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import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

def plot_linear_regression():

a = 0.5

b = 1.0

# x from 0 to 10

x = 30 * np.random.random(20)

# y = a*x + b with noise

y = a * x + b + np.random.normal(size=x.shape)

# create a linear regression classifier

clf = LinearRegression()

clf.fit(x[:, None], y)

# predict y from the data

x_new = np.linspace(0, 30, 100)

y_new = clf.predict(x_new[:, None])

# plot the results

ax = plt.axes()

ax.scatter(x, y)

ax.plot(x_new, y_new)

ax.set_xlabel('x')

ax.set_ylabel('y')

ax.axis('tight')

if __name__ == '__main__':

plot_linear_regression()

plt.show()