always valid for stateful iterators

src/IteratorSampling.jl

@@ -11,37 +11,26 @@ include("UnweightedSamplingSingle.jl")
 include("UnweightedSamplingMulti.jl")
 
 """
-    itsample([rng], iter, [condition::Function]; [alloc])
+    itsample([rng], iter)
 
 Return a random element of the iterator, optionally specifying a `rng` 
-(which defaults to `Random.GLOBAL_RNG`) and a condition to restrict the
-sampling on only those elements for which the function returns `true`. 
-If the iterator is empty or no random element satisfies the condition, 
-it returns `nothing`.
-
-## Keywords
-* `alloc = false`: this keyword chooses the algorithm to perform, if
-  `alloc = false` the algorithm doesn't allocate a new collection to 
-  perform the sampling, which should be better when the number of elements is
-  large.
+(which defaults to `Random.default_rng()`). If the iterator is empty, it 
+returns `nothing`.
 
 -----
 
-    itsample([rng], iter, [condition::Function], n::Int; [alloc, iter_type])
-
-Return a vector of `n` random elements of the iterator without replacement, 
-optionally specifying a `rng` (which defaults to `Random.GLOBAL_RNG`) and 
-a condition to restrict the sampling on only those elements for which the 
-function returns `true`. If the iterator has less than `n` elements or less 
-than `n` elements satisfy the condition, it returns a vector of these elements.
-
-## Keywords
-* `alloc = true`: when the function returns a vector, it happens to be much
-  better to use the allocating version for small iterators.
-* `iter_type = Any`: the iterator type of the given iterator, if not given
-  it defaults to `Any`, which means that the returned vector will be also of
-  `Any` type. For performance reasons, if you can infer the type of the iterator, 
-  it is better to pass it.
+    itsample([rng], iter, n::Int; replace = true, ordered = false)
+
+Return a vector of `n` random elements of the iterator, 
+optionally specifying a `rng` (which defaults to `Random.default_rng()`).
+
+`replace` dictates whether sampling is performed with replacement. 
+`ordered` dictates whether an ordered sample (also called a sequential 
+sample, i.e. a sample where items appear in the same order as in `iter`).
+
+If the iterator has less than `n` elements, it returns a vector of 
+these elements.
+
 """
 function itsample end
 

src/UnweightedSamplingMulti.jl

@@ -1,22 +1,16 @@
 
-function itsample(iter, n::Int; 
-        replace = false, ordered = false, is_stateful = false)
-    return itsample(Random.GLOBAL_RNG, iter, n; 
-                    replace=replace, ordered=ordered, is_stateful=is_stateful)
+function itsample(iter, n::Int; replace = false, ordered = false)
+    return itsample(Random.default_rng(), iter, n; replace=replace, ordered=ordered)
 end
 
 function itsample(rng::AbstractRNG, iter, n::Int; 
-        replace = false, ordered = false, is_stateful = false)
+        replace = false, ordered = false)
     IterHasKnownSize = Base.IteratorSize(iter)
     if IterHasKnownSize isa NonIndexable
-        if is_stateful
-            if replace
-                error("Not implemented yet")
-            else
-                unweighted_resorvoir_sampling(rng, iter, n, Val(ordered))
-            end
+        if replace
+            error("Not implemented yet")
         else
-            double_scan_sampling(rng, iter, n, replace, ordered)
+            unweighted_resorvoir_sampling(rng, iter, n, Val(ordered))
         end
     else
         single_scan_sampling(rng, iter, n, replace, ordered)

src/UnweightedSamplingSingle.jl

@@ -1,16 +1,12 @@
 
-function itsample(iter; is_stateful = false)
-    return itsample(Random.GLOBAL_RNG, iter; is_stateful = false)
+function itsample(iter)
+    return itsample(Random.default_rng(), iter)
 end
 
-function itsample(rng::AbstractRNG, iter; is_stateful = false)
+function itsample(rng::AbstractRNG, iter)
     IterHasKnownSize = Base.IteratorSize(iter)
     if IterHasKnownSize isa NonIndexable
-        if is_stateful
-            unweighted_resorvoir_sampling(rng, iter)
-        else
-            double_scan_sampling(rng, iter)
-        end    
+        return unweighted_resorvoir_sampling(rng, iter)
     else 
         return single_scan_sampling(rng, iter)
     end