实现简单的决策树（先找出合适的决策参量，再估计错误率）
import sys
import numpy as np
import math
from random import *
def read_input(path):
x = []
y = []
for line in open(path).readlines():
items = line.strip().split(' ')
tmp_x = []
for i in range(0, len(items)-1):
tmp_x.append(float(items[i]))
x.append(tmp_x)
y.append(float(items[-1]))
return np.array(x), np.array(y)
def calculate_Ein(x, y):
thetas = np.array([float("-inf")]+[(x[i]+x[i+1])/2 for i in range(0, x.shape[0]-1)]+[float("inf")])
target_theta = 0.0
sign = 1
Ein = x.shape[0]
for theta in thetas:
y_positive = np.where(x>theta, 1, -1)
y_negative = np.where(x<theta, 1, -1)
err_positive = sum(y_positive != y)
err_negative = sum(y_negative != y)
if err_positive > err_negative:
if Ein > err_negative:
Ein = err_negative
sign = -1
target_theta = theta
else:
if Ein > err_positive:
Ein = err_positive
sign = 1
target_theta = theta
return Ein, target_theta, sign
if __name__ == '__main__':
x, y = read_input('train.txt')
x_test, y_test = read_input('test.txt')
Ein = x.shape[0]
theta = 0.0
sign = 1
index = 0
for i in range(0, x.shape[1]):
cur_x = x[:,i]
cur_data = np.transpose(np.array([cur_x, y]))
cur_data = cur_data[np.argsort(cur_data[:,0])]
cur_Ein, cur_theta, cur_sign = calculate_Ein(cur_data[:,0], cur_data[:,1])
if cur_Ein < Ein:
Ein = cur_Ein
theta = cur_theta
sign = cur_sign
index = i
print(Ein/x.shape[0])
# test
x_test = x_test[:, index]
if sign == 1:
y_predict = np.where(x_test > theta, 1.0, -1.0)
else:
y_predict = np.where(x_test < theta, 1.0, -1.0)
Eout = sum(y_predict != y_test)
print(Eout/x_test.shape[0])
注意：
- python中把数据每一行分开后得到的是一系列字符串，要先把它转化成浮点数再后续计算。
- np.array([cur_x, y])两个列向量拼接后得到了两个行向量，要转置变成想要的格式。
references:
http://www.cnblogs.com/xbf9xbf/p/4595990.html
