Bias This calculates Ken Perlin's Bias function. Given an input value between 0 and 1, it calculates an output value which is also between 0 and 1 according to: y(x) = x^(log(B)/log(0.5)) where the input value x and bias B correspond to Value_1 and Value_2 on the Math node. If B=0.5, then y(x)=x. Values of B less than 0.5 push the output toward smaller values, while values of B greater than 0.5 push the output toward larger values. An interesting artifact of this function's definition is that Bias(A, B) is the same as Bias(B, A).

Gain This calculates Ken Perlin's Gain function. Given an input value between 0 and 1, it calculates an output value which is also between 0 and 1 according to: y(x) = Bias(2x, 1-G)/2 if x<0.5 1-Bias(2-2x, 1-G)/2 if x>0.5 where the input value x and gain G correspond to Value_1 and Value_2 on the node, and Bias(x, B) is the Bias function described above. If G=0.5, then y(x)=x. Values of G less than 0.5 smooth the input by pushing the output toward 0.5, while values of G greater than 0.5 sharpen the input by pushing the output toward 0 or 1, away from .5.

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