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小編給大家分享一下Python如何實(shí)現(xiàn)滑動(dòng)平均,相信大部分人都還不怎么了解,因此分享這篇文章給大家參考一下,希望大家閱讀完這篇文章后大有收獲,下面讓我們一起去了解一下吧!
Python中滑動(dòng)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函數(shù),但是平滑窗口必須為奇數(shù)。 # 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:原始數(shù)據(jù),NumPy 1-D array containing the data to be smoothed # 必須是1-D的,如果不是,請(qǐng)使用 np.ravel()或者np.squeeze()轉(zhuǎn)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,邊緣處理的不好 """ 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 # 輸出結(jié)果 不與原始數(shù)據(jù)等長(zhǎng),假設(shè)原數(shù)據(jù)為m,平滑步長(zhǎng)為t,則輸出數(shù)據(jù)為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 """
以上是“Python如何實(shí)現(xiàn)滑動(dòng)平均”這篇文章的所有內(nèi)容,感謝各位的閱讀!相信大家都有了一定的了解,希望分享的內(nèi)容對(duì)大家有所幫助,如果還想學(xué)習(xí)更多知識(shí),歡迎關(guān)注億速云行業(yè)資訊頻道!
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