bon, le code est 'bon', j'ai plus qu'à l'épurer et à mettre un peu d'aide au début, là les flèches ne dépasseront jamais un certain seuil, d'ailleurs c'est plus des flèches
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DylanVsn
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108
3/TP3_voisin.py
108
3/TP3_voisin.py
@ -5,6 +5,7 @@ from random import random, choice
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import numpy as np
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from time import sleep
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import argparse
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from math import * # pour utliser les fonctions mathématiques dans l'option -f
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"""
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MNI TP3 - 27 janvier 2020
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@ -68,31 +69,85 @@ def diagonaliser_matrice22(m):
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u1 = np.array([u2[1], -u2[0]])
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return u1, u2, lambda1, lambda2
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def get_u1_u2_affichage(u1, u2, bary, dist_x, dist_y, pb, pg):
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"""renvoie u1 et u2 prêt pour l'affichage en fonction du barycentre,
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de la distance x et y sur l'ensemble des points, le point le plus bas et
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le point le plus à gauche"""
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if np.linalg.norm(u1) == np.linalg.norm(u2) == 0:
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# un seul point
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u1 = np.array([1, 0]) / 500
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u2 = np.array([0, 1]) / 500
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return u1, u2
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bary = np.array((bary[0] - pg[0], bary[1] - pb[1]))
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if u1[0] != 0 and u1[1] != 0:
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if u1[0] > 0: # vers la droite
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t1x = (dist_x - bary[0]) / u1[0]
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else:
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t1x = -bary[0] / u1[0]
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if u1[1] > 0: # vers le haut
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t1y = (dist_y - bary[1]) / u1[1]
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else:
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t1y = -bary[1] / u1[1]
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t1 = min(t1x, t1y)
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elif u1[0] != 0:
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if u1[0] > 0: # vers la droite
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t1 = (dist_x - bary[0]) / u1[0]
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else:
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t1 = -bary[0] / u1[0]
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else:
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if u1[1] > 0: # vers le haut
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t1 = (dist_y - bary[1]) / u1[1]
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else:
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t1 = -bary[1] / u1[1]
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if u2[0] != 0 and u2[1] != 0:
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if u2[0] > 0: # vers la droite
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t2x = (dist_x - bary[0]) / u2[0]
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else:
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t2x = -bary[0] / u2[0]
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if u2[1] > 0: # vers le haut
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t2y = (dist_y - bary[1]) / u2[1]
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else:
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t2y = -bary[1] / u2[1]
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t2 = min(t2x, t2y)
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elif u2[0] != 0:
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if u2[0] > 0: # vers la droite
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t2 = (dist_x - bary[0]) / u2[0]
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else:
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t2 = -bary[0] / u2[0]
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else:
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if u2[1] > 0: # vers le haut
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t2 = (dist_y - bary[1]) / u2[1]
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else:
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t2 = -bary[1] / u2[1]
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u1 = u1 * t1 * 2/3
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u2 = u2 * t2 * 1/3
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return u1, u2
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def get_info_affichage(l_pts):
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"""renvoie les vecteurs u1 et u2 pour l'affichage ainsi que xmin, xmax, ymin, ymax"""
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bary = calc_barycentre(l_pts)
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u1, u2 = diagonaliser_matrice22(calc_matrice_corrélation(l_pts))[:2]
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pb = min(l_pts, key = lambda x: x[1])
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pg = min(l_pts, key = lambda x: x[0])
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dist = min(max(l_pts[:,0]) - bary[0], max(l_pts[:,1]) - bary[1])
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# dist = max(map(lambda x: np.linalg.norm(x-bary), l_pts))
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dist_x = max(l_pts[:,0]) - min(l_pts[:,0])
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dist_y = max(l_pts[:,1]) - min(l_pts[:,1])
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x_range = min(l_pts[:,0]) - 0.1 * dist_x, max(l_pts[:,0]) + 0.1 * dist_x
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y_range = min(l_pts[:,1]) - 0.1 * dist_y, max(l_pts[:,1]) + 0.1 * dist_y
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u1 = u1 / np.linalg.norm(u1) * dist
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u2 = u2 / np.linalg.norm(u2) * 1/2 * dist
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return u1, u2, x_range, y_range, dist
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u1, u2 = get_u1_u2_affichage(u1, u2, bary, dist_x, dist_y, pb, pg)
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# print(u1, u2)
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return u1, u2, x_range, y_range
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def visualiser_nuage(l_pts):
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bary = calc_barycentre(l_pts)
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u1, u2 = diagonaliser_matrice22(calc_matrice_corrélation(l_pts))[:2]
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dist = min(max(l_pts[:,0]) - bary[0], max(l_pts[:,1]) - bary[1])
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u1 = u1 / np.linalg.norm(u1) * dist
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u2 = u2 / np.linalg.norm(u2) * 1/2 * dist
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u1, u2 = get_info_affichage(l_pts)[:2]
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ax = plt.axes()
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# plot du nuage
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plt.scatter(l_pts[:,0], l_pts[:,1])
