creates value set and generates data

since r2019b

description

use the and fixed.datagenerator objects to generate simulation inputs to test the full operating range of your designs.

creation

syntax

data = fixed.datagenerator(name, value)

description

example

data = fixed.datagenerator(name, value) creates a datagenerator object with additional properties specified as name, value pair arguments.

properties

`dataspecifications` — properties of generated data
`fixed.dataspecification` object | cell array of `fixed.dataspecification` objects

properties of the data to generate, specified as a object.

specifying a cell array of dataspecification objects produces a single datagenerator object for input to a system with the same number of inputs and in the same order as elements in the cell array.

`numdatapointslimit` — maximum number of data points in generated data
100000 (default) | integer-valued scalar

maximum number of data points in generated data, specified as an integer-valued scalar. for more information, see .

object functions

	get unique values from `fixed.datagenerator` object
	get information about number of data points in generated data
	get data from `fixed.datagenerator` object

examples

create a `fixed.datagenerator` object

create a datagenerator object by specifying a dataspecification object in the constructor.

create the dataspecification object with an interval from $- 2 π$ to $2 π$ with a data type of single.

dataspec = fixed.dataspecification('single',...
    'intervals',{-2*pi, 2*pi})

dataspec = 
  fixed.dataspecification with properties:
          datatypestr: 'single'
            intervals: [-6.2832,6.2832]
     excludedenormals: false
  excludenegativezero: false
      mandatoryvalues: 
           complexity: 'real'
           dimensions: 1

use the dataspecification object to create a datagenerator object. limit the number of data points in the generated data to 5000 points. you can specify these properties as name-value pairs in the constructor of the datagenerator object.

datagen = fixed.datagenerator('dataspecifications',dataspec,...
    'numdatapointslimit',5000)

datagen = 
  fixed.datagenerator with properties:
    dataspecifications: {[1x1 fixed.dataspecification]}
    numdatapointslimit: 5000

use the outputalldata function to see the generated data.

mydata = outputalldata(datagen);

algorithms

data generation for one-dimensional, two-dimensional, and complex data

when you use a datagenerator object to generate data for a dataspecification object with the dimensions property set to 1, the output data always contains the minimum and maximum values of the specified intervals, and any values specified by the mandatoryvalues property.

when you generate data for a dataspecification object with the dimensions property set to a value greater than 1, the output is generated by taking a cartesian product of the one-dimensional output.

for example, consider the following two dataspecification objects. the two objects are identical except that one is one-dimensional, and the other is two-dimensional.

dataspec_1d = fixed.dataspecification('single',...
   'intervals', {-1,1}, 'dimensions',1);
dataspec_2d = fixed.dataspecification('single',...
   'intervals', {-1,1}, 'dimensions',2);

create two datagenerator objects based on these specifications. set the maximum number of data points in the generated data to inf.

datagen_1d = fixed.datagenerator('dataspecifications', ...
  dataspec_1d, 'numdatapointslimit', inf);
datagen_2d = fixed.datagenerator('dataspecifications', ...
  dataspec_2d, 'numdatapointslimit', inf);

get the size of the generated data for each of the configurations.

size_1d_data = size(outputalldata(datagen_1d))
size_2d_data = size(outputalldata(datagen_2d))

size_1d_data =
     1   244

size_2d_data =
           2       59536

the length of the two-dimensional data is exactly the squared length of the one-dimensional data.

the datagenerator generates complex data in a similar way to the two-dimensional data. create a dataspecification object with dimensions set to 1 and the complexity set to complex. create a datagenerator object using this specification.

dataspec_complex = fixed.dataspecification('single', ...
'intervals', {-1,1}, 'dimensions', 1, 'complexity', 'complex');

datagen_complex = fixed.datagenerator('dataspecifications', ...
  dataspec_complex, 'numdatapointslimit', inf);

get the size of the generated data from this configuration.

size_complex_data = size(outputalldata(datagen_complex))

size_complex_data =
           1       59536

the length of the output data for the one-dimensional complex data is the same as the length of the two-dimensional real data.

version history

introduced in r2019b

creates value set and generates data -凯发k8网页登录

description

creation

syntax

description

properties

`dataspecifications` — properties of generated data
`fixed.dataspecification` object | cell array of `fixed.dataspecification` objects

`numdatapointslimit` — maximum number of data points in generated data
100000 (default) | integer-valued scalar

object functions

examples

create a `fixed.datagenerator` object

algorithms

data generation for one-dimensional, two-dimensional, and complex data

version history

see also

objects

creates value set and generates data -凯发k8网页登录

description

creation

syntax

description

properties

dataspecifications — properties of generated data fixed.dataspecification object | cell array of fixed.dataspecification objects

numdatapointslimit — maximum number of data points in generated data 100000 (default) | integer-valued scalar

object functions

examples

create a fixed.datagenerator object

algorithms

data generation for one-dimensional, two-dimensional, and complex data

version history

see also

objects

wechat

`dataspecifications` — properties of generated data
`fixed.dataspecification` object | cell array of `fixed.dataspecification` objects

`numdatapointslimit` — maximum number of data points in generated data
100000 (default) | integer-valued scalar

create a `fixed.datagenerator` object