Sliding window program in matlab

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Asked 8 years, 11 months ago. Active 5 years, 8 months ago. Viewed 14k times. Binja Binja 3 3 gold badges 3 3 silver badges 12 12 bronze badges. Add a comment. Active Oldest Votes. Sridutt Sridutt 3 3 silver badges 14 14 bronze badges. Can someone confirm? Will Faithfull Will Faithfull 1, 13 13 silver badges 20 20 bronze badges.

I think u got wrong my questions. I have mean and variance values. I just want to plot them inside the sliding windows — Binja. Error: File: SampEn. This statement is not inside any function. My goal is for it to return a row vector with calculation of SampEn for each window length; the row vector should have elements if the window length is and it moves forward by 1 each time up to Please do not insert my code inside your function!

It will not work that way, and will probably cause recursion errors. I intend that the loop in my code calls your function in every iteration of the loop, each time with a different window of your data, and stores the output. Salutations to you, thank you for helping me figure that out. It works beautifully now! Thank you. As always, my pleasure! Star Strider if i want to use your code to call my function every 25th observation instead of every other observation, how should i edit your code?

SampEn data and so on, moving 25 elements forward each time until I reach the end of that data set. Star Strider on 9 May If I remember correctly how it works no promises , inserting a step in the for vector may do what you want:. Star Strider thanks a lot! My pleasure! I would appreciate a Vote for my Answer. More Answers 0. See Also. Tags sliding window. Start Hunting!

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However, the right elements to access now are 1, 2, 3 for the first row, then 3, 4, 5 for the second row then 5, 6, 7 for the third row. For each row, we now offset the base indices by 2 instead of 1 now. Therefore the second row we add 2 to each base index, then we add 4 to each base index for the third row and so on. In general, the base indices are created using a vector 1:W and the offset indices are created using a vector 0:X:numel T -W.

The subtraction of W is required so that we don't go out of bounds when sampling the signal as per the requirement. To create these indices that we just talked about, bsxfun handles this for us. We create a row vector of 1:W which corresponds to the base indices and a column vector of 0:X:numel T -W.

Note that the first offset starts at 0, then we increment by X amount to ensure that the correct centre is calculated to place our base indices at. We stop until we hit numel T -W elements, which is the condition you have stated. By using bsxfun , two temporary 2D matrices are created where the row vector is duplicated for as many rows as there are rows as there are in the column vector and the column vector is duplicated for as many columns as there are in the row vector.

Once you add these two matrices together, you get the resulting index matrix. You can verify for yourself that these indices correspond to the correct elements in T to create your desired matrix in this case.

Based on rayryeng's answer I wrote a function that does exactly this, plus some additional functionality. It's designed for generating indices for autoregression on a univariate time series. It can easily be used for the multivariate case by simply using the same indices and concatenating the referenced data.

It returns the indices to the predictor variable X as per your request and for the regressors y as well. In addition you have the option of applying a 'mask' to the predictor variables X while sliding the window.

You can also change the prediction horizon - how many steps into the future the indices for y are. How are we doing? Please help us improve Stack Overflow. Take our short survey. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow.