How to use imprimer method in SeleniumBase

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FonctionTP_sklearn.py

Source: FonctionTP_sklearn.py Github

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1#Fonction utilisées dans la partie 2 tu TP 2 de Machine learning 2020/​20212from matplotlib.pyplot import subplot3from matplotlib import cm4from sklearn import linear_model, datasets5from sklearn.metrics import mean_squared_error, r2_score6import numpy as np7from numpy.core.multiarray import result_type8import pylab as pl9import matplotlib.pyplot as mp10import math11## Inplementation de l'algoritme permettant de recuperer la régression à partir d'une liste de données (x_i,y_i)12def prediction(model, ensemble):13 """14 Renvoie un ensemble de valeurs prédites pour l'ensemble donné par le model donné.15 """16 result = model.predict(ensemble)17 return result18def erreurSkl(valeursReelles , valeursPredites):19 """20 Renvoie l'erreur mse du model étant donnés un ensemble de valeurs prédites et un ensemble de valeur réelles.21 """22 result = mean_squared_error(valeursReelles , valeursPredites)23 return result24# def planEnFonctionVecteurPonderation(sub_plot,W, zOrder = 0):25# """26# Desine le plan normal à W dans la sous figure sub_plot.27# """28# if W.shape[0] == 3:29# a,b,d = W[0], W[1], W[-1]30# x_ = np.linspace(-2,2,10)31# y_ = np.linspace(-2,2,10)32# X_,Y_ = np.meshgrid(x_,y_)33# Z_ = d + a*X_ + b*Y_34# sub_plot.plot_surface(X_, Y_, Z_, cmap=cm.plasma, zorder = zOrder)35# pass36# elif W.shape[0] == 2:37# a,b,d = W[0], W[-1]38# x_ = np.linspace(-1,1,10)39# y_ = np.linspace(-1,1,10)40# X_,Y_ = np.meshgrid(x_,y_)41# Z_ = a*X_ + b*Y_42# sub_plot.plot_surface(X_, Y_, Z_)43# else:44# print("Le vecteur de ponderation ne comporte pas le bon nombre de paramètrd")45# pass46# def rss(vecteurPonderation, X, Y):47# """48# Calcul la somme des moindres carrée (Residual sum of squares).49# ensemble de données X de valeurs Y50# """51# nombreDeValeurs = X.shape[0]52# resultat = 053# for indice in range(0,nombreDeValeurs):54# valeurPredite = vecteurPonderation[-1] #Biais55# valeurReelle = Y[indice]56# for coef in range(0,vecteurPonderation.shape[0]-1):57# valeurPredite += vecteurPonderation[coef]*X[indice][coef]58# resultat += (valeurPredite - valeurReelle)**259# return resultat60# def mse(vecteurPonderation, X, Y):61# """62# Calcul skl la moyenne des moindres carrée (mean-square error).63# ensemble de données X de valeurs Y64# """65# resultat = rss(vecteurPonderation, X, Y)/​ X.shape[0]66# return resultat67# def rmse(vecteurPonderation, X, Y):68# resultat = math.sqrt(mse(vecteurPonderation, X, Y))69# return resultat70# def regLin(x,y):71# """72# Regression Linéaire sklearn.73# ensemble de données x de valeurs y74# """75# reg = linear_model.LinearRegression()76# reg.fit(x,y)77# result = np.concatenate([reg.coef_, [reg.intercept_]])78# return result79# def graphRegLin2d(x,y,a=-5,b=5, imprimer = False, afficherDroite = False, afficherNuageDePoints = False, afficherErreur = False, Tout = False):80# """81# Representation graphique de la régression linéaire avec biais.82# ensemble de données x de valeurs y83# a et b : abscisses min et max du segment représentant l'approximation affine.84# Les parametres booleens imprimer, afficherDroite, afficherErreur, afficherNuageDePoints permette de choisir ce que l'on affiche.85# """86# reg = regLin(x,y)87# if afficherNuageDePoints: pl.scatter(x[:, 0], y)88# if afficherDroite : pl.plot([a, b],[reg[1] + a*reg[0], reg[1] + b*reg[0]],'r--', lw=2)89# #if afficherErreur : print(mse(reg,x,y)) 90# if imprimer: pl.show()91# def graphRegLin3d(x,y,a=-5,b=5, imprimer = False, afficherDroite = False, afficherNuageDePoints = False, afficherErreur = False):92# """93# Representation graphique de la régression linéaire 3d avec biais.94# ensemble de données x de valeurs y95# Les parametres booleens imprimer, afficherDroite, afficherErreur, afficherNuageDePoints permette de choisir ce que l'on affiche.96# """97# fig = pl.figure()98# ax = fig.add_subplot(111, projection='3d')99# ax.set_xlabel('X Label')100# ax.set_ylabel('Y Label')101# ax.set_zlabel('Z Label')102# regressionLineaire = regLin(x,y)103# if afficherNuageDePoints: ax.scatter(x[:,0], x[:,1], y, s=50 )104# if afficherDroite :planEnFonctionVecteurPonderation(ax, regressionLineaire, zOrder = 2)105# #if afficherErreur : print(mse(regressionLineaire,x,y)) 106# if imprimer: pl.show()107# def graphRegLin(x,y, a=-5,b=-5,imprimer = False, afficherDroite = False, afficherNuageDePoints = False, afficherErreur = False, Tout = False):108# if Tout: imprimer, afficherDroite, afficherErreur, afficherNuageDePoints = True, True, True, True109# if x.shape[1] == 1:graphRegLin2d(x,y,a=a,b=b, imprimer = imprimer, afficherDroite = afficherDroite, afficherNuageDePoints = afficherNuageDePoints, afficherErreur = afficherErreur)110# if x.shape[1] == 2:graphRegLin3d(x,y, imprimer = imprimer, afficherDroite = afficherDroite, afficherNuageDePoints = afficherNuageDePoints, afficherErreur = afficherNuageDePoints)111# def regressionLineaireSkLearn():...

