![]() Say, you want to generate interesting test inputs for your deep learning algorithm. However, for really complex solution spaces, it can have trouble. It’s a great sampler and will work very fast on many instances. ![]() I personally have worked on one called UniGen, a guaranteed approximate probabilistic sampler, meaning that it’ll give approximately uniform samples most of the time, and we have a proof to back this up. There have been many samplers proposed in the literature. For this, I need a fast way of generating uniform samples given the constraints on the solution space. If I want to test that this function operates correctly, one way to do it is to generate 100 uniformly random inputs that don’t violate any of the constraints, run the function, and see if all is OK. the 1st parameter must be larger than the second, the 2nd parameter must be divisible by the 3rd etc. Let’s say that I have a function I want to test, but the input to the function has some real-world constraints like e.g. However, when there are constraints on the solution space, it starts to get tricky. Just pick 5 random numbers from a box and we are done! For the lotto the solution space is very easy to generate. In some cases, this is quite simple, say, for the lotto. ![]() Uniform sampling is a problem where you are given a solution space and you have to present solutions uniformly, at random. ![]()
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