1314 lines
2.6 MiB
Text
1314 lines
2.6 MiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# TP2 Régression\n",
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"\n",
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"On souhaite faire de la modélisation sur des problématiques de régression. Comment construire un bon modèle et surtout un modèle optimale par rapport au jeu de données et à la famille de modèle utilisés.\n",
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"\n",
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"## 1. Modèles polynomiaux"
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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": 94,
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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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"import numpy as np\n",
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"from sklearn.preprocessing import PolynomialFeatures\n",
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"from sklearn.linear_model import LinearRegression"
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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": 95,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fonction de génération\n",
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"\n",
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"def genere_exemple_dim1(xmin, xmax, NbFx, sigma):\n",
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" x = np.arange(xmin, xmax, (xmax-xmin)/NbFx)\n",
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" y = np.sin(-np.pi + 2*x*np.pi)+np.random.normal(loc=0, scale=sigma, size=x.size)\n",
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" return x.reshape(-1,1), y"
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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": 96,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fonction EQM\n",
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"def getMSE(x, y, reg):\n",
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" return sum(pow(reg.predict(x)-y,2))/x.shape[0]"
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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": 97,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fonctions plot\n",
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"\n",
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"def plot_model(Xa, Ya, Xt, Yt, reg, degre, nameFig):\n",
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" Ypred = reg.predict(Xt)\n",
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" plt.plot(Xa[:,1], Ya, '*r')\n",
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" plt.plot(Xt[:,1], Yt, '-b')\n",
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" plt.plot(Xt[:,1], Ypred, '-r')\n",
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" plt.grid()\n",
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" plt.savefig(nameFig+'.jpg', dpi=200)\n",
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" plt.legend([\"Sortie bruitée\", \"Sortie réelle\", \"Modèle prédit\"])\n",
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" plt.title(\"Prédiction du modèle à degré %02d\" % (degre))\n",
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" plt.show()\n",
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" plt.close()\n",
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"\n",
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"def plot_error_profile(L_error_app, L_error_test, nameFig):\n",
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" plt.semilogy(range(1, len(L_error_app)+1), L_error_app, '-r')\n",
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" plt.semilogy(range(1, len(L_error_test)+1), L_error_test, '-b')\n",
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" plt.grid()\n",
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" plt.savefig(nameFig+'.jpg', dpi=200)\n",
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" plt.title(\"Profil d'erreur\")\n",
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" plt.ylabel(\"erreur\")\n",
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" plt.xlabel(\"degré du modèle\")\n",
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" plt.legend([\"EQMT\",\"EQMA\"])\n",
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" plt.show()\n",
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" plt.close()\n",
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"\n",
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"def plot_confusion(Xt, Yt, reg, nameFig):\n",
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" plt.plot(Yt, reg.predict(Xt), '.b')\n",
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" plt.plot(Yt, Yt, '-r')\n",
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" plt.savefig(nameFig+'.jpg', dpi=200)\n",
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" plt.legend([\"Y prédit\",\"Y désiré\"])\n",
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" plt.title(\"Graphe de confusion pour le meilleur modèle\")\n",
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" plt.show()\n",
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" plt.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Création du script principal pour l'exploration des exemples et du bruit sur les données"
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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": 98,
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"metadata": {},
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"outputs": [],
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"source": [
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"def main(degreMax=12, NbEx=20, sigma=0.2, ens=200):\n",
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" xmin = 0\n",
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" xmax = 1.2\n",
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" xapp, yapp = genere_exemple_dim1(xmin, xmax, NbEx, sigma)\n",
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" xtest, ytest = genere_exemple_dim1(xmin, xmax, ens, 0)\n",
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"\n",
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" L_error_app = []\n",
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" L_error_test = []\n",
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"\n",
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" for i in range(1, degreMax+1):\n",
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" print(\"Degre = \", i)\n",
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"\n",
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" # Transformation des données d'entrée des basses d'app et de test\n",
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" poly = PolynomialFeatures(i)\n",
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" Xa = poly.fit_transform(xapp)\n",
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" Xt = poly.fit_transform(xtest)\n",
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" # print(xapp)\n",
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"\n",
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" # Création du modèle linéaire\n",
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" reg = LinearRegression().fit(Xa,yapp)\n",
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"\n",
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" # Estimation des erreurs d'apprentissage et de test\n",
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" L_error_app.append(getMSE(Xa,yapp,reg))\n",
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" L_error_test.append(getMSE(Xt,ytest,reg))\n",
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"\n",
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" plot_model(Xa, yapp, Xt, ytest, reg, i, \"Model_%02d\"%i)\n",