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FonctionTP2.py

Source: FonctionTP2.py Github

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1#Algorithme Régression linéaire par moindres carrés2from matplotlib.pyplot import subplot3from matplotlib import cm4from sklearn import linear_model5import numpy as np6from numpy.core.multiarray import result_type7import pylab as pl8import matplotlib.pyplot as mp9import math10## Inplementation de l'algoritme permettant de recuperer la régression à partir d'une liste de données (x_i,y_i)11def planEnFonctionVecteurPonderation(sub_plot,W, zOrder = 0):12 """13 Desine le plan normal à W dans la sous figure sub_plot.14 """15 if W.shape[0] == 3:16 a,b,d = W[0], W[1], W[-1]17 x_ = np.linspace(-2,2,10)18 y_ = np.linspace(-2,2,10)19 X_,Y_ = np.meshgrid(x_,y_)20 Z_ = d + a*X_ + b*Y_21 sub_plot.plot_surface(X_, Y_, Z_, cmap=cm.plasma, zorder = zOrder)22 pass23 elif W.shape[0] == 2:24 a,b,d = W[0], W[-1]25 x_ = np.linspace(-1,1,10)26 y_ = np.linspace(-1,1,10)27 X_,Y_ = np.meshgrid(x_,y_)28 Z_ = a*X_ + b*Y_29 sub_plot.plot_surface(X_, Y_, Z_)30 else:31 print("Le vecteur de ponderation ne comporte pas le bon nombre de paramètrd")32 pass33def rss(vecteurPonderation, X, Y):34 """35 Calcul la somme des moindres carrée (Residual sum of squares).36 ensemble de données X de valeurs Y37 """38 nombreDeValeurs = X.shape[0]39 resultat = 040 for indice in range(0,nombreDeValeurs):41 valeurPredite = vecteurPonderation[-1] #Biais42 valeurReelle = Y[indice]43 for coef in range(0,vecteurPonderation.shape[0]-1):44 valeurPredite += vecteurPonderation[coef]*X[indice][coef]45 resultat += (valeurPredite - valeurReelle)**246 return resultat47def mse(vecteurPonderation, X, Y):48 """49 Calcul la moyenne des moindres carrée (mean-square error).50 ensemble de données X de valeurs Y51 """52 resultat = rss(vecteurPonderation, X, Y)/​ X.shape[0]53 return resultat54def rmse(vecteurPonderation, X, Y):55 resultat = math.sqrt(mse(vecteurPonderation, X, Y))56 return resultat57def regLin(x,y):58 """59 Regression Linéaire avec biais.60 ensemble de données x de valeurs y61 """62 x = np.c_[x,np.ones(x.shape[0])]63 xt = np.transpose(x)64 xtx = np.dot(xt,x)65 inv = np.linalg.inv(xtx)66 xty = np.dot(xt,y)67 return np.dot(inv,xty)68def graphRegLin2d(x,y,a=-5,b=5, imprimer = False, afficherDroite = False, afficherNuageDePoints = False, afficherErreur = False, Tout = False):69 """70 Representation graphique de la régression linéaire avec biais.71 ensemble de données x de valeurs y72 a et b : abscisses min et max du segment représentant l'approximation affine.73 Les parametres booleens imprimer, afficherDroite, afficherErreur, afficherNuageDePoints permette de choisir ce que l'on affiche.74 """75 reg = regLin(x,y)76 if afficherNuageDePoints: pl.scatter(x[:, 0], y)77 if afficherDroite : pl.plot([a, b],[reg[1] + a*reg[0], reg[1] + b*reg[0]],'r--', lw=2)78 if afficherErreur : print(mse(reg,x,y)) 79 if imprimer: pl.show()80def graphRegLin3d(x,y,a=-5,b=5, imprimer = False, afficherDroite = False, afficherNuageDePoints = False, afficherErreur = False):81 """82 Representation graphique de la régression linéaire 3d avec biais.83 ensemble de données x de valeurs y84 Les parametres booleens imprimer, afficherDroite, afficherErreur, afficherNuageDePoints permette de choisir ce que l'on affiche.85 """86 fig = pl.figure()87 ax = fig.add_subplot(111, projection='3d')88 ax.set_xlabel('X Label')89 ax.set_ylabel('Y Label')90 ax.set_zlabel('Z Label')91 regressionLineaire = regLin(x,y)92 if afficherNuageDePoints: ax.scatter(x[:,0], x[:,1], y, s=50 )93 if afficherDroite :planEnFonctionVecteurPonderation(ax, regressionLineaire, zOrder = 2)94 if afficherErreur : print(mse(regressionLineaire,x,y)) 95 if imprimer: pl.show()96def graphRegLin(x,y, a=-5,b=-5,imprimer = False, afficherDroite = False, afficherNuageDePoints = False, afficherErreur = False, Tout = False):97 if Tout: imprimer, afficherDroite, afficherErreur, afficherNuageDePoints = True, True, True, True98 if x.shape[1] == 1:graphRegLin2d(x,y,a=a,b=b, imprimer = imprimer, afficherDroite = afficherDroite, afficherNuageDePoints = afficherNuageDePoints, afficherErreur = afficherErreur)...

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sect.py

Source: sect.py Github

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1from swatpyplus.sect import Section2from .comp import Composantes3from .compte_obj import CompteObjet4from .impr import Imprimer5from .impr_obj import ImprimerObjet6from .temps import Temps7class Simul(Section):8 nom = 'simulation'...

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