Python实现滑动平均(Moving Average)的例子
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 """
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