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- from matplotlib.colors import ListedColormap
- import matplotlib.pyplot as plt
- import warnings
- def versiontuple(v):
- return tuple(map(int, (v.split("."))))
- def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
- # konfiguruje generator znaczników i mapę kolorów
- markers = ('s', 'x', 'o', '^', 'v')
- colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
- cmap = ListedColormap(colors[:len(np.unique(y))])
- # rysuje wykres powierzchni decyzyjnej
- 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.reshape(xx1.shape)
- plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
- plt.xlim(xx1.min(), xx1.max())
- plt.ylim(xx2.min(), xx2.max())
- # rysuje wykres wszystkich próbek
- 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)
- # zaznacza próbki testowe
- if test_idx:
- if not versiontuple(np.__version__) >= versiontuple('1.9.0'):
- X_test, y_test = X[list(test_idx), :], y[list(test_idx)]
- warnings.warn('Zaktualizuj bibliotekę NumPy do wersji 1.9.0 lub nowszej')
- else:
- X_test, y_test = X[test_idx, :], y[test_idx]
- plt.scatter(X_test[:, 0],
- X_test[:, 1],
- c='',
- alpha=1.0,
- linewidths=1,
- marker='o',
- edgecolors='k',
- s=80, label='Zestaw testowy')
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