import perceptron as per import visualize df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None) y = df.iloc[0:100, 4].values y = np.where(y == 'Iris-setosa', -1, 1) X = df.iloc[0:100, [0,2]].values plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa') plt.scatter(X[50:100, 0], X[50:100, 1], color='blue', marker='x', label='versicolor') plt.xlabel('petal length') plt.ylabel('sepal length') plt.legend(loc='upper left') plt.show() ppn = per.Perceptron(eta=0.1, n_iter=10) ppn.fit(X, y) plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o') plt.xlabel('Epochs') plt.ylabel('Number of misclassifications') plt.show() visualize.plot_decision_regions(plt, X, y, classifier=ppn) plt.xlabel('sepal length [cm]') plt.ylabel('petal length [cm]') plt.legend(loc='upper left') plt.show()
ax[0].set_title('Adaline learning rate 0.01') ada2 = ad.AdalineGD(n_iter=10, eta=0.0001).fit(X, y) ax[1].plot(range(1, len(ada2.cost_) + 1), np.log10(ada2.cost_), marker='o') ax[1].set_xlabel('Epochs') ax[1].set_ylabel('log(Sum-squared-error)') ax[1].set_title('Adaline learning rate 0.0001') plt.show() """ X_std = np.copy(X) X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std() X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std() ada = ad.AdalineSGD(n_iter=15, eta=0.01, random_state=1) ada.fit(X_std, y) visualize.plot_decision_regions(plt, X_std, y, classifier=ada) plt.title('Adaline stochastic gradient descent') plt.xlabel('sepal length standardized') plt.ylabel('petal length standardized') plt.legend(loc='upper left') plt.show() plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o') plt.xlabel('Epochs') plt.ylabel('Sum-squared-errors') plt.show()