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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
def plot_decision_regions(X, y, classifier, title=None, test_idx=None, resolution=0.02):
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[: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
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z[Z> .5] = 1
Z[Z<=.5] = 0
Z = Z.reshape(xx1.shape)
plt.figure(figsize=(8, 8))
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
if title:
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8,
c=cmap(idx), marker=markers[idx], label=cl)
if test_idx:
X_t, y_t = X[test_idx, :], y[test_idx]
plt.scatter(X_t[:, 0], X_t[:, 1], c='black', alpha=1.0,
linewidths=2, marker='|', s=55, label='test set')
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