Téléverser les fichiers vers "/"
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@@ -20,7 +20,7 @@
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"id": "ea73e020",
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"metadata": {},
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"source": [
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"## 1. Imports and plot function"
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"## 1. Imports"
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]
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},
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{
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@@ -39,45 +39,6 @@
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "54c5e14f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits.mplot3d import Axes3D # Nécessaire pour la 3D\n",
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"\n",
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"def plot_pareto_3d(archive, model_type:str):\n",
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" fig = plt.figure(figsize=(12, 8))\n",
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" ax = fig.add_subplot(111, projection='3d')\n",
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"\n",
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" # Extraction des scores depuis l'archive\n",
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" # f_best[0] = Coût, f_best[1] = Insatisfaction, f_best[2] = Stress Réseau\n",
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" f1 = [p.f_best[0] for p in archive] \n",
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" f2 = [p.f_best[1] for p in archive] \n",
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" f3 = [p.f_best[2] for p in archive] \n",
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"\n",
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" # Création du nuage de points\n",
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" img = ax.scatter(f1, f2, f3, c=f3, cmap='viridis', s=60, edgecolors='black')\n",
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" \n",
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" ax.set_xlabel('Coût (€)')\n",
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" ax.set_ylabel('Insatisfaction (SoC manquant)')\n",
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" ax.set_zlabel('Pic Réseau (kW)')\n",
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" ax.set_title(f'Front de Pareto des Solutions Non-Dominées ({model_type})')\n",
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" \n",
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" # Barre de couleur\n",
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" cbar = fig.colorbar(img, ax=ax, pad=0.1)\n",
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" cbar.set_label('Intensité du Pic Réseau (kW)')\n",
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" \n",
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" # Sauvegarde et affichage\n",
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" filename = f\"{model_type}_pareto_3d.png\"\n",
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" plt.savefig(filename)\n",
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" print(f\"Graphique sauvegardé sous : {filename}\")\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee8b04e4",
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@@ -91,7 +52,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "f783ba32",
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"metadata": {},
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"outputs": [],
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@@ -109,18 +70,7 @@
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" for p in self.particles:\n",
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" self.surrogate_handler.add_data(p.x, p.f_current[1])\n",
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"\n",
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" def iterate(self):\n",
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" train_freq = 10 # Retrain every 10 iterations\n",
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" \n",
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" # Check if retraining is needed\n",
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" if self.use_surrogate and (self.t % train_freq == 0):\n",
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" self.surrogate_handler.train()\n",
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"\n",
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" # Determine if AI prediction should be used\n",
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" use_ai = (self.use_surrogate and \n",
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" self.surrogate_handler.is_trained and \n",
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" self.t % train_freq != 0)\n",
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"\n",
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" def iterate(self, prediction_freq:int=10):\n",
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" # Main loop (overriding original logic to manage control flow)\n",
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" for t in range(self.t): \n",
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" self.select_leader()\n",
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@@ -131,7 +81,7 @@