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" \n",
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" # Determiner le degré optimal\n",
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" best = np.argmin(L_error_test)+1\n",
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" print('Meilleur modèle -> degre =', best)\n",
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" plot_error_profile(L_error_app, L_error_test, 'Profil_Err_App_Test')\n",
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" \n",
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" # Création du modèle final optimal\n",
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" poly = PolynomialFeatures(best)\n",
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" Xa = poly.fit_transform(xapp)\n",
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" Xt = poly.fit_transform(xtest)\n",
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"\n",
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" reg = LinearRegression().fit(Xa,yapp)\n",
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" plot_confusion(Xt, ytest, reg, 'Confusion')\n"
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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": 99,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Degre = 1\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Degre = 2\n"
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]
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},
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 3\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 4\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 5\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 6\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 7\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 8\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 9\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
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{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 10\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 11\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 12\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Meilleur modèle -> degre = 5\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"main(degreMax=12, NbEx=20, sigma=0.2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"On observe qu'à partir d'un certain moment (degré du modèle supérieur au degré sur la fenêtre observée) on observe que le modèle va venir faire la régression sur le bruit, c'est-à-dire que la précision devient trop grand pour approximer la fonction observée et le modèle va venir prendre en compte les petites variations dûes au bruit. On observe le degré optimal grâce à la courbe du profil d'erreur lorsque l'EQMA commence à remonter. On observe également l'effet du bruit et son écart à la fonction prédite grâce au graphique de confusion."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"On peut observer l'impact du nombre d'exemple sur le profil d'erreur."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 100,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 1\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 2\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 3\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 4\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 5\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 6\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 7\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 8\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 9\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 10\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 11\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 12\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Meilleur modèle -> degre = 