Python中的滑动窗口方法
[英]sliding window approach in Python
问题描述
I have a dataframe DF, with two columns A and B shown below: 
我有一个数据框DF，下面显示了两列A和B：
A B
1 0
3 0
4 0
2 1
6 0
4 1
7 1
8 1
1 0
First part: A sliding window approach should be performed as shown below. 第一部分：应该执行滑动窗口方法，如下所示。 I need to calculate mean for column B in a sliding window of size 3 sliding by 1 position . 我需要在大小为3滑动1个位置的滑动窗口中计算B列的均值。 The mean values for each window are calculated manually and shown on the left side. 手动计算每个窗口的平均值，并在左侧显示。
A: 1 3 4 2 6 4 7 8 1
B: 0 0 0 1 0 1 1 1 0
[0 0 0] 0
[0 0 1] 0.33
[0 1 0] 0.33
[1 0 1] 0.66
[0 1 1] 0.66
[1 1 1] 1
[1 1 0] 0.66
output: 0 0.33 0.33 0.66 0.66 1 1 1 0.66
Second part :Now, for each row/coordinate in column A, all windows containing the coordinate are considered and should retain the highest mean value which gives the results as shown in column 'output'. 第二部分：现在，对于A列中的每一行/坐标，将考虑所有包含该坐标的窗口，并应保留最高平均值，该平均值给出的结果如“输出”列中所示。
Detailed explanation for second part:The first part is calculating the mean in a sliding window 3 sliding by 1 position. 第二部分的详细说明：第一部分是计算在滑动窗口3中滑动1个位置的平均值。 The second step is: For each coordinate 'i' in column A, all windows containing the coordinate 'i' should be evaluated and retain the highest mean score. 第二步是：对于列A中的每个坐标“ i”，应评估所有包含坐标“ i”的窗口并保留最高的平均得分。 For example in column A, 1 is present only in the first window, so the score for 1 is 0 (which is the mean of the first window). 例如，在A列中，仅在第一个窗口中显示1，因此1的得分为0（这是第一个窗口的平均值）。 Similarly, 2 is present in first and second window, therefore the score for 2 should be the highest among the scores of window1 and window2 ie max(0, 0.33333). 类似地，第一个和第二个窗口中存在2，因此2的分数应在window1和window2的分数中最高，即max（0，0.33333）。 Likewise 3 is present in first,second and third windows, therefore score for 3 is max of the scores of first three windows ie max(0,0.333333,0.3333333). 同样，在第一，第二和第三窗口中存在3，因此3的分数是前三个窗口的分数中的最大值，即max（0,0.333333,0.3333333）。 4 is present in second,third and fourth windows, therefore score for 4 is max of the scores of those windows ie max(0.333333,0.3333333,0.666667)and so on.. 4位于第二，第三和第四窗口中，因此4的分数是那些窗口的分数的最大值，即max（0.333333、0.3333333、0.666667），依此类推。
I need to obtain the output as shown above. 我需要获得如上所述的输出。 The output should like: 输出应为：
A B Output
1 0 0
3 0 0.33
4 0 0.33
2 1 0.66
6 0 0.66
4 1 1
7 1 1
8 1 1
1 0 0.66
Any help in python would be highly appreciated? python中的任何帮助将不胜感激？
1 个解决方案
解决方案1
1 2013-04-12 17:14:25
For the first part, using numpy : 对于第一部分，使用numpy ：
WS = 3
B = numpy.array([0,0,0,1,0,1,1,1,0])
filt = numpy.ones(WS) / WS
mean = numpy.convolve(B, filt, 'valid')
For the second part: 对于第二部分：
paddedmean = numpy.zeros(mean.size + 2 * (WS - 1))
paddedmean[WS-1:-(WS-1)] = mean
output = [numpy.max(paddedmean[i:i+WS]) for i in range(mean.size+WS-1)]
But what is A used for??? 但是A是什么？
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