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" self.particles[i].update_position()\n",
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" self.particles[i].keep_boudaries(self.A_max)\n",
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"\n",
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" if use_ai:\n",
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" if (t % (prediction_freq) != 0) and self.use_surrogate:\n",
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" # Fast exact calculation (f1, f3)\n",
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" f1 = self.particles[i].f1(self.prices)\n",
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" f3 = self.particles[i].f3()\n",
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@@ -147,13 +97,31 @@
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" self.particles[i].updating_socs(self.socs, self.capacities)\n",
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" self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)\n",
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"\n",
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" # Capture data for AI training\n",
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" if self.use_surrogate:\n",
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" self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])\n",
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"\n",
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" self.particles[i].update_best()\n",
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" \n",
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" self.update_archive()"
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" self.update_archive()\n",
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"\n",
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"\n",
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" # Run Classic MOPSO, collect data and run training for the model\n",
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" def train_surrogate_model(self):\n",
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" # Generation of data\n",
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" for t in range(self.t): \n",
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" self.select_leader()\n",
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" \n",
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" for i in range(self.n):\n",
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" # Movement \n",
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" self.particles[i].update_velocity(self.leader.x, self.c1, self.c2, self.w)\n",
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" self.particles[i].update_position()\n",
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" self.particles[i].keep_boudaries(self.A_max)\n",
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" # Standard Calculation (Slow and Exact)\n",
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" self.particles[i].updating_socs(self.socs, self.capacities)\n",
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" self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)\n",
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" \n",
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" # Capture data for AI training\n",
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" self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])\n",
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" \n",
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" # End of dataset generation (based on classic MOPSO)\n",
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" self.surrogate_handler.train()"
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]
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},
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{
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@@ -167,7 +135,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"id": "a7789c8c",
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"metadata": {},
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"outputs": [],
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@@ -221,7 +189,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"id": "a8797755",
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"metadata": {},
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"outputs": [],
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@@ -243,6 +211,9 @@
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"# Instantiate extended class\n",
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" optimizer = SmartMOPSO(model_type=model_type, **params)\n",
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"\n",
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" if(model_type is not None):\n",
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" optimizer.train_surrogate_model()\n",
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" \n",
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" start_time = time.time()\n",
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" \n",
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" # Run simulation\n",
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@@ -277,7 +248,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"id": "6cdd4953",
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"metadata": {},
