import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import adaline as ad

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


fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8,4))

ada1 = ad.AdalineGD(n_iter=10, eta=0.01).fit(X, y).fit(X,y)
ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(Sum-squared-error)')
ax[0].set_title('Adaline - Learning rate 0.01')
ada2 = ad.AdalineGD(n_iter=10, eta=0.0001).fit(X, y).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.AdalineGD(n_iter=15, eta=0.01)
ada.fit(X_std, y)
ad.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()
# plt.savefig('./adaline_2.png', dpi=300)
plt.show()

plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Sum-squared-error')

plt.tight_layout()
# plt.savefig('./adaline_3.png', dpi=300)
plt.show()
