($\aleph_0$). Therefore probability for randomly chosen real number to be equivalent to some formula is zero. We must therefore restrict to numbers with finite decimal expansion. This is also practical approach, due to widespread of floating-point hardware, with double precision [@double] being *de facto* standard. However, in this case we encounter infinity from the other side. Assuming floating-point constant is a truncated real number[^1], we can produce arbitrary number of formulas which numerically differ only at more distant decimal places: $1.8226300001, 1.8226300000009 ,1.822630000100017$ etc. Fortunately, one still can ask which one of these formulas has *lowest* Kolmogorov complexity [@complexity]. But to do this, we must specify ”programing language” first. This lead to the following formulation of the numerical constant recognition problem. Imagine a person with hand-held scientific calculator, who secretly push a sequence of buttons, and print out the decimal/numerical result. We ask: could this process be reversed? In other words, given numerical result, are we able to recover sequence of some particular calculator buttons? Since every meaningful combination of buttons is equivalent to some explicit, closed-form [@closed], mathematical formula, above thought experiment becomes practical formulation of the constant recognition problem. In real world implementation, human and calculator are replaced 