From b775d77f24e3ddf20717b706d9fbf1ef5a9c0321 Mon Sep 17 00:00:00 2001
From: Adrienucl <adrien.payen@student.uclouvain.be>
Date: Mon, 29 Apr 2024 23:46:31 +0200
Subject: [PATCH] commit plots and simulation
---
.DS_Store | Bin 6148 -> 6148 bytes
plotting.py | 41 +++++++++++++++
simulate.py | 61 ++++++++++++++++++++++
plot.py => test_files/plot.py | 0
validation.py => test_files/validation.py | 0
5 files changed, 102 insertions(+)
create mode 100644 plotting.py
create mode 100644 simulate.py
rename plot.py => test_files/plot.py (100%)
rename validation.py => test_files/validation.py (100%)
diff --git a/.DS_Store b/.DS_Store
index 49ec909d4a2edc342593b083d5485c3037bef0ed..837b005cf49930c5ead4ac78ca4b5abed2e970f5 100644
GIT binary patch
delta 67
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Rz#_7_jHQuzGdss$egG9f5Pbju
delta 54
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diff --git a/plotting.py b/plotting.py
new file mode 100644
index 0000000..5ce6476
--- /dev/null
+++ b/plotting.py
@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from simulate import Simulate as sim
+from tmc import TransitionMatrixCalculator as tmc
+from markovDecision import MarkovDecisionSolver as mD
+
+def get_results(layouts, circle, n_iterations=100):
+ results_markov = []
+ results_safe = []
+ results_normal = []
+ results_risky = []
+ results_random = []
+
+ for layout in layouts:
+ # Compute optimal policy
+ expec, policy = mD(layout, circle).solve()
+
+ # Simulate game using Simulate class
+ sim_instance = sim(layout, circle)
+ result_markov = sim_instance.simulate_game(policy, n_iterations)
+ results_markov.append(result_markov)
+
+ # Simulate with fixed strategies using Simulate class
+ results_safe.append(sim_instance.simulate_game([1]*15, n_iterations))
+ results_normal.append(sim_instance.simulate_game([2]*15, n_iterations))
+ results_risky.append(sim_instance.simulate_game([3]*15, n_iterations))
+ results_random.append(sim_instance.simulate_game(np.random.randint(1, 4, size=15), n_iterations))
+
+ return results_markov, results_safe, results_normal, results_risky, results_random
+
+# Utilisation de la fonction get_results pour obtenir les résultats
+layouts = [[0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 1, 0]] # Exemple de layouts à utiliser
+circle = True # Exemple de valeur pour circle
+results_markov, results_safe, results_normal, results_risky, results_random = get_results(layouts, circle, n_iterations=100)
+
+# Imprimer les résultats (vous pouvez les enregistrer dans un fichier si nécessaire)
+print("Results Markov:", results_markov)
+print("Results Safe:", results_safe)
+print("Results Normal:", results_normal)
+print("Results Risky:", results_risky)
+print("Results Random:", results_random)
diff --git a/simulate.py b/simulate.py
new file mode 100644
index 0000000..bbaa211
--- /dev/null
+++ b/simulate.py
@@ -0,0 +1,61 @@
+import random as rd
+import numpy as np
+from tmc import TransitionMatrixCalculator as tmc
+from markovDecision import MarkovDecisionSolver as mD
+
+
+class Simulate:
+ def __init__(self, layout, circle):
+ self.layout = layout
+ self.circle = circle
+ self.tmc_instance = tmc()
+ self.safe_dice, self.normal_dice, self.risky_dice = self.tmc_instance.compute_transition_matrix(layout, circle)
+ self.transition_matrices = [self.safe_dice, self.normal_dice, self.risky_dice]
+
+ def simulate_game(self, strategy, n_iterations=10000):
+ number_turns = []
+ for _ in range(n_iterations):
+ total_turns = 0
+ state = 0 # initial state
+
+ while state < len(self.layout) - 1: # until goal state is reached
+ action = strategy[state] # get action according to strategy
+ transition_matrix = self.transition_matrices[int(action) - 1]
+ state = np.random.choice(len(self.layout), p=transition_matrix[state])
+
+ if self.layout[state] == 3 and action == 2:
+ total_turns += rd.choice([1, 2], p=[0.5, 0.5])
+ elif self.layout[state] == 3 and action == 3:
+ total_turns += 2
+ else:
+ total_turns += 1
+
+ number_turns.append(total_turns)
+
+ return np.mean(number_turns)
+
+ def simulate_state(self, strategy, n_iterations=10000):
+ number_mean = []
+ for _ in range(n_iterations):
+ number_turns = []
+
+ for state in range(len(self.layout) - 1):
+ total_turns = 0
+
+ while state < len(self.layout) - 1:
+ print("Current state:", state)
+ print("Transition matrix:", transition_matrix[state])
+ state = np.random.choice(len(self.layout), p=transition_matrix[state])
+
+ if self.layout[state] == 3 and action == 2:
+ total_turns += rd.choice([1, 2], p=[0.5, 0.5])
+ elif self.layout[state] == 3 and action == 3:
+ total_turns += 2
+ else:
+ total_turns += 1
+
+ number_turns.append(total_turns)
+
+ number_mean.append(number_turns)
+
+ return np.mean(number_mean, axis=0)
diff --git a/plot.py b/test_files/plot.py
similarity index 100%
rename from plot.py
rename to test_files/plot.py
diff --git a/validation.py b/test_files/validation.py
similarity index 100%
rename from validation.py
rename to test_files/validation.py
--
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