Remove overprotective measures from samplers

[jnsiemer]

Description

This PR removes the following "overprotective" measures from samplers...

• uniform sampling now also works over intervals of size 1, which will always output this single element
• discrete Gaussian sampling adapts the change from uniform sampling (just some changes on the doc-comments)
• binomial sampling now also works for 0 tries, i.e. always with output 0, which is well-defined

Furthermore, this PR implements the more efficient discrete Gaussian sampler (for sampling with static Gaussian parameters across all entries) for matrices over polynomials, i.e.

• MatPolyOverZ::sample_discrete_gauss
• MatPolynomialRingZq::sample_discrete_gauss

Testing

• I added basic working examples (possibly in doc-comment)
• I added tests for large (pointer representation) values
• I triggered all possible errors in my test in every possible way
• I included tests for all reasonable edge cases

Checklist:

• I have performed a self-review of my own code
  • The code provides good readability and maintainability s.t. it fulfills best practices like talking code, modularity, ...
    • The chosen implementation is not more complex than it has to be
  • My code should work as intended and no side effects occur (e.g. memory leaks)
  • The doc comments fit our style guide

[Marvin-Beckmann]

looks good