我想訓(xùn)練邏輯回歸模型，然后創(chuàng)建一個(gè)以特定方式顯示邊界線的圖。到目前為止我的工作import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LogisticRegressionfrom sklearn import datasetsfrom matplotlib.colors import ListedColormapcmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])# import some data to play withiris = datasets.load_iris()X = iris.data[:, :2]  # we only take the first two features.Y = iris.targetlogreg = LogisticRegression(C=1e5)# Create an instance of Logistic Regression Classifier and fit the data.logreg.fit(X, Y)# Plot the decision boundary. For that, we will assign a color to each# point in the mesh [x_min, x_max]x[y_min, y_max].x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5h = .02  # step size in the meshxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])# Put the result into a color plotZ = Z.reshape(xx.shape)plt.figure(1, figsize=(4, 3))plt.pcolormesh(xx, yy, Z, cmap=cmap_light)# Plot also the training pointsplt.scatter(X[:, 0], X[:,1], c=Y, marker='x',edgecolors='k', cmap=cmap_bold)plt.xlabel('Sepal length'),plt.ylabel('Sepal width')plt.xlim(xx.min(), xx.max())plt.ylim(yy.min(), yy.max())plt.xticks(())plt.yticks(())plt.show()但是我發(fā)現(xiàn)它非常難以閱讀。我想在左上角為每個(gè)分類和圖例添加其他標(biāo)記。就像下圖所示：你知道我該如何改變嗎？我玩過marker ='s', marker='x'，但這些改變了散點(diǎn)圖上的所有點(diǎn)，而不是一個(gè)特定的分類。