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function C = random (varargin)
%GRB.RANDOM random sparse matrix.
% C = GrB.random (A) has the same pattern as A, but with uniformly
% distributed random entries.
%
% C = GrB.random (m, n, d) is a random m-by-n GraphBLAS matrix, with
% about d*m*n uniformly distributed entries. The entries are
% constructed by computing d*m*n entries at random positions, and
% then any duplicates are discarded, so if d is large or m*n is
% small, then C will have fewer than d*m*n entries. The value of
% d may exceed 1 (this differs from the built-in sprand, which limits
% d to 1). If d is inf, then C is generated as a full GraphBLAS
% matrix, with numel (C) = m*n.
%
% Optional parameters may be used, in any order, after the A or m,n,d
% arguments:
%
% C = GrB.random (..., 'uniform', ...) uses a uniform distribution
% of values, with entries greater than zero and less than one.
%
% C = GrB.random (..., 'normal', ...) uses a normal distribution, like
% the built-in sprandn.
%
% C = GrB.random (..., 'range', [lo hi], ...) changes the range of
% the random numbers. If 'range' is not present, the default
% is double ([0 1]). The type of [lo hi] determines the type
% of the random matrix C. If [lo hi] is logical, all entries
% in the pattern are true. If [lo hi] is 'double' or 'single',
% then if the random number generator (rand or randn) produces
% a double random value of x, it is scaled to (hi-lo)*x+lo. If
% [lo hi] is integer, then x becomes floor ((hi-lo+1)*x + lo),
% which is then typecasted to the requested. integer type. This
% scaling applies to both the 'uniform' and 'normal'
% distribution. To construct a random complex matrix, pass in
% [lo hi] as single complex or double complex.
%
% With the normal distribution, [lo hi] specifies the mean (lo)
% and the standard deviation (hi) of the final distribution.
%
% C = GrB.random (A, 'symmetric', ...) creates a symmetric matrix, like
% the built-in C = sprandsym (A), except that the default
% distribution is 'uniform'. The input matrix A must be
% square. Only tril(A) is used to construct C.
%
% C = GrB.random (n, d, 'symmetric', ...) creates an n-by-n symmetric
% matrix C, with a uniform distribution of values. To create a
% matrix like C = srandsym (n,d) with the built-in sprandym, use
% C = GrB.random (n, d, 'symmetric', 'normal'). Note that the
% arguments (m, n, ...) do not appear; just a single
% dimension (n, ...).
%
% To construct a Hermitian matrix instead, use 'hermitian' in place of
% 'symmetric'.
%
% The rc option of the built-in sprand and sprandn is not supported.
%
% Example:
%
% rng ('default')
% A = sprand (4, 5, 0.5) % 4-by-5 with at most 10 entries
% rng ('default')
% C = GrB.random (4, 5, 0.5) % same pattern as A
% isequal (spones (A), spones (C))
%
% C = GrB.random (C) % same pattern but newly random values
%
% C = GrB.random (2, 4, inf) % 2-by-4 with all 8 entries
% C = GrB.random (2, 4, 0.5, 'normal') % like sprandn (2,4,0.5)
%
% % random 10-by-10 int16 matrix with entries from -3 to 6,
% % including explicit zeros, with a uniform distribution
% C = GrB.random (10, 10, 0.5, 'range', int16 ([-3 6]))
%
% % larger matrix, with normal distribution:
% C = GrB.random (1000, 1000, 0.5, 'normal', 'range', int16 ([-3 6])) ;
% [i,j,x] = find (C) ;
% histogram (x, 'BinMethod', 'integers') ;
%
% % lots of explicit zeros, since uint8 saturates:
% C = GrB.random (1000, 1000, 0.5, 'normal', 'range', uint8 ([-3 6])) ;
% [i,j,x] = find (C) ;
% histogram (x, 'BinMethod', 'integers') ;
%
% % uniform distribution across all possible uint8 values
% C = GrB.random (1000, 1000, 0.5, 'range', uint8 ([0 255])) ;
% [i,j,x] = find (C) ;
% histogram (x, 'BinMethod', 'integers') ;
%
% % large symmetric matrix with normal distribution
% C = GrB.random (1e6, 1e-5, 'symmetric', 'normal')
% [i,j,x] = find (C) ;
% histogram (x)
%
% See also GrB/sprand, GrB/sprandn, GrB/sprandsym.
% SuiteSparse:GraphBLAS, Timothy A. Davis, (c) 2017-2022, All Rights Reserved.
% SPDX-License-Identifier: Apache-2.0
C = GrB (gb_random (varargin {:})) ;
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