How can I control the output of a hash function to output to specific data according to similarity? I do not know if the question lies exactly in that field but I'll give it a try unless rejection.

I want to study methods of applying LSH functions to feet in a specific area of digest values. Briefly i would like to control the results of hash functions such as "similar" inputs (similarity is defined by an LSH algorithm given a distance metric – i.e:hamming distance) fall to a specific value. That is, I want to control the output of LSH to result in a specific range of values.
Well, if the question is 'how do we select inputs to a cryptographical hash function so that the outputs are within a specific range', well, you're pretty much limited to:


Rejection methods -- that is, you hash the input with some salt, and if the resulting hash value isn't in the range you want, you keep on trying different salt values until it is.  For example, if you want the MSBit of the hash to be zero, you keep on trying different salt values until you find a hash with has an MSBit of zero.
Postprocessing methods -- that is, you hash the input as usual, and then map it into the range you want.  For example,if you want the MSBit of the hash to be zero, you simply take the result of the crpytographical hash function, and set the MSBit to zero.


If you're looking for a more efficient method of selecting hash inputs to constrict the hash output, well, they're not known to exist for cryptographical hash functions.  In fact, if there was a large, easily computable subset of inputs that generated a biased hash output, that would imply a cryptographical weakness of the hash function.  For example, to find a collision in the hash function, one could just take inputs from the subset, and hash those; if there was a bias in the hash outputs, one would find a collision with probability $0.5$ with a number of hashes strictly less than $1.17741 \cdot 2^{N/2}$ attempts (where $N$ is the size of the hash function); this is a weakness (although a small one if the bias is small, or it takes a large amount of computation to find elements in the subset).