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| Matlab or Octave |
= rand(m, n)
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| C++ |
= rand(m, n)
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= rand(m, n)
sets x to an @(@
m \times n
@)@ matrix each entry
drawn from an independent uniform distribution on the interval
@(@
[0,1]
@)@.
function [ok] = rand_ok()
ok = true;
m = 2;
n = 3;
% -------------------
x = rand(2, 3);
% -------------------
[m, n] = size(x);
ok = ok & (m == 2);
ok = ok & (n == 3);
ok = ok & all( all( 0 < x ) ); % probability of limit case is zero
ok = ok & all( all( x < 1 ) );
ok = ok & all( x(1,:) ~= x(2,:) ); % probability of equality is zero
return
rand function with the syntax:
= rand(m, n)
where m and n are size_t values
and x is an @(@
m \times n
@)@
ublas matrix<double> object.
std::rand to generate the random values,
you must first call the standard library function
std::srand()
where seed is an unsigned int
to initialize the random number generator.
seed > 1
,
the mztuni
random number generator will be used
(instead of std::rand).
# include <mat2cpp.hpp>
# include <cstdlib>
bool rand_ok(void)
{ bool ok = true;
using namespace mat2cpp;
size_t i, j, m(2), n(3);
// ---------------------------
matrix<double> x = rand(m, n);
// ---------------------------
ok &= (x.size1() == m);
ok &= (x.size2() == n);
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
{ ok &= (0. < x(i, j) ); // probability of limit is zero
ok &= (x(i, j) < 1.);
}
}
for(j = 0; j < n; j++)
ok &= (x(0, j) != x(1, j)); // probability zero
return ok;
}