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How to use imprimer method in SeleniumBase

Best Python code snippet using SeleniumBase

FonctionTP_sklearn.py

1#Fonction utilisÃ©es 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 rÃ©gression Ã  partir d'une liste de donnÃ©es (x_i,y_i)12def prediction(model, ensemble):13    """14    Renvoie un ensemble de valeurs prÃ©dites pour l'ensemble donnÃ© par le model donnÃ©.15    """16    result = model.predict(ensemble)17    return result18def erreurSkl(valeursReelles , valeursPredites):19    """20    Renvoie l'erreur mse du model Ã©tant donnÃ©s un ensemble de valeurs prÃ©dites et un ensemble de valeur rÃ©elles.21    """22    result = mean_squared_error(valeursReelles , valeursPredites)23    return result24# def planEnFonctionVecteurPonderation(sub_plot,W, zOrder = 0):25#     """26#     Desine le plan normal Ã  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 paramÃ¨trd")45#     pass46# def rss(vecteurPonderation, X, Y):47#     """48#     Calcul la somme des moindres carrÃ©e (Residual sum of squares).49#     ensemble de donnÃ©es 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 carrÃ©e (mean-square error).63#     ensemble de donnÃ©es 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 LinÃ©aire sklearn.73#     ensemble de donnÃ©es 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 rÃ©gression linÃ©aire avec biais.82#     ensemble de donnÃ©es x de valeurs y83#     a et b : abscisses min et max du segment reprÃ©sentant 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 rÃ©gression linÃ©aire 3d avec biais.94#     ensemble de donnÃ©es 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():...

FonctionTP2.py

Source: FonctionTP2.py

1#Algorithme RÃ©gression linÃ©aire par moindres carrÃ©s2from 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 rÃ©gression Ã  partir d'une liste de donnÃ©es (x_i,y_i)11def planEnFonctionVecteurPonderation(sub_plot,W, zOrder = 0):12    """13    Desine le plan normal Ã  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 paramÃ¨trd")32    pass33def rss(vecteurPonderation, X, Y):34    """35    Calcul la somme des moindres carrÃ©e (Residual sum of squares).36    ensemble de donnÃ©es 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 carrÃ©e (mean-square error).50    ensemble de donnÃ©es 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 LinÃ©aire avec biais.60    ensemble de donnÃ©es 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 rÃ©gression linÃ©aire avec biais.71    ensemble de donnÃ©es x de valeurs y72    a et b : abscisses min et max du segment reprÃ©sentant 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 rÃ©gression linÃ©aire 3d avec biais.83    ensemble de donnÃ©es 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)...

sect.py

Source: sect.py

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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