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Initially, neural network based evaluation functions generally consisted of one neural network for the entire evaluation function, with input features selected from the board and whose output is an integer, normalized to the centipawn scale so that a value of 100 is roughly equivalent to a material advantage of a pawn.
The solution with the function value can be found after 325 function evaluations. Using the Nelder–Mead method from starting point x 0 = ( − 1 , 1 ) {\displaystyle x_{0}=(-1,1)} with a regular initial simplex a minimum is found with function value 1.36 ⋅ 10 − 10 {\displaystyle 1.36\cdot 10^{-10}} after 185 function evaluations.
Thomae's function: is a function that is continuous at all irrational numbers and discontinuous at all rational numbers. It is also a modification of Dirichlet function and sometimes called Riemann function. Kronecker delta function: is a function of two variables, usually integers, which is 1 if they are equal, and 0 otherwise.
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A modular function is a function that is invariant with respect to the modular group, but without the condition that it be holomorphic in the upper half-plane (among other requirements). Instead, modular functions are meromorphic: they are holomorphic on the complement of a set of isolated points, which are poles of the function.
The practical motivation for partial application is that very often the functions obtained by supplying some but not all of the arguments to a function are useful; for example, many languages have a function or operator similar to plus_one. Partial application makes it easy to define these functions, for example by creating a function that ...
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When the function is evaluated for a given value, as in f(3): or, y = f(3) = 3 + 2 = 5, 3 is the actual parameter (the argument) for evaluation by the defined function; it is a given value (actual value) that is substituted for the formal parameter of the defined function.