Python|机器学习-一元线性回归

def model(a,b,x):
  # x is vector，a and b are the common number.
  return a*x+b

def cost(a,b,x,y):
    # x is argu, y is actual result.    
    n=len(x)
    return np.square(y-a*x-b).sum()/n

def optimize(a,b,x,y):
    theta = 1e-1  # settle the step as 0.1
    n=len(x)
    y_hat = model(a,b,x)
    # compute forecast values
    da = ((y_hat-y)*x).sum()/n
    db = (y_hat-y).sum()/n
    a = a - theta*da
    b = b - theta*db
    return a, b

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

x = [1,2,4,5,6,6,7,9]
x = np.reshape(x,newshape=(8,1))
y =  [10,9,9,9,7,5,3,1]
y = np.reshape(y,newshape=(8,1))
# create an instance of LinearRegression model
lr = LinearRegression()
# train model
lr.fit(x,y)
lr.score(x,y)
# compute y_hat
y_hat = lr.predict(x)
# show the result
plt.scatter(x,y)
plt.plot(x, y_hat)
plt.show()