6\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHHCAYAAABTMjf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAABX2klEQVR4nO3deZyNdf/H8deZfcYYuzEYEVpkLW63EJVtJmRNN0Kk7hpFQrq7k1aRtNxNirLc7UnoV4hESgthqNsuW5E1xjrGzPX749uZmWPOjFnOnGvOnPfz8TiPuc51Xeecz7lsb9/v9/p+HZZlWYiIiIj4oQC7CxARERGxi4KQiIiI+C0FIREREfFbCkIiIiLitxSERERExG8pCImIiIjfUhASERERv6UgJCIiIn5LQUhERET8loKQiBQbFy5cYMyYMcTGxhIQEEC3bt0AcDgcjB8/PuO8WbNm4XA42L17d4E+Z9CgQdSsWbPQ9YqI71MQEpFcOUOH8xEWFsYVV1zBsGHDOHjwoEc/a8aMGTz//PP06tWL2bNn8+CDD3r0/XPjcDiYNWuW1z5PRIqHILsLEBHf8OSTT1KrVi3OnTvHt99+y9SpU1m4cCG//PILERERHvmMr776imrVqvHiiy+67D979ixBQfrrSkQ8T3+ziEiexMXF0bRpUwDuuusuKlSowJQpU1iwYAH/+Mc/3L7m9OnTlCpVKs+fcejQIcqWLZttf1hYWIFqtsu5c+cICQkhICB7o3t+r0lhePOzRHyVusZEpEBuuukmAHbt2gWYcTeRkZHs3LmT+Ph4SpcuTb9+/QDzD/JDDz1EbGwsoaGhXHnllUyePBnLsgDYvXs3DoeD5cuX87///S+jG27FihVA9jFC+TF//nzq169PWFgY9evXZ968eXl+7e+//87gwYOJjo4mNDSUa665hhkzZrics2LFChwOBx988AH//ve/qVatGhERESQnJ+d6TdLT03nppZe45pprCAsLIzo6mnvuuYc///zT5f1z+u41a9Zk0KBBGc+dXZhff/019913H5UrV6Z69ep5v1AifkotQiJSIDt37gSgQoUKGfsuXLhAx44dadWqFZMnTyYiIgLLsujatSvLly9nyJAhNG7cmC+++ILRo0fz+++/8+KLL1KpUiXefvttnnnmGU6dOsWECRMAuPrqqwtV45IlS+jZsyf16tVjwoQJHD16lDvvvDNPAeHgwYP8/e9/x+FwMGzYMCpVqsSiRYsYMmQIycnJjBgxwuX8p556ipCQEEaNGkVKSgohISE5XhOAe+65h1mzZnHnnXfywAMPsGvXLl599VXWr1/PqlWrCA4OLtB3vu+++6hUqRLjxo3j9OnTBXoPEb9iiYjkYubMmRZgffnll9bhw4etffv2WR988IFVoUIFKzw83Prtt98sy7KsgQMHWoA1duxYl9fPnz/fAqynn37aZX+vXr0sh8Nh7dixI2NfmzZtrGuuuSZbDYD1+OOPZ6tp165dudbeuHFjKyYmxjp+/HjGviVLlliAddlll+X62iFDhlgxMTHWkSNHXPbffvvtVpkyZawzZ85YlmVZy5cvtwDr8ssvz9jnlNM1+eabbyzAevfdd132L168ONv+i7+702WXXWYNHDgw47nzmrRq1cq6cOFCrt9NRDKpa0xE8qRdu3ZUqlSJ2NhYbr/9diIjI5k3bx7VqlVzOe/ee+91eb5w4UICAwN54IEHXPY/9NBDWJbFokWLiqTeAwcOkJSUxMCBAylTpkzG/vbt21OvXr1cX2tZFnPnzqVLly5YlsWRI0cyHh07duTEiROsW7fO5TUDBw4kPDzc7ftdfE3mzJlDmTJlaN++vct7X3fddURGRrJ8+fICfmsYOnQogYGBBX69iL9R15iI5EliYiJXXHEFQUFBREdHc+WVV2YbDBwUFJSt22nPnj1UrVqV0qVLu+x3dnvt2bOnSOp1vm/dunWzHbvyyiuzBZmsDh8+zPHjx5k2bRrTpk1ze86hQ4dcnteqVcvtee6uyfbt2zlx4gSVK1fO03vnR051iIh7CkIikid/+9vfMu4ay0loaKjbO6V8TXp6OgD9+/dn4MCBbs9p2LChy/OcWoPcXZP09HQqV67Mu+++6/Y1lSpVumSNaWlpbvfnVIeIuKcgJCJF6rLLLuPLL7/k5MmTLq1CW7ZsyTheVJ8LpvXlYlu3bs31tZUqVaJ06dKkpaXRrl07j9dWu3ZtvvzyS1q2bHnJ4FKuXDmOHz/usu/8+fMcOHDA43WJ+CPf/6+biBRr8fHxpKWl8eqrr7rsf/HFF3E4HMTFxRXJ58bExNC4cWNmz57NiRMnMvYvXbqUTZs25frawMBAevbsydy5c/nll1+yHT98+HCharvttttIS0vjqaeeynbswoULLsGndu3arFy50uWcadOm5dgiJCL5oxYhESlSXbp04cYbb+TRRx9l9+7dNGrUiCVLlrBgwQJGjBhB7dq1i+yzJ0yYwC233EKrVq0YPHgwx44d4z//+Q/XXHMNp06dyvW1zz33HMuXL6d58+YMHTqUevXqcezYMdatW8eXX37JsWPHClxXmzZtuOeee5gwYQJJSUl06NCB4OBgtm/fzpw5c3j55Zfp1asXYCav/Oc//0nPnj1p3749GzZs4IsvvqBixYoF/nwRyaQgJCJFKiAggE8//ZRx48bx4YcfMnPmTGrWrMnzzz/PQw89VKSf3alTJ+bMmcO///1vHnnkEWrXrs3MmTNZsGBBxmSNOYmOjmb16tU8+eSTfPLJJ7z22mtUqFCBa665hokTJxa6ttdff53rrruON954g3/9618EBQVRs2ZN+vfvT8uWLTPOGzp0KLt27eKtt95i8eLFtG7dmqVLl3LzzTcXugYRAYdl/TW1q4iIiIif0RghERER8VsKQiIiIuK3FIRERETEbykIiYiIiN9SEBIRERG/pSAkIiIifkvzCOUiPT2d/fv3U7p0aRwOh93liIiISB5YlsXJkyepWrXqJdc/VBDKxf79+4mNjbW7DBERESmAffv2Ub169VzPURDKhXOByH379hEVFWVzNd6XmprKkiVLMqb/l4LRdfQMXUfP0HX0DF1Hzyiq65icnExsbKzLQs85URDKhbM7LCoqym+DUEREBFFRUfqDXgi6jp6h6+gZuo6eoevoGUV9HfMyrEWDpUVERMRvKQiJiIiI31IQEhEREb+lMUIiIiIeZlkWFy5cIC0tze5SirXU1FSCgoI4d+5cvq9VYGAgQUFBhZ7eRkHIjcTERBITE/UbWERE8u38+fMcOHCAM2fO2F1KsWdZFlWqVGHfvn0FCjQRERHExMQQEhJS4BoUhNxISEggISGB5ORkypQpY3c5IiLiI9LT09m1axeBgYFUrVqVkJAQTcibi/T0dE6dOkVkZOQlJz7MyrIszp8/z+HDh9m1axd169bN1+uzUhASERHxkPPnz5Oenk5sbCwRERF2l1Pspaenc/78ecLCwvIdZMLDwwkODmbPnj0Z71EQGiwtIiLiYQVtnZD88cR11q+UiIiI+C0FIREREfFbCkIiIiLitxSE7GJZdlcgIiKSYdCgQTgcjmyPTp06ZZzz3XffER8fT7ly5QgLC6NBgwZMmTIl23Qzztf+8MMPLvtTUlKoUKECDoeDFStWMGvWLMqVK0dgYKDbz3Y4HOzevbtIv7eCkF2GDIHx4yElxe5KREREAOjUqRMHDhxwebz//vsAzJs3jzZt2lC9enWWL1/Oli1bGD58OE8//TS333471kX/wY+NjWXmzJku++bNm0dkZGTG8z59+rBlyxZ+//13Dhw4QIsWLRg6dKjL58fGxhbpd9bt83ZYswacvzk++gimTYNWreytSUREioZlgV2TK0ZEQD7mMQoNDaVKlSrZ9p8+fZqhQ4fStWtXpk2blrH/rrvuIjo6mq5du/LRRx/Rp0+fjGMDBw7klVde4aWXXiI8PByAGTNmMHDgQJ566inA3AIfHR1NVFQUAQEBhISEEBER4baGoqIWITs0bQoffgjR0bB5M7RuDffeCydO2F2ZiIh42pkzEBlpz8NDAWzJkiUcPXq
