# rndu¶

## Purpose¶

Computes uniform random numbers with a choice of underlying random number generator.

## Format¶

y = rndu(r, c)
{ y, newstate } = rndu(r, c, state)
Parameters
• r (scalar) – row dimension.

• c (scalar) – column dimension.

• state (scalar or opaque vector) –

Optional argument.

scalar case

state = starting seed value only. If -1, GAUSS computes the starting seed based on the system clock.

opaque vector case

state = the state vector returned from a previous call to one of the rnd random number functions.

Returns
• y (RxC matrix) – uniform random numbers, $$0 <= y < 1$$.

• newstate (Opaque vector) – the updated state.

## Examples¶

### Example 1¶

Basic usage.

If a state or seed is not passed in, then only the random numbers are returned.

// Create a 100x1 vector of uniform random numbers
y = rndu(100, 1);


### Example 2¶

rndu() can be used to create a vector of random integers in a specified range. The example below, creates 30 random integers in the range $$[1, 1000]$$.

// Largest number in integer range
size = 1000;

// Number of integers to calculate
num_indices = 30;

idx = ceil(size .* rndu(num_indices, 1));


### Example 3¶

This example generates two thousand vectors of uniform random numbers, each with one million elements. The state of the random number generator after each iteration is used as an input to the next generation of random numbers.

// starting seed
state = 13;

// Number of submeans to calculate
n_iters = 2000;

// Number of random numbers to generate
// on each iteration
k = 1000000;

// Pre-allocate 'submean' vector
submean = zeros(n_iters, 1);

for i(1, n_iters, 1);
{ y,state } = rndu(k,1,state);
submean[i] = meanc(y);
endfor;

mean = meanc(submean);
print 0.5-mean;


## Remarks¶

r and c will be truncated to integers if necessary.

## Technical Notes¶

The default generator for rndu() is the SFMT Mersenne-Twister 19937. You can specify a different underlying random number generator with the function rndCreateState().