Weakly defined Boolean functions play important role in Data Engineering. In Pattern Recognition they introduce the Learning Set models. Complexity of the Reduced Disjunctive Normal Form of these functions is shown equivalent to the Second Kind Stirling combinatorial numbers. Alternatively this gives the complexity of Logic Separation algorithms. Geometrical structure of these functions is described in terms of asymptotic distribution of cubes and spheres.