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ax.arrow(*bary, *u1, head_length=0.1*dist, head_width=0.1*dist, ec="black", fc="white")
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ax.arrow(*bary, *u2, head_length=0.1*dist, head_width=0.1*dist, ec="black", fc="white")
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ax.arrow(*bary, *u1, ec="red", fc="white")
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ax.arrow(*bary, *u2, ec="orange", fc="white")
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# ax.set_aspect('equal')
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plt.show()
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@ -103,10 +158,11 @@ def animation_nuage(l_pts, loops=10, time_sleep=2, delta_bruit=3):
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fig.canvas.draw()
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# nuage courant (pas de do while en Python)
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bary = calc_barycentre(l_pts)
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u1, u2, x_range, y_range, dist = get_info_affichage(l_pts)
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u1, u2 = get_info_affichage(l_pts)[:2]
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u1l = np.linalg.norm(u1)
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sc = ax.scatter(l_pts[:,0], l_pts[:,1])
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arr1 = ax.arrow(*bary, *u1, head_length=0.1*dist, head_width=0.1*dist, ec="black", fc="white")
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arr2 = ax.arrow(*bary, *u2, head_length=0.1*dist, head_width=0.1*dist, ec="black", fc="white")
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arr1 = ax.arrow(*bary, *u1, ec="red", fc="white")
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arr2 = ax.arrow(*bary, *u2, ec="orange", fc="white")
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plt.title("itération 0")
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fig.canvas.flush_events()
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fig.canvas.draw()
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@ -119,15 +175,16 @@ def animation_nuage(l_pts, loops=10, time_sleep=2, delta_bruit=3):
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l_pts += ran
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# recalcul des données
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bary = calc_barycentre(l_pts)
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u1, u2, x_range, y_range, dist = get_info_affichage(l_pts)
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u1, u2, x_range, y_range = get_info_affichage(l_pts)
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u1l = np.linalg.norm(u1)
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ax.set_xlim(*x_range)
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ax.set_ylim(*y_range)
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# plot
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sc.set_offsets(l_pts) # repositionnement des points
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arr1.remove() # on enlève les vecteurs
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arr2.remove()
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arr1 = ax.arrow(*bary, *u1, head_length=0.1*dist, head_width=0.1*dist, ec="black", fc="white")
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arr2 = ax.arrow(*bary, *u2, head_length=0.1*dist, head_width=0.1*dist, ec="black", fc="white")
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arr1 = ax.arrow(*bary, *u1, ec="red", fc="white")
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arr2 = ax.arrow(*bary, *u2, ec="orange", fc="white")
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fig.canvas.flush_events()
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fig.canvas.draw() # on redessine
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fig.canvas.flush_events()
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@ -141,17 +198,19 @@ def main(argv):
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gpi.add_argument("-i", "--input",
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help="input file for dots - if none, generate dots")
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gpi.add_argument("-f", "--function",
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help="function to use to generate dots, ex -f 3*x+4")
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parser.add_argument("-N", "--nb-dots", type=int, default=500,
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help="number of dots to generate (from 0 to NB_DOTS) from function")
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help="function to use to generate dots, ex -f \"3*x**2+cos(x)\"")
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parser.add_argument("-m", "--max-dot-xval", type=float, default=10,
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help="x range of generated dots (from 0 to MAX_DOT_VAL, step=0.1) from function")
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gps.add_argument("-s", "--show", action="store_true",
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help="show cloud of dots with u1 and u2")
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gps.add_argument("-l", "--loop", type=int, default=10,
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gps.add_argument("-l", "--loop", type=int,
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help="number of loop to show/perform on randomized dots from originals ones")
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parser.add_argument("-g", "--generate-with-noise", action="store_true",
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help="apply noise on dots generation")
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parser.add_argument("-d", "--delay", type=float, default=2,
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help="amount of time between each loop")
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parser.add_argument("-n", "--noise", type = float, default=10,
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help="maximum value of the noise applied between each step")
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help="maximum value of the noise applied between each step and on generation if option -g and -f called")
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if not argv:
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parser.print_usage()
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sys.exit(0)
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@ -164,10 +223,13 @@ def main(argv):
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l_pts = acquérir_depuis_fichier(args.input)
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else:
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l_pts = []
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n = args.nb_dots # nombre de points
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n = args.max_dot_xval
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f = lambda x: eval(args.function) # fonction génératrice de la ditribution
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delta_bruit = 0 # bruit généré, 0 si on en veut aucun
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for i in range(n):
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if args.generate_with_noise:
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delta_bruit = args.noise # bruit généré, 0 si on en veut aucun
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else:
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delta_bruit = 0
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for i in np.arange(0, n, 0.1):
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y = f(i) + choice((-1, 1)) * random() * delta_bruit/2
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l_pts.append([i, y])
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l_pts = np.array(l_pts)
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