Liner Regression

本文主要是介绍Liner Regression，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

import numpy as np

# y = w*x + b
def compute_error_for_line_given_points(b,w,points):
    totalError=0
    for i in range(0,len(points)):
        x=points[i,0]
        y=points[i,1]
        totalError +=(y-(w*x+b))**2
    return totalError / float(len(points))

def step_gradient(b_current,w_current,points,learningRate):
    b_gradient=0
    w_gradient=0
    N=float(len(points))
    for i in range(0,len(points)):
        x=points[i,0]
        y=points[i,1]
        b_gradient+=-(2/N)*(y-((w_current*x)+b_current))
        w_gradient+=-(2/N)*x*(y-((w_current*x))+b_current)
    new_b = b_current-(learningRate*b_gradient)
    new_w = w_current-(learningRate*w_gradient)
    return [new_b,new_w]

def gradient_descent_runnder(points,starting_b,starting_w,learning_rate,num_iterations):
    b=starting_b
    w=starting_w
    for i in range(num_iterations):
        b,w=step_gradient(b,w,np.array(points),learning_rate)
    return [b,w]

def run():
    points=np.genfromtxt("data.csv",delimiter=',')
    learning_rate=0.0001
    initial_b=0
    initial_w=0
    num_iterations=1000
    print("Starting gradient descent at b={0},w={1},error={2}"
          .format(initial_b,initial_w,
                  compute_error_for_line_given_points(initial_b,initial_w,points))
          )
    print('Running.....')
    [b,w]=gradient_descent_runnder(points,initial_b,initial_w,learning_rate,num_iterations)
    print('After {0} iterations b={1},w={2},error={3}'
          .format(num_iterations,b,w,
                 compute_error_for_line_given_points(b,w,points))
          )

if __name__=='__main__':
    run()

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