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"outputs": [
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@@ -311,64 +282,52 @@
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"dtype: float64\n",
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"\n",
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"--- Launching Scenario: Only MOPSO ---\n",
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"Finished in 0.40 seconds.\n",
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"Best f2 found: 5.3493\n",
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"Finished in 0.39 seconds.\n",
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"Best f2 found: 5.6198\n",
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"\n",
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"--- Launching Scenario: With MLP ---\n",
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"Finished in 0.68 seconds.\n",
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"Best f2 found: 6.3994\n",
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"Finished in 0.32 seconds.\n",
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"Best f2 found: 6.7202\n",
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"\n",
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"--- Launching Scenario: With Random Forest ---\n",
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"Finished in 0.52 seconds.\n",
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"Best f2 found: 5.2397\n",
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"Finished in 2.77 seconds.\n",
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"Best f2 found: 5.7399\n",
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"\n",
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"--- Launching Scenario: Only MOPSO ---\n",
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"Finished in 0.96 seconds.\n",
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"Best f2 found: 4.5495\n",
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"Finished in 0.92 seconds.\n",
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"Best f2 found: 4.0497\n",
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"\n",
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"--- Launching Scenario: With MLP ---\n",
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"Finished in 1.30 seconds.\n",
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"Best f2 found: 5.3998\n",
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"Finished in 0.78 seconds.\n",
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"Best f2 found: 4.2297\n",
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"\n",
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"--- Launching Scenario: With Random Forest ---\n",
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"Finished in 1.12 seconds.\n",
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"Best f2 found: 4.4896\n",
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"Finished in 6.91 seconds.\n",
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"Best f2 found: 5.2098\n",
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"\n",
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"--- Launching Scenario: Only MOPSO ---\n",
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"Finished in 9.63 seconds.\n",
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"Best f2 found: 5.4597\n",
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"Finished in 1.90 seconds.\n",
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"Best f2 found: 6.0193\n",
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"\n",
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"--- Launching Scenario: With MLP ---\n",
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"Finished in 10.31 seconds.\n",
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"Best f2 found: 6.9999\n",
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"Finished in 1.80 seconds.\n",
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"Best f2 found: 3.2313\n",
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"\n",
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"--- Launching Scenario: With Random Forest ---\n",
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"Finished in 9.77 seconds.\n",
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"Best f2 found: 5.7496\n",
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"Finished in 13.86 seconds.\n",
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"Best f2 found: 5.1697\n",
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"\n",
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"--- Launching Scenario: Only MOPSO ---\n",
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"Finished in 18.79 seconds.\n",
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"Best f2 found: 6.6090\n",
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"Finished in 9.62 seconds.\n",
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"Best f2 found: 5.3396\n",
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"\n",
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"--- Launching Scenario: With MLP ---\n",
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"Finished in 20.60 seconds.\n",
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"Best f2 found: 5.8495\n",
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"Finished in 8.16 seconds.\n",
