leavepout

			leavepout(n::Integer, [size = 1]) -> Tuple

Compute the train/validation assignments for k ≈ n/size repartitions of n observations, and return them in the form of two vectors. The first vector contains the index-vectors for the training subsets, and the second vector the index-vectors for the validation subsets respectively. Each validation subset will have either size or size+1 observations assigned to it. The following code snippet generates the index-vectors for size = 2.

			
			
			
			
			
			
			julia
			>
			 
			train_idx
			,
			 
			val_idx
			 
			=
			 
			

			leavepout
			(
			10
			,
			 
			2
			)
			;

Each observation is assigned to the validation subset once (and only once). Thus, a union over all validation index-vectors reproduces the full range 1:n. Note that there is no random assignment of observations to subsets, which means that adjacent observations are likely to be part of the same validation subset.

			
			julia> train_idx
5-element Array{Array{Int64,1},1}:
 [3,4,5,6,7,8,9,10]
 [1,2,5,6,7,8,9,10]
 [1,2,3,4,7,8,9,10]
 [1,2,3,4,5,6,9,10]
 [1,2,3,4,5,6,7,8]

julia> val_idx
5-element Array{UnitRange{Int64},1}:
 1:2
 3:4
 5:6
 7:8
 9:10

			leavepout(data, p = 1)

Repartition a data container using a k-fold strategy, where k is chosen in such a way, that each validation subset of the resulting folds contains roughly p observations. Defaults to p = 1, which is also known as "leave-one-out" partitioning.

The resulting sequence of folds is returned as a lazy iterator. Only data subsets are created. That means no actual data is copied until getobs is invoked.

			
			
			
			for
			
			 
			
			(
			train
			,
			 
			val
			)
			 
			in
			 
			

			leavepout
			(
			X
			,
			 
			
			p
			=
			2
			)
			
			
    
			# if nobs(X) is dividable by 2,
			
    
			# then numobs(val) will be 2 for each iteraton,
			
    
			# otherwise it may be 3 for the first few iterations.
			

			end

See kfolds for a related function.