There are four fundamental random number functions: `rand`

, `randi`

, `randn`

, and `randperm`

.
The `rand`

function returns real numbers between
0 and 1 that are drawn from a uniform distribution. For example,

r1 = rand(1000,1);

`r1`

is
a 1000-by-1 column vector containing real floating-point numbers drawn
from a uniform distribution. All the values in `r1`

are
in the open interval (0, 1). A histogram of these values is roughly
flat, which indicates a fairly uniform sampling of numbers.The `randi`

function returns `double`

integer
values drawn from a discrete uniform distribution. For example,

r2 = randi(10,1000,1);

`r2`

is
a 1000-by-1 column vector containing integer values drawn from a discrete
uniform distribution whose range is 1,2,...,10. A histogram of these
values is roughly flat, which indicates a fairly uniform sampling
of integers between 1 and 10. The `randn`

function returns arrays of real
floating-point numbers that are drawn from a standard normal distribution.
For example,

r3 = randn(1000,1);

`r3`

is
a 1000-by-1 column vector containing numbers drawn from a standard
normal distribution. A histogram of `r3`

looks like
a roughly normal distribution whose mean is 0 and standard deviation
is 1.You can use the `randperm`

function to create
arrays of random integer values that have no repeated values. For
example,

r4 = randperm(15,5);

`r4`

is
a 1-by-5 array containing randomly selected integer values on the
closed interval, [1, 15]. Unlike `randi`

, which
can return an array containing repeated values, the array returned
by `randperm`

has no repeated values.Successive calls to any of these functions return different results. This behavior is useful for creating several different arrays of random values.

`rand`

| `randi`

| `randn`

| `randperm`

- Random Numbers Within a Specific Range
- Random Integers
- Random Numbers from Normal Distribution with Specific Mean and Variance

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