arthur/ArtStudies
1
0
Fork 0
mirror of https://github.com/ArthurDanjou/ArtStudies.git synced 2026-02-01 10:29:32 +01:00
Code Issues Packages Projects Releases Wiki Activity

Correction de la mise en forme et mise à jour de la version de Python dans le notebook TP_2_M2_ISF.ipynb

Browse Source
This commit is contained in:
Arthur Danjou
2025-10-02 08:26:49 +02:00
parent 7b9a6bd0ff
commit c925c8a5c0
1 changed files with 10 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb
View File

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