| import numpy as np |
| import matplotlib.pyplot as plt |
| from matplotlib.colors import ListedColormap |
| from matplotlib.font_manager import FontProperties |
| from sklearn import datasets |
| from sklearn.linear_model import LogisticRegression |
| %matplotlib inline |
| font = FontProperties(fname='/Library/Fonts/Heiti.ttc') |
| iris_data = datasets.load_iris() |
| X = iris_data.data[:, [2, 3]] |
| y = iris_data.target |
| label_list = ['山鸢尾', '杂色鸢尾', '维吉尼亚鸢尾'] |
| def plot_decision_regions(X, y, classifier=None): |
| marker_list = ['o', 'x', 's'] |
| color_list = ['r', 'b', 'g'] |
| cmap = ListedColormap(color_list[:len(np.unique(y))]) |
| |
| x1_min, x1_max = X[:, 0].min()-1, X[:, 0].max()+1 |
| x2_min, x2_max = X[:, 1].min()-1, X[:, 1].max()+1 |
| t1 = np.linspace(x1_min, x1_max, 666) |
| t2 = np.linspace(x2_min, x2_max, 666) |
| |
| x1, x2 = np.meshgrid(t1, t2) |
| y_hat = classifier.predict(np.array([x1.ravel(), x2.ravel()]).T) |
| y_hat = y_hat.reshape(x1.shape) |
| plt.contourf(x1, x2, y_hat, alpha=0.2, cmap=cmap) |
| plt.xlim(x1_min, x1_max) |
| plt.ylim(x2_min, x2_max) |
| |
| for ind, clas in enumerate(np.unique(y)): |
| plt.scatter(X[y == clas, 0], X[y == clas, 1], alpha=0.8, s=50, |
| c=color_list[ind], marker=marker_list[ind], label=label_list[clas]) |
| |
| |
| |
| lr = LogisticRegression(C=100, random_state=1, |
| solver='lbfgs', multi_class='ovr') |
| lr.fit(X, y) |
| LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True, |
| intercept_scaling=1, max_iter=100, multi_class='ovr', |
| n_jobs=None, penalty='l2', random_state=1, solver='lbfgs', |
| tol=0.0001, verbose=0, warm_start=False) |