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Correction de la mise en forme et mise à jour de la version de Python dans le notebook TP_2_M2_ISF.ipynb
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@@ -44,7 +44,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "97d58527",
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"id": "be5e7be4",
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -52,13 +52,13 @@
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"#Graphiques \n",
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"# Graphiques\n",
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"import seaborn as sns\n",
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"sns.set()\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as gp\n",
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"\n",
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"#Statistiques\n",
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"# Statistiques\n",
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"from scipy.stats import chi2_contingency\n",
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"\n",
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"# Machine Learning\n",
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@@ -78,7 +78,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "c9597b48",
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"metadata": {},
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"outputs": [],
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@@ -140,8 +140,7 @@
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"#Itinitialisation algo\n",
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"kmeans = KMeans(init=init_points.reshape(-1,1),\n",
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" n_clusters=#Complétez avec votre code,\n",
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" n_init = 1) \n",
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" "
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" n_init = 1)"
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]
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},
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{
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@@ -155,7 +154,7 @@
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"data_x = np.array(x)\n",
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"data_x = data_x.reshape(-1,1)\n",
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"\n",
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"# Fitting \n",
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"# Fitting\n",
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"kmeans.fit(data_x)"
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]
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},
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@@ -180,7 +179,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"#Représentation Graphique \n",
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"#Représentation Graphique\n",
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"plt.scatter(x, y, c=labels, cmap='viridis')\n",
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"plt.show()"
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]
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@@ -242,7 +241,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"#Complétez avec votre code "
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"#Complétez avec votre code"
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]
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},
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{
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@@ -519,7 +518,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "studies",
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"language": "python",
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"name": "python3"
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},
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@@ -533,7 +532,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.13.3"
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}
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},
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"nbformat": 4,
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