y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
#
# 5
#
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 = AdalineGD(n_iter=15, eta=0.01)
ada.fit(X_std, y)
plot_decision_regions(X_std, y, classifier=ada)
plt.title('Adaline - Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)
ml02_plot_url5 = b64encode(img.getvalue()).decode('ascii')
ppn = 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 updates')
img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)
ml02_plot_url2 = b64encode(img.getvalue()).decode('ascii')
#
# 3
#
plot_decision_regions(X, y, classifier=ppn)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
img = BytesIO()
plt.savefig(img, dpi=300)
plt.close()
img.seek(0)
ml02_plot_url3 = b64encode(img.getvalue()).decode('ascii')