Let me introduce what I need by giving an example which is not working:
n-sphere with radius 1 and respective hypercube with edges of length 2. The ratio reaches already in 10th dimension a level of less than 1%.
My question essentially is whether a geometric object G with following features exists:
The hyper-cube containing G is canonically defined for all dimensions with edges of length L and center is the 0.
G is contained in above hyper-cube for all dimensions.
The ratio of the G's volume and the hyper-cubes volume is constant over all dimensions.
Calculating whether a point chosen from the hyper-cube uniformly at random is contained in G is canonical. (in other words: the calculation can be written down as a single formula depending on the number of dimensions and of course L and the point)
Any path along the surface of G is differentiable.
The volume ratio should be either adjustable or between .2 and .8.
(point 5 is maybe not correctly formulated. I want to make sure that G is not "edgy"/"weird" (sorry for these terms). Maybe it is the same as when I request G to be Riemann integrable)
I need such an object to generate a sample data set from an arbitrary dimensional space to benchmark performance of SVMs (and other ML algorithms) over dimensionality of input space.