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"Best f2 found: 6.0898\n",
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"\n",
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"--- Launching Scenario: With Random Forest ---\n",
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"Finished in 22.21 seconds.\n",
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"Best f2 found: 3.4493\n",
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"\n",
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"--- Launching Scenario: Only MOPSO ---\n",
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"Finished in 196.01 seconds.\n",
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"Best f2 found: 5.0795\n",
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"\n",
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"--- Launching Scenario: With MLP ---\n",
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"Finished in 206.84 seconds.\n",
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"Best f2 found: 5.2493\n",
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"\n",
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"--- Launching Scenario: With Random Forest ---\n",
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"Finished in 202.25 seconds.\n",
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"Best f2 found: 5.4393\n"
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"Finished in 69.95 seconds.\n",
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"Best f2 found: 7.1100\n"
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]
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}
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],
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@@ -397,7 +356,7 @@
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" 'MLP': [],\n",
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" 'RF': []\n",
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"}\n",
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"nb_particles = [20,50,500,1000,10000]\n",
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"nb_particles = [20,50,100,500]\n",
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"\n",
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"for k in range(len(nb_particles)):\n",
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" # 1. Without Surrogate (Baseline)\n",
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@@ -439,7 +398,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 11,
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"id": "165b48cd",
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"metadata": {},
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"outputs": [
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@@ -451,21 +410,18 @@
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"=== SUMMARY ===\n",
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"Mode | Time (s) | Best f2 \n",
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"---------------------------------------------\n",
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"MOPSO _20 | 0.40 | 5.3493 \n",
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"MOPSO _50 | 0.96 | 4.5495 \n",
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"MOPSO _500 | 9.63 | 5.4597 \n",
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"MOPSO _1000 | 18.79 | 6.6090 \n",
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"MOPSO _10000 | 196.01 | 5.0795 \n",
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"MLP _20 | 0.68 | 6.3994 \n",
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"MLP _50 | 1.30 | 5.3998 \n",
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"MLP _500 | 10.31 | 6.9999 \n",
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"MLP _1000 | 20.60 | 5.8495 \n",
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"MLP _10000 | 206.84 | 5.2493 \n",
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"RF _20 | 0.52 | 5.2397 \n",
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"RF _50 | 1.12 | 4.4896 \n",
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"RF _500 | 9.77 | 5.7496 \n",
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"RF _1000 | 22.21 | 3.4493 \n",
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"RF _10000 | 202.25 | 5.4393 \n"
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"MOPSO _20 | 0.39 | 5.6198 \n",
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"MOPSO _50 | 0.92 | 4.0497 \n",
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"MOPSO _100 | 1.90 | 6.0193 \n",
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"MOPSO _500 | 9.62 | 5.3396 \n",
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"MLP _20 | 0.32 | 6.7202 \n",
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"MLP _50 | 0.78 | 4.2297 \n",
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"MLP _100 | 1.80 | 3.2313 \n",
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"MLP _500 | 8.16 | 6.0898 \n",
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"RF _20 | 2.77 | 5.7399 \n",
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"RF _50 | 6.91 | 5.2098 \n",
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"RF _100 | 13.86 | 5.1697 \n",
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"RF _500 | 69.95 | 7.1100 \n"
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]
