fix: remove unneeded trait bounds

also adds a comment demonstrating success of unwrap

src/distribution/multinomial.rs

@@ -188,11 +188,10 @@ where
 fn sample_generic<D, R, T>(p: &[f64], n: u64, dim: D, rng: &mut R) -> OVector<T, D>
 where
     D: Dim,
-    nalgebra::DefaultAllocator: nalgebra::allocator::Allocator<f64, D>,
     nalgebra::DefaultAllocator: nalgebra::allocator::Allocator<T, D>,
     R: ::rand::Rng + ?Sized,
-    T: ::num_traits::Num
-        + ::nalgebra::Scalar
+    T: nalgebra::Scalar
+        + num_traits::Zero
         + num_traits::AsPrimitive<u64>
         + num_traits::FromPrimitive,
     super::Binomial: rand::distributions::Distribution<T>,
@@ -203,8 +202,7 @@ where
     let mut samples_left = n;
 
     let mut p_sorted_inds: Vec<_> = (0..p.len()).collect();
-
-    // unwrap because NAN elements not allowed from this struct's `new`
+    // unwrap succeeds as NAN elements not allowed from this struct's `new`
     p_sorted_inds.sort_unstable_by(|&i, &j| p[j].partial_cmp(&p[i]).unwrap());
 
     for ind in p_sorted_inds.into_iter().take(p.len() - 1) {
@@ -215,6 +213,11 @@ where
         if !(0.0..=1.0).contains(&probs_not_taken) || samples_left == 0 {
             break;
         }
+        // since $p_j \le p_i \forall j < i$ and $\vec{p}$ is normalized, then
+        // $1 - sum(p_j, j, 0, i-1) = sum(p_j, j, i, n) = p_i + sum(p_j, j, i+1, n) > p_i$
+        // this guarantees that logically p_binom on [0,1]
+        // TODO: demonstrate that this behavior also behaves well with floating point
+        // the logical reasoning provides that `unwrap` of Binomial::new will typically succeed
         let p_binom = pi / probs_not_taken;
         res[ind] = super::Binomial::new(p_binom, samples_left)
             .unwrap()