Resize coder.Type
object
returns a modified copy of t_out
= coder.resize(t
,sz
,variable_dims
)coder.Type
t
with (upper-bound) size sz
and variable dimensions
variable_dims
. If variable_dims
or sz
are scalars, the function applies the scalars to all
dimensions of t
. By default, variable_dims
does not apply
to dimensions where sz
is 0
or 1
, which
are fixed. Use the 'uniform' option to override this special case. The
coder.resize
function ignores variable_dims
for
dimensions with size inf
. These dimensions are variable size.
t
can be a cell array of types, in which case,
coder.resize
resizes all elements of
the cell array.
changes t_out
= coder.resize(t
,[],variable_dims
) t
to have variable dimensions variable_dims
while
leaving the size unchanged.
resizes t_out
= coder.resize(t
,sz
,variable_dims
,Name,Value
)t
by using additional options specified by one or more Name, Value
pair arguments.
resizes the individual dimensions of t_out
= coder.resize(t
,'sizelimits',limits
)t
based on the threshold values in the
limits
vector. The limits
vector is a row vector
containing two positive integer elements. Each dimension of t
is individually
resized according to the thresholds in the limits
vector.
When the size S
of a dimension is lesser than both thresholds defined
in limits
, the dimension remains the same.
When the size S
of a dimension is greater than or equal to the first
threshold and less than the second threshold defined in limits
, the
dimension becomes variable size with upper bound S
.
However, when the size S
of a dimension is also greater than or equal
to the second threshold defined in limits
, the dimension becomes an
unbounded variable size.
If the value of limits
is scalar, the threshold gets scalar-expanded to
represent both thresholds. For example, if limits
is defined as
4
, it is interpreted as [4 4]
.
The 'sizelimits'
option allows you to dynamically allocate memory to
large arrays in your generated code.
For sparse matrices, coder.resize
drops the upper bounds for
variable-size dimensions.