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

n_samples = 50
X = np.zeros((n_samples,2))
y = np.zeros((n_samples,2))
X[:,0] = np.array(list(range(n_samples)))
y[:,0] = 5+X[:,0]+np.random.rand(n_samples)*20

# this is our chosen model
linear_regression = linear_model.LinearRegression()  
linear_regression.fit(X, y) # train the model
w = linear_regression.coef_
b = linear_regression.intercept_
print(w[0][0])
print(b[0])

# y_predicted = []
# for i in range(len(X)):
#     y_predicted.append(X[i,0]*w[0]+b)
    
y_predicted = linear_regression.predict(X)

plt.plot(X, y_predicted, 'k-')
plt.plot(X, y, 'yo')
plt.show()

