房价预测（HackerRank）

从今天开始要多做一些关于机器学习方面的竞赛题目，题目来源主要是Hackerrank和Kaggle。链接如下

 

Hackerrank：https://www.hackerrank.com/

Kaggle：https://www.kaggle.com/

 

在Hackerrank中提交源代码，这就使得很多库都需要自己写，限制比较多。而Kaggle只需要提交数据，所以随便怎么搞都行。现在来讲第一道题，房价预测，这是Andrew Ng课程里的比较经典的例子。题目描述如下

 

题目：https://www.hackerrank.com/challenges/predicting-house-prices

 

分析：比较简单，用梯度下降法即可。

 

代码：

#coding:utf-8

class Data:
	def __init__(self):
		self.x = []
		self.y = 0.0

def WX(d, w):
	ans = 0.0
	for i in range(0, len(w)):
		ans += w[i] * d.x[i]
	return ans

def Gradient(d, w, alpha):
	for i in range(0, len(w)):
		tmp = 0.0
		for j in range(0, len(d)):
			tmp += alpha * d[j].x[i] * (WX(d[j], w) - d[j].y)
		w[i] -= tmp

def getValues(d, w):
	res = 0.0
	for i in range(0, len(d)):
		tmp = WX(d[i], w)
		res += (d[i].y - tmp) * (d[i].y - tmp)
	return res

def Iterator(d, w):
	alpha = 0.005
	delta = 0.000001
	oldVal = getValues(d, w)
	Gradient(d, w, alpha)
	newVal = getValues(d, w)
	while abs(oldVal - newVal) > delta:
		oldVal = newVal
		Gradient(d, w, alpha)
		newVal = getValues(d, w)

def main():
	while True:
		try:
			d = []
			w = []
			F, N = map(int, raw_input().split())
			for i in range(0, N):
				t = Data()
				t.x = map(float, raw_input().split())
				t.x.insert(0, 1.0)
				t.y = t.x.pop()
				d.append(t)
			for i in range(0, F + 1):
				w.append(0)
			Iterator(d, w)
			N = int(raw_input())
			for i in range(0, N):
				t = Data()
				t.x = map(float, raw_input().split())
				t.x.insert(0, 1.0)
				print '%.2f'% WX(t, w)
		except EOFError:
			break

if __name__ == '__main__':
	main()