RegressionSVM Predict
Libraries:
Statistics and Machine Learning Toolbox /
Regression
Description
The RegressionSVM Predict block predicts responses using an SVM regression
object (RegressionSVM
or CompactRegressionSVM
).
Import a trained SVM regression object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port yfit returns a predicted response for the observation.
Examples
Predict Responses Using RegressionSVM Predict Block
Train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction.
Ports
Input
x — Predictor data
row vector | column vector
Predictor data, specified as a column vector or row vector of one observation.
The variables in x must have the same order as the predictor variables that trained the SVM model specified by Select trained machine learning model.
If you set 'Standardize',true
in fitrsvm
when training the
SVM model, then the RegressionSVM Predict block
standardizes the values of x using the means and
standard deviations in the Mu
and
Sigma
properties (respectively) of the SVM
model.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Output
yfit — Predicted response
scalar
Predicted response, returned as a scalar.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Parameters
Main
Select trained machine learning model — SVM regression model
svmMdl
(default) | RegressionSVM
object | CompactRegressionSVM
object
Specify the name of a workspace variable that contains a RegressionSVM
object or
CompactRegressionSVM
object.
When you train the SVM model by using fitrsvm
, the following
restrictions apply:
The predictor data cannot include categorical predictors (
logical
,categorical
,char
,string
, orcell
). If you supply training data in a table, the predictors must be numeric (double
orsingle
). Also, you cannot use theCategoricalPredictors
name-value argument. To include categorical predictors in a model, preprocess them by usingdummyvar
before fitting the model.The value of the
ResponseTransform
name-value argument must be'none'
(default).The value of the
KernelFunction
name-value argument must be'gaussian'
,'linear'
(default), or'polynomial'
.
Programmatic Use
Block Parameter:
TrainedLearner |
Type: workspace variable |
Values:
RegressionSVM object |
CompactRegressionSVM object |
Default:
'svmMdl' |
Data Types
Fixed