Python中滑动平均算法（MovingAverage）方案：#!/usr/bin/envpython#-*-coding:utf-8-*-importnumpyasnp#等同于M
Python中滑动平均算法（Moving Average）方案：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
# 等同于MATLAB中的smooth函数，但是平滑窗口必须为奇数。
# yy = smooth(y) smooths the data in the column vector y ..
# The first few elements of yy are given by
# yy(1) = y(1)
# yy(2) = (y(1) + y(2) + y(3))/3
# yy(3) = (y(1) + y(2) + y(3) + y(4) + y(5))/5
# yy(4) = (y(2) + y(3) + y(4) + y(5) + y(6))/5
# ...
def smooth(a,WSZ):
# a:原始数据，NumPy 1-D array containing the data to be smoothed
# 必须是1-D的，如果不是，请使用 np.ravel()或者np.squeeze()转化
# WSZ: smoothing window size needs, which must be odd number,
# as in the original MATLAB implementation
out0 = np.convolve(a,np.ones(WSZ,dtype=int),'valid')/WSZ
r = np.arange(1,WSZ-1,2)
start = np.cumsum(a[:WSZ-1])[::2]/r
stop = (np.cumsum(a[:-WSZ:-1])[::2]/r)[::-1]
return np.concatenate(( start , out0, stop ))
# another one，边缘处理的不好
"""
def movingaverage(data, window_size):
window = np.ones(int(window_size))/float(window_size)
return np.convolve(data, window, 'same')
"""
# another one，速度更快
# 输出结果 不与原始数据等长，假设原数据为m，平滑步长为t，则输出数据为m-t+1
"""
def movingaverage(data, window_size):
cumsum_vec = np.cumsum(np.insert(data, 0, 0))
ma_vec = (cumsum_vec[window_size:] - cumsum_vec[:-window_size]) / window_size
return ma_vec
"""
以上这篇Python实现滑动平均(Moving Average)的例子就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
Python 滑动平均 Moving Average