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}
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],
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@@ -481,13 +437,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 12,
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"id": "314f8d5a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd0FNXjNvBndtNJbxAghN4JXaqA9I70IlUUpVeRnw17wa8NUERRKUpXqoCAAiI1gYAkQGihhgAJKaRnd+77B2/G3TQ2IWF3Js/nnBxmZ2Zn7t15MuHu3LkjCSEEiIiIiIiIiKjY6axdACIiIiIiIiKtYqObiIiIiIiIqISw0U1ERERERERUQtjoJiIiIiIiIiohbHQTERERERERlRA2uomIiIiIiIhKCBvdRERERERERCWEjW4iIiIiIiKiEsJGNxEREREREVEJYaObiKiUkGUZXbp0gSRJaNasGeLj461dJCIiIiLNY6ObiKiIxo4dC0mSIEkSOnToYO3iPNL8+fOxd+9eNG/eHHv37oWXl5e1i5SvDh06KJ/t2LFjrV0cq7h69aryGUiShP3791u7SLksX77crIyUP9PPafny5dYuTpGpIZfWZu3fi/3795vt/+rVq0+8DERkjo1uolKicuXKZn+ELfnhf6aKLrvR+Pbbb1u7KACAnTt34oMPPkDLli2xZ88eeHp6Wq0sbFATkVpZu0FNROpkZ+0CEBFRybt06RLeffddTJs2De7u7tYuziNNnDgRvXv3BgDUr1/fyqUhIrJc8+bN8emnn1q7GERkQ9joJiolXn/9dSQmJiqv4+Pj8eGHHyqvu3Tpgq5du5q9p1q1ak+sfFSypk6dau0iFMrQoUOtXQQim5eUlKSKL9G0oDCfdb169VCvXr0SLhERqQm7lxOVEi+++CLmzJmj/Lz44otmy1u3bm22fM6cOahQoQJWrVqFrl27wt/fHw4ODvDz80OvXr2wY8eOXPvIeR9ZZGQk5s+fj6CgILi4uOCpp57Crl27AAD37t3D+PHj4efnB2dnZ7Rt2xYHDx7Mtc2c90Hu2LEDbdu2haurK7y8vDBo0CBcunQp1/sOHjyI/v37o0KFCnBwcICrqysqV66MHj164O233zb7AuJR/v77b3To0AFlypSBt7c3Bg8ejMuXL1v8/pzu3LmD1157DY0aNYKbmxucnJxQvXp1TJ48GdevXzdb99tvv1Xqb29vj5MnTyrLLl26hDJlyijL33///QL34+joiKpVq2LChAm4ePFinmUTQmDjxo3o27cvKlSoAEdHR3h7e6Nx48aYNWsWMjMzATz6vs68upC//fbbkCQJBw4cUNZbsWJFnvcePqoL+oULFzBx4kTUqlULLi4ucHFxQc2aNfHSSy/h/PnzudbPef/97du3MWHCBAQEBMDR0RF16tTB999/n+dnkp8zZ85g5MiRqFy5MhwdHeHs7IxKlSqhY8eO+L//+z/cunUr13u2bduGfv36ISAgAA4ODvDy8kLHjh3xyy+/QAhRqP0DwK+//opevXqhXLlyyvZat26Nzz77DKmpqbnWz/n7tGfPHjzzzDNwdXWFm5sbevTogYiIiEKXIz8ZGRlYvHgx2rVrB29vbzg4OCAgIACDBw/GkSNHCrWtxzmGaWlp+OKLL9CmTRt4eXnBwcEBZcuWRc+ePbF+/fpc65fEuSynvXv3ol27dgWey/L6Pfvhhx/QpEkTODs7o127dmbrF2e+UlNTMW/ePAQGBsLJyQn16tXD119/bdF2iqscObtyp6enY/78+ahWrZpyPnv33XeV81K2qKgozJgxA08//TQCAwNRpkwZODo6okKFCujTpw+2bdv2yH2lpqbi9ddfR9WqVWFvb4/Ro0dDkiSMGzfO7H2m78m+nehRXdANBgN+/PFHdO3aFWXLllX+trZs2RLvvPOOst6j7s02vW2sMLcyybJcqL/tALB161Z0794dZcuWhb29Pdzd3VGtWjU8++yz+OijjyDLssX7JyqVBBGVSlFRUQKA8jN//nyz5ampqaJz585m6+T8mTVrltl79u3bZ7a8adOmud6j0+nE2rVrRZUqVXItc3R0FGfPnjXbpunyZ555Js9y+Pj4iMjISOU9e/fuFXq9vsCynzt3zqLPadu2bcLOzi7X+729vUWrVq2U1+3btzd7X/v27fP8XA8fPix8fX3zLZeHh4f4+++/zd7Tr18/ZXmDBg1ERkaGMBqNok2bNsr8du3aCaPRaPF+XFxcxLZt28z2k5aWJnr16lXg5xYfHy+EyJ2fffv25Vl/AGLMmDFCCCHmz59f4LYBiKioqHzfn239+vXCyckp3204OjqKNWvWmL1nzJgxyvKqVauKgICAPN/7ww8/FByI/y8iIkK4uLgUWJedO3cq6xuNRjFq1KgC1x88eLAwGAzKewr6jA0GgxgyZEiB26tTp46Ijo42K7fp8jZt2ghJkvL8fbp7965Fn8NPP/1k9l5Td+/eFY0aNcq3fDqdTnz55ZcW7UeIoh/D27dvi3r16hX4WQ0cOFBkZWUp7ynpc1mPHj3y/exNz2U5M/D000+bvW7YsKEQomj5KkhmZmaufWX/5DxHmOayuMuRM18dO3bMc5t9+/YVsiwr79u2bVuBZQAg3nnnnQL3lbP+pufh/H6yz/cF/V7ExcWJ5s2b57sNDw8PZd2cOcw+P2YLCgrKte9Hva8of9tz1ievn7S0NIuOKVFpxe7lRJSnmTNnYu/evQAABwcHDBs2DDVq1MCZM2ewYcMGCCHw+eefo2nTphgxYkSe2zhx4gSGDh2KqlWrYvHixXjw4AFkWcawYcMAAKNGjYKvry8WLVoEg8GAjIwMfPXVV/j222/z3N6+ffvQtGlT9OzZE+Hh4di0aRMAIC4uDi+//DL++usvAMB3330Ho9EIAKhduzYGDx4MOzs7XL9+HadOnTK7WlyQ1NRUjB8/HgaDAQBgb2+P559/Hl5eXvj5558LvFKX1yB0SUlJePbZZxEbGwsACAoKwtChQ+Hs7IyNGzciIiICiYmJGDhwIC5evAgPDw8AwA8//ICQkBBER0fjzJkzePfdd+Hp6YlDhw4BADw9PfHzzz9Dp9NZvJ/U1FQMGzYMFy9eREBAAABg9uzZ+P3335XyBgYGon///vDw8EBERAS2b99u0eeWn65du8LV1RVLlizBlStXAADNmjUz60ru7e1d4DYuXbqEUaNGISMjAwDg4+ODMWPGQJIkrFixArGxscjIyMCYMWPQtGlT1KhRI9c2rly5AicnJ0ycOBHOzs5YsmQJ0tLSAAALFizA888//8i6rFixQrmSXLFiRYwcORJlypTBzZs3ER4ejqNHj5qtv2DBAqxatQrAwytjAwcORMOGDREVFYVVq1YhKysLGzZsQKNGjfDaa689cv8ffvih2RXali1bomvXrjh37hw2bNgAADh37hyee+455fcip0OHDqF27doYMGAATp06pVzhiouLww8//IB58+Y9shwFGTVqFE6dOgUAcHNzw4gRI1CxYkUcOnQIu3btgizLmDlzJpo1a4Y2bdoUatuFOYbPPfec2dX7QYMGoW7dutizZ4/yO/zrr7/iww8/xFtvvZXn/or7XLZz506LzmU5HTx4EEFBQRg4cCBcXFxw9+5dpc7Fma+vvvrK7Gp948aN0bt3b7Oy5qW4y5HTvn37MGrUKFSqVAm//vqr0qtl69atWLVqFUaPHg0AsLOzQ6NGjdCsWTP4+fnB3d0dKSkpOHToEPbt2wcAeO+99zB+/HhUqFAhz30dPHgQLVq0QJcuXZCSkgJPT0+0bdsWoaGhWLdunbKe6b3brVu3fmQdRo0ahZCQEOV1nTp10LNnTzg6OiIsLAzHjh0r9OdSGEX5275kyRLl/c2bN0fv3r1hMBhw48YNHDt2DOfOnSvRMhNpgrVb/URkHQVd6Y6LizO7uvvjjz+avXfSpEnKssaNGyvzc367/sILLyjL/u///s9s2eTJk5Vlw4YNU+Y3adLEbF+m76lXr57IyMhQlr344otmyy9evCiEEKJv377KvJxXPIV4eOUrJSXlkZ/RmjVrzLa/bNkys8/P3t5eWZbzSndevvrqK2V9Ly8vERcXpyxLTk4Wfn5+yvKvvvrK7L179+5VrozZ2dkJR0dHZd3169cXeT/vvfeeEEKI+/fvmx3zxo0biwcPHpht9/r16yIzM1Opv+lnY8mVbkuWPWqd6dOnK/N1Op04c+aMsuzMmTNCp9Mpy6dPn64sM71KCkBs3rxZWfbll1+aLUtKSsqzTKamTZumrP/RRx/lWn7//n1x//59IcTDq3+mvQ7eeusts3UXLFigLPPx8VF6LOT3GRuNRuHt7a3Mb9WqldmVw7lz55q9LywsTFlmOj8wMNCsro0bN1aWDRgw4JGfgRD5X9E7ffq02fy//vrL7H09e/ZUlvXv39+ifRXlGIaFhZnNnzt3rvIeg8Fg1lvF29tb+ext5VyWMwNVqlRReptkK2q+ClKrVi3lPdWrVxfp6en5ltU0l8Vdjpz5+uCDD5RliYmJZvtr06ZNrvdHRkaKtWvXikWLFon//e9/4tNPPzXrobJy5cp89zVgwIA8y1jQVexHrfPvv/+aze/Zs6dyTs12+fJlZbq4r3QX9W97cHCwMv/IkSO56hsVFWXR8SQqzXhPNxHlcuzYMeXqLgA8//zzZveVffPNN8qyU6dO5XnvKACMHDlSma5cubLZsiFDhijTpgO2xcfH51uuoUOHwsHBIc/tAw+vRgHA008/rcwbO3YsnnnmGbz00kv4/PPPcezYMZQtWxYuLi757idbaGio2WvTK/qVK1dG27ZtH7kNU9lXpoGH9fTx8VE+U1dXV9y7d09ZfvjwYbP3durUCXPmzAEA5Uoa8PDYDB48uMj7yf7Mjh49anbM582bB1dXV7PtBgYGwt7evlB1Lm6mvQuaNm1qNrJ5/fr10bRp0zzXNVWLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x600 with 1 Axes>"
|
||||
]
|
||||
@@ -529,13 +485,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 13,
|
||||
"id": "1e3fa2ff",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x600 with 1 Axes>"
|
||||
]
|
||||
|
||||
Reference in new issue
Block a user