Is there an efficient way to calculate hash for objects

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Yoav Romach
Yoav Romach 2020 年 4 月 27 日
コメント済み: Yoav Romach 2020 年 4 月 28 日
Hey,
I have objects ("Sequence") on which I need to do some heavy calculations, the result is another object of the same kind (a different, fixed, "Sequence").
These calculations are often repeated, so I want to save the results in a Map, such that they will only be done once (for each Sequence).
The problem: The keys for the map have to be numbers or strings...
Is there any Efficient way to calculate some sort of unique Hash for an object?
I've seen Datahash, but it seems to be for structs...
If it matters, "Sequence" contains only a single property: a cell array of "Pulse" (another object). "Pulse" has several primitive properties and a struct.
Small clarification, I need the hash to be the same for different handles if internal structs are the same (same as isequal, and not as ==).
For example:
a = Sequence() % Default constructor
b = Sequence() % Default constructor
Hash(a) == Hash(b) % Should be true
Of course, I can calculate some sort of hash myself, but I wonder if there is some other way which might be more efficient...

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Ameer Hamza
Ameer Hamza 2020 年 4 月 27 日
Have a look at the memorized function: https://www.mathworks.com/help/matlab/ref/memoizedfunction.html. This might be helpful. The submission by Jan, you linked in your question, will also work for objects, not just structs. You may also want to have a look at jsonencode(): https://www.mathworks.com/help/matlab/ref/jsonencode.html
  5 件のコメント
Ameer Hamza
Ameer Hamza 2020 年 4 月 28 日
Ah!! The undocumented MATLAB. Glad that you found a solution. If you already have the bitstream then you may avoid Datahash. You can directly use md5 hashing function provided by MATLAB: https://www.mathworks.com/help/releases/R2020a/rptgen/ug/mlreportgen.utils.hash.html, but I guess that requires a separate toolbox. You can select whatever option is more convenient.
Yoav Romach
Yoav Romach 2020 年 4 月 28 日
Nice!
Unfortunately, I'm currently using R2017b, and this function was only introduced in 2018a...
So I'll stick with DataHash for now.

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