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"main(degreMax=12, NbEx=100, sigma=0.2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"On va pouvoir mieux approximer les observations, c'est-à-dire diminuer l'erreur pour le degré optimal mais on aura toujours le cas d'EQMA qui remonte."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"L'évolution de l'écart-type va venir augmenter l'erreur globale sur le profil d'erreur en diminuant la précision des mesures."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 101,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 1\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 2\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 3\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAG0CAYAAAD6ncdZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAACYu0lEQVR4nOzdd3hTZRvA4d9JuillFgpS9t577z1kiYCAQpElMgRRhn4sZQiigGxEloBscLEKskGQUbYsKSAbhJbVmfP98ZK0pTPQNB3PfV29kpycnPPk7Wnz5J2arus6QgghhBAphMHeAQghhBBCWEOSFyGEEEKkKJK8CCGEECJFkeRFCCGEECmKJC9CCCGESFEkeRFCCCFEiiLJixBCCCFSFElehBBCCJGiSPIi0pQ9e/bwxRdfEBAQYO9QhJ2dPn2aMWPG8O+//ybpeU+ePMmYMWO4efNmkp43KZnf4/Xr1+0dikilJHkRacbVq1dp06YN6dOnJ0OGDAl6Td68efHx8bE83rVrF5qmsWvXrkSLS9M0xowZk2jHe10+Pj7kzZvX3mEkqpjKuHjx4pw6dYqOHTsSFhYW4+v8/f3RNI3FixcnWiwlS5bk+PHjdO7cmfDw8EQ5Zt26dalbt26iHOt1BQQE0LZtWx4+fIi3t7e9wxGplCQvItlavHgxmqZZflxcXChcuDD9+/fnzp07Vh0rNDSUjh074uPjw+DBg20Ucew2bdqUrBIUAQaDgeXLlwMwbNiwJD3vTz/9xPPnzxk9enSSnTepdO/enXLlyjF16tQEvyY4OJhhw4aRM2dOXF1dqVKlCr6+vtH2mzBhAlWrVsXT0xMXFxcKFSrEoEGDuHfvXmK+BZECSPIikr0vvviCH3/8kZkzZ1K9enXmzJlDtWrVePbsWYKPcebMGd555x2++eab14qldu3aPH/+nNq1a1v1uk2bNjF27NgYn3v+/Dn/+9//Xisu8WpcXFz45ZdfyJAhA48ePUqy87q5ufHrr7/i7OzM06dPk+y8tubv70/FihVZtmwZBkPCP158fHz49ttv6dKlC9OnT8doNNK8eXP27dsXZb+jR49StmxZPv/8c2bNmkXr1q1ZtGgR1atXT1XlKOLnYO8AhIhPs2bNqFixIgA9e/YkS5YsfPvtt/z888906tQpxtc8ffqUdOnSWR6XLVuWsmXLvnYsBoMBFxeX1z5OZIl9PGGdLFmyMGrUqCQ/b7Zs2Rg5cmSSnzcxvfx3ljdvXj777DOrjnH48GFWrlzJ119/zSeffAJA165dKVmyJEOHDuXAgQOWfdetWxft9dWqVePtt9/m119/5Z133nnFdyJSGql5ESlO/fr1Abhy5QqgvrW5u7tz+fJlmjdvTvr06enSpQsAJpOJadOmUaJECVxcXMiePTt9+vTh4cOHUY6p6zrjxo0jV65cuLm5Ua9ePc6cORPt3LH1eTl06BDNmzcnU6ZMpEuXjtKlSzN9+nRLfLNmzQKI0gxmFlN/jOPHj9OsWTM8PDxwd3enQYMG/Pnnn1H2MTer7d+/n48//hhPT0/SpUtH27ZtE1yNvnHjRkqWLImLiwslS5Zkw4YNCX7PCe0PYo5z3759DBw4EE9PTzJmzEifPn0ICQnh0aNHdO3alUyZMpEpUyaGDh3Ky4vdP336lCFDhuDt7Y2zszNFihRhypQp0fYLDg5m8ODBeHp6kj59elq1ahVrh9wbN27w/vvvkz17dpydnSlRogQLFiyIv9CAv//+m7fffpvMmTPj4uJCxYoV+eWXXxL02ilTplC9enWyZMmCq6srFSpUYO3atQl6LcD8+fMpUKAArq6uVK5cmb1798a4X3BwMKNHj6ZgwYI4Ozvj7e3N0KFDCQ4OjrLf8+fPGThwIFmzZrWU2Y0bN6Jdl2PGjEHTNM6ePUvnzp3JlCkTNWvWtDy/bNkyKlSogKurK5kzZ+add95JUIfdtWvXYjQa6d27t2Wbi4sLPXr04ODBg/Eew9w/KylrzoT9Sc2LSHEuX74MqG/MZmFhYTRp0oSaNWsyZcoU3NzcAOjTpw+LFy+me/fuDBw4kCtXrjBz5kyOHz/O/v37cXR0BGDUqFGMGzeO5s2b07x5c44dO0bjxo0JCQmJNx5fX1/efPNNcuTIwUcffYSXlxfnzp3jt99+46OPPqJPnz7cvHkTX19ffvzxx3iPd+bMGWrVqoWHhwdDhw7F0dGRefPmUbduXXbv3k2VKlWi7D9gwAAyZcrE6NGj8ff3Z9q0afTv359Vq1bFeZ5t27bRrl07ihcvzsSJE3nw4AHdu3cnV65c8cb4KgYMGICXlxdjx47lzz//ZP78+WTMmJEDBw6QO3duJkyYwKZNm/j6668pWbIkXbt2BVRi2apVK3bu3EmPHj0oW7YsW7du5dNPP+XGjRtR+lb07NmTZcuW0blzZ6pXr84ff/xBixYtosVy584dqlatiq7r9OvXD09PT7Zs2UKvXr0ICAhgyJAhsb6PM2fOUKNGDd544w2GDx9OunTpWL16NW3atGHdunW0bds2znKYPn06rVq1okuXLoSEhLBy5Urat2/Pb7/9FmOskf3www/06dOH6tWrM2jQIP755x9atWpF5syZo3SONZlMtGrVin379tG7d2+KFSvGqVOnmDp1KhcuXGDjxo2WfX18fFi9ejXvvfceVatWZffu3XHG0b59ewoVKsSECRMsyeP48eMZOXIkHTp0oGfPnty7d48ZM2ZQu3Ztjh8/TsaMGWM93vHjxylcuDAeHh5RtleuXBkAPz+/KO9N13UePHhAWFgYFy9eZPjw4RiNxmTTYVkkEV2IZGrRokU6oG/fvl2/d++efv36dX3lypV6lixZdFdXV/3ff//VdV3Xu3XrpgP68OHDo7x+7969OqAvX748yvYtW7ZE2X737l3dyclJb9GihW4ymSz7ffbZZzqgd+vWzbJt586dOqDv3LlT13VdDwsL0/Ply6fnyZNHf/jwYZTzRD5Wv3799Nj+3AB99OjRlsdt2rTRnZyc9MuXL1u23bx5U0+fPr1eu3btaOXTsGHDKOcaPHiwbjQa9UePHsV4PrOyZcvqOXLkiLLftm3bdEDPkydPrO/Z7MqVKzqgL1q0KM7zmONs0qRJlDirVauma5qmf/DBB5ZtYWFheq5cufQ6depYtm3cuFEH9HHjxkU57ttvv61rmqZfunRJ13Vd9/Pz0wH9ww8/jLJf586do5Vxjx499OzZs+t3796Nsm+HDh10Dw8P/enTp7G+xwYNGuilSpXSg4KCLNtMJpNevXp1vVChQnGWha7r+rNnz6I8DgkJ0UuWLKnXr18/zteFhITo2bJl08uWLasHBwdbts+fP18HopTZjz/+qBsMBn3v3r1RjjF37lwd0Pfv36/ruq4fPXpUB/RBgwZF2c/HxydamY0ePVoH9E6dOkXZ19/fXzcajfr48eOjbD916pTu4OAQbfvLSpQoEeN7P3PmjA7oc+fOjbL91q1bOmD5yZUrl75q1ao4zyFSH2k2Eslew4YN8fT0xNvbm3feeQd3d3c2bNjAG2+8EWW/vn37Rnm8Zs0aMmTIQKNGjbh//77lp0KFCri7u7Nz504Atm/fTkhICAMGDIjSnDNo0KB4Yzt+/DhXrlxh0KBB0b5dRj5WQoWHh7Nt2zbatGlD/vz5Ldtz5MhB586d2bdvH4GBgVFe07t37yjnqlWrFuHh4Vy9ejXW89y6dQs/Pz+6desWZdh4o0aNKF68uNVxJ0SPHj2ixFmlShV0XadHjx6WbUajkYoVK/LPP/9Ytm3atAmj0cjAgQOjHG/IkCHous7mzZst+wHR9nv596jrOuvWraNdu3akT5+eoKAgy0/btm0JDAzk2LFjMb6H//77jz/++IMOHTrw+PFjyzX14MEDmjRpwsWLF7lx40ac5eDq6mq5//DhQwICAqhVq1as5zQ7cuQId+/e5YMPPsDJycmy3cfHJ9rQ/zVr1lCsWDGKFi0a5do3N7mar/0tW7YA8OGHH0Z5/YABA2KN44MPPojyeP369ZhMJjp06BDlXF5eXhQqVMhyrtg8f/4cZ2fnaNvNfcG
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 4\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 5\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 6\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 7\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 8\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 9\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 10\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 11\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 12\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Meilleur modèle -> degre = 4\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"main(degreMax=12, NbEx=20, sigma=0.5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 108,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 1\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 2\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 3\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 4\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 5\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 6\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 7\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 8\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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||
|
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"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 9\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 10\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 11\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Degre = 12\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAG0CAYAAAD6ncdZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAACWpElEQVR4nOzdd3iTZRfA4V+6W0opo1AqhbJkbxTZe3+AioCCIggIyJAlMmQvQRSQ7aIMkSEIyh6yQZBRRJaMlr2hLWV05f3+eEhK6UpK0yTtua+rV5J35eRp2pw8U6dpmoYQQgghhJ1wsHYAQgghhBDmkORFCCGEEHZFkhchhBBC2BVJXoQQQghhVyR5EUIIIYRdkeRFCCGEEHZFkhchhBBC2BVJXoQQQghhVyR5EZnK7t27GTt2LGFhYdYORVjZv//+y+jRo7l69Wq6Pu8///zD6NGjuX79ero+b3oyvMYrV65YOxSRQUnyIjKNS5cu8eabb5I1a1ayZctm0jkBAQF06tTJ+Hjnzp3odDp27tyZZnHpdDpGjx6dZtd7WZ06dSIgIMDaYaSpxMq4ZMmSnDhxgnbt2hETE5PoeSEhIeh0OgIDA9MsltKlS3Ps2DHat29PbGxsmlyzTp061KlTJ02u9bLCwsJ46623ePDgAf7+/tYOR2RQkrwImxUYGIhOpzP+uLm58eqrr9K7d29u3bpl1rWio6Np164dnTp1on///haKOGkbNmywqQRFgIODAz///DMAn3/+ebo+7y+//MKTJ08YNWpUuj1veuncuTMVKlRg2rRpJh0fERHBqFGjaNKkCTly5EgyWdTr9QQGBtKyZUv8/f3JkiULpUuXZvz48Tx9+jSNX4WwdZK8CJs3duxYFi9ezKxZs6hWrRpz586latWqPH782ORrnDx5knfffZevv/76pWKpVasWT548oVatWmadt2HDBsaMGZPovidPnvDFF1+8VFwiddzc3Pj999/Jli0boaGh6fa8Hh4e/PHHH7i6uvLo0aN0e15LCwkJoXLlyixZsgQHB9M+Xu7evcvYsWM5ffo05cqVS/K4x48f07lzZ+7cuUOPHj2YPn06r7/+OqNGjaJp06bIMn2Zi5O1AxAiJU2bNqVy5coAdO3alZw5c/LNN9+wdu1a3nvvvUTPefToEVmyZDE+Ll++POXLl3/pWBwcHHBzc3vp6zwvra8nzJMzZ05GjhyZ7s+bO3duRowYke7Pm5Ze/DsLCAhg2LBhZl0jb9683LhxA19fXw4fPsxrr72W6HEuLi7s27ePatWqGbd169aNgIAARo0axfbt22nQoEHqXoiwO1LzIuxOvXr1AAgODgZUHw1PT08uXLhAs2bNyJo1Kx06dABUVfP06dMpVaoUbm5u5MmTh+7du/PgwYN419Q0jfHjx5MvXz48PDyoW7cuJ0+eTPDcSfV5OXjwIM2aNSN79uxkyZKFsmXLMmPGDGN8s2fPBojXDGaQWH+MY8eO0bRpU7y8vPD09KR+/fr89ddf8Y4xNKvt27ePAQMG4OPjQ5YsWXjrrbe4c+eOSWW5Zs0aSpcujZubG6VLl+a3334z+TWb2h/EEOfevXvp27cvPj4+eHt70717d6KioggNDaVjx45kz56d7NmzM3jw4ATfoh89esTAgQPx9/fH1dWVYsWKMXXq1ATHRUZG0r9/f3x8fMiaNSstW7ZMskPutWvX+Oijj8iTJw+urq6UKlWKH374IeVCA86cOcM777xDjhw5cHNzo3Llyvz+++8mnTt16lSqVatGzpw5cXd3p1KlSvz6668mnQvw3XffUbhwYdzd3Xn99dfZs2dPosdFRkYyatQoihQpgqurK/7+/gwePJjIyMh4xz158oS+ffuSK1cuY5ldu3Ytwfty9OjR6HQ6Tp06Rfv27cmePTs1atQw7l+yZAmVKlXC3d2dHDly8O6775rUYdfV1RVfX98Uj3NxcYmXuBi89dZbAJw+fTrFa4iMQ2pehN25cOECoL4xG8TExNC4cWNq1KjB1KlT8fDwAKB79+4EBgbSuXNn+vbtS3BwMLNmzeLYsWPs27cPZ2dnAEaOHMn48eNp1qwZzZo14+jRozRq1IioqKgU49m6dSv/+9//yJs3L59++im+vr6cPn2adevW8emnn9K9e3euX7/O1q1bWbx4cYrXO3nyJDVr1sTLy4vBgwfj7OzM/PnzqVOnDrt27aJKlSrxju/Tpw/Zs2dn1KhRhISEMH36dHr37s3y5cuTfZ4tW7bQunVrSpYsyaRJk7h37x6dO3cmX758KcaYGn369MHX15cxY8bw119/8d133+Ht7c3+/fvJnz8/EydOZMOGDXz11VeULl2ajh07AiqxbNmyJTt27KBLly6UL1+ezZs389lnn3Ht2rV4fSu6du3KkiVLaN++PdWqVePPP/+kefPmCWK5desWb7zxBpqm0atXL3x8fNi0aRPdunUjLCyMgQMHJvk6Tp48SfXq1XnllVcYMmQIWbJkYcWKFbz55pusWrXK+GGalBkzZtCyZUs6dOhAVFQUy5Yto02bNqxbty7RWJ/3448/0r17d6pVq0a/fv24ePEiLVu2JEeOHPE6x+r1elq2bMnevXv5+OOPKVGiBCdOnGDatGn8999/rFmzxnhsp06dWLFiBR988AFvvPEGu3btSjaONm3aULRoUSZOnGhMHidMmMCIESNo27YtXbt25c6dO8ycOZNatWpx7NgxvL29k31dL+PmzZsA5MqVy2LPIWyQJoSNWrBggQZo27Zt0+7cuaNduXJFW7ZsmZYzZ07N3d1du3r1qqZpmvbhhx9qgDZkyJB45+/Zs0cDtJ9//jne9k2bNsXbfvv2bc3FxUVr3ry5ptfrjccNGzZMA7QPP/zQuG3Hjh0aoO3YsUPTNE2LiYnRChYsqBUoUEB78OBBvOd5/lq9evXSkvpzA7RRo0YZH7/55puai4uLduHCBeO269eva1mzZtVq1aqVoHwaNGgQ77n69++vOTo6aqGhoYk+n0H58uW1vHnzxjtuy5YtGqAVKFAgyddsEBwcrAHaggULkn0eQ5yNGzeOF2fVqlU1nU6n9ejRw7gtJiZGy5cvn1a7dm3jtjVr1miANn78+HjXfeeddzSdTqedP39e0zRNCwoK0gDtk08+iXdc+/btE5Rxly5dtDx58mi3b9+Od2zbtm01Ly8v7dGjR0m+xvr162tlypTRnj59atym1+u1atWqaUWLFk22LDRN0x4/fhzvcVRUlFa6dGmtXr16yZ4XFRWl5c6dWytfvrwWGRlp3P7dd99pQLwyW7x4sebg4KDt2bMn3jXmzZunAdq+ffs0TdO0I0eOaIDWr1+/eMd16tQpQZmNGjVKA7T33nsv3rEhISGao6OjNmHChHjbT5w4oTk5OSXYnpy///7bpPfU8xo0aKB5eXkl+PsTGZs0Gwmb16BBA3x8fPD39+fdd9/F09OT3377jVdeeSXecT179oz3eOXKlWTLlo2GDRty9+5d40+lSpXw9PRkx44dAGzbto2oqCj69OkTrzmnX79+KcZ27NgxgoOD6devX4Jvl89fy1SxsbFs2bKFN998k0KFChm3582bl/bt27N3717Cw8PjnfPxxx/He66aNWsSGxvLpUuXknyeGzduEBQUxIcffhhv2HjDhg0pWbKk2XGbokuXLvHirFKlCpqm0aVLF+M2R0dHKleuzMWLF43bNmzYgKOjI3379o13vYEDB6JpGhs3bjQeByQ47sXfo6ZprFq1itatW5M1a1aePn1q/HnrrbcIDw/n6NGjib6G+/fv8+eff9K2bVsePnxofE/du3ePxo0bc+7cOa5du5ZsObi7uxvvP3jwgLCwMGrWrJnkcxocPnyY27dv06NHD1xcXIzbO3XqlGDo/8qVKylRogTFixeP9943NLka3vubNm0C4JNPPol3fp8+fZKMo0ePHvEer169Gr1eT9u2beM9l6+vL0WLFjU+lyVMnDiRbdu28eWXX1q0dkfYHmk2EjZv9uzZvPrqqzg5OZEnTx6KFSuWYCSDk5NTguaOc+fOERYWRu7cuRO97u3btwGMH/JFixa
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Meilleur modèle -> degre = 7\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"main(degreMax=12, NbEx=20, sigma=0.2, ens=500)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## 2. Modélisation par réseaux de neurones MLP\n",
|
||
|
|
"\n",
|
||
|
|
"On souhaite à nouveau trouver un modèle de prédiction à partir des observations. Cette fois-ci, on n'utilise pas de solution unique mais plutôt une convergence de solution sur une moyenne de plusieurs initialisation du réseau pour chaque niveau de complexité. On aurra cette fois, un problème de surapprentissage qui sera observé lorsque l'on entraîne notre modèle."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 145,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from matplotlib import cm\n",
|
||
|
|
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
|
|
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
|
||
|
|
"from sklearn.neural_network import MLPRegressor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 155,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def sinus_cardinal(x): \n",
|
||
|
|
" A = np.array([1, 1], [-2, 1])\n",
|
||
|
|
" b = np.array([0.2, -0.3])\n",
|
||
|
|
"\n",
|
||
|
|
" x = -np.pi + 2*x*np.pi\n",
|
||
|
|
" z = A.dot(x + b)\n",
|
||
|
|
" h = np.sqrt(np.transpose(z).dot(z))\n",
|
||
|
|
" if np.abs(h) < 0.001:\n",
|
||
|
|
" y = 1\n",
|
||
|
|
" else:\n",
|
||
|
|
" y = np.sin(h)/h\n",
|
||
|
|
" return y"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 147,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def plot_surf(figName, regr=None):\n",
|
||
|
|
" step_v = 0.005\n",
|
||
|
|
"\n",
|
||
|
|
" x1v = np.arange(0,1,step_v)\n",
|
||
|
|
" x2v = np.arange(0,1,step_v)\n",
|
||
|
|
" Xv, Yv = np.meshgrid(x1v, x2v)\n",
|
||
|
|
"\n",
|
||
|
|
" R = np.zeros(Xv.shape)\n",
|
||
|
|
" for i,x1 in enumerate(x1v):\n",
|
||
|
|
" for j,x2 in enumerate(x2v):\n",
|
||
|
|
" if not regr:\n",
|
||
|
|
" R[i,j] = sinus_cardinal(np.array([x1, x2]))\n",
|
||
|
|
" else:\n",
|
||
|
|
" R[i,j] = regr.predict(np.array([[x1,x2]]))[0]\n",
|
||
|
|
" \n",
|
||
|
|
" fig = plt.figure()\n",
|
||
|
|
" ax = fig.gca(projection='3d')\n",
|
||
|
|
"\n",
|
||
|
|
" # Plot the surface\n",
|
||
|
|
" surf = ax.plot_surface(Xv, Yv, R, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n",
|
||
|
|
"\n",
|
||
|
|
" ax.set_zim(-1.01, 1.01)\n",
|
||
|
|
" ax.zaxis.set_major_locator(LinearLocator(10))\n",
|
||
|
|
" ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
|
||
|
|
"\n",
|
||
|
|
" fig.colorbar(surf, shrink=0.5, aspect=5)\n",
|
||
|
|
" plt.savefig(figName+'.png', dpi=300)\n",
|
||
|
|
" plt.close()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 156,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def main(fileData):\n",
|
||
|
|
" # Chargement des données d'entrainement\n",
|
||
|
|
" with open(fileData, 'r') as f:\n",
|
||
|
|
" f.readline()\n",
|
||
|
|
" Xapp = np.loadtxt(f, delimiter = \";\", skiprows=1)\n",
|
||
|
|
" \n",
|
||
|
|
" Yapp = []\n",
|
||
|
|
" for x in Xapp:\n",
|
||
|
|
" print(x)\n",
|
||
|
|
" Yapp.append(sinus_cardinal(x))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" # regr = MLPRegressor(\n",
|
||
|
|
" # hidden_layer_sizes=(16,16,16),\n",
|
||
|
|
" # max_iter=500,\n",
|
||
|
|
" # verbose=True,\n",
|
||
|
|
" # random_state=2,\n",
|
||
|
|
" # ).fit(X,Y)\n",
|
||
|
|
" \n",
|
||
|
|
" # plot_surf(\"mlp_test\")\n",
|
||
|
|
"\n",
|
||
|
|
" # plot_surf(\"mlp_pred\", regr=regr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 157,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"fileData = \"sinc_dim2_input.csv\""
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 158,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[0.3475 0.1425]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"ename": "TypeError",
|
||
|
|
"evalue": "Field elements must be 2- or 3-tuples, got '-2'",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001b[0;32mIn [158], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m main(fileData\u001b[39m=\u001b[39;49mfileData)\n",
|
||
|
|
"Cell \u001b[0;32mIn [156], line 10\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(fileData)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m Xapp:\n\u001b[1;32m 9\u001b[0m \u001b[39mprint\u001b[39m(x)\n\u001b[0;32m---> 10\u001b[0m Yapp\u001b[39m.\u001b[39mappend(sinus_cardinal(x))\n",
|
||
|
|
"Cell \u001b[0;32mIn [155], line 2\u001b[0m, in \u001b[0;36msinus_cardinal\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msinus_cardinal\u001b[39m(x): \n\u001b[0;32m----> 2\u001b[0m A \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49marray([\u001b[39m1\u001b[39;49m, \u001b[39m1\u001b[39;49m], [\u001b[39m-\u001b[39;49m\u001b[39m2\u001b[39;49m, \u001b[39m1\u001b[39;49m])\n\u001b[1;32m 3\u001b[0m b \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m0.2\u001b[39m, \u001b[39m-\u001b[39m\u001b[39m0.3\u001b[39m])\n\u001b[1;32m 5\u001b[0m x \u001b[39m=\u001b[39m \u001b[39m-\u001b[39mnp\u001b[39m.\u001b[39mpi \u001b[39m+\u001b[39m \u001b[39m2\u001b[39m\u001b[39m*\u001b[39mx\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mpi\n",
|
||
|
|
"\u001b[0;31mTypeError\u001b[0m: Field elements must be 2- or 3-tuples, got '-2'"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"main(fileData=fileData)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.8.10 64-bit",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.10"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|