import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model

x = np.arange(0,20).reshape(-1, 1)  # reshape vectors into 1d array for sklearn
y = np.array([0]*10 + [1]*10).reshape(-1, 1)

reg = linear_model.LinearRegression()
reg.fit(x, y)

logit = linear_model.LogisticRegression(penalty='none')
logit.fit(x, y)

plt.rcParams.update({'font.size': 18})

plt.subplot(1, 2, 1)
plt.scatter(x, y, color='b')
plt.plot(x, reg.predict(x), color='k')
plt.title('linear regression')
plt.legend(['regression line', 'original data'])
plt.xticks(())
plt.show()

plt.subplot(1, 2, 2)
plt.scatter(x, y, color='b')
plt.plot(x, logit.predict(x), color='k')
plt.title('logistic regression')
plt.legend(['logit line', 'original data'])
plt.xticks(())
plt.show()