# Indicates whether the board is circular or linear
diff --git a/validation.py b/validation.py
index b76988c..e55680c 100644
--- a/validation.py
+++ b/validation.py
@@ -85,7 +85,7 @@ class Validation:
total_turns = []
for _ in range(n_iterations):
- state_turns = np.zeros(len(layout) - 1) # Utiliser un tableau numpy pour stocker les tours par état
+ state_turns = np.zeros(len(layout) - 1) # Numpy to store the turns by state
for state in range(len(layout) - 1):
k = state
@@ -103,7 +103,7 @@ class Validation:
if layout[k] == 3:
if action == 2:
- turns += np.random.choice([1, 2], p=[0.5, 0.5]) # Utiliser numpy pour la randomisation
+ turns += np.random.choice([1, 2], p=[0.5, 0.5]) # Numpy for randomisation
elif action == 3:
turns += 2
else:
--
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