machine_learn/linear/gradient_linear.py

#!/usr/bin/python
# -*- coding: UTF-8 -*-
'''
最简单的mse
'''
import sys
reload(sys)
sys.setdefaultencoding('utf-8')

import random
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from draw import draw_util


def read_data(path):
    with open(path) as f :
        lines=f.readlines()
    lines=[eval(line.strip()) for line in lines]
    return lines


def cal_step_pow(data, w, b=3):
    p = [(w*item[0][0] + b - item[1][0])*item[0][0]*2  for item in data]
    sum_p = sum(p)
    return sum_p/len(data)

def cal_mse(data, w, b=3):
    sum_p = sum([(w * item[0][0] + b - item[1][0]) * (w * item[0][0] + b - item[1][0]) for item in data])
    return sum_p / len(data)

def train():
    train_data = read_data('train_data')
    w = random.uniform(-50, 50)
    for i in range(10):
        step = cal_step_pow(train_data, w)*0.01
        mse = cal_mse(train_data, w)
        print w, step, mse

        w = w - step


if __name__ == '__main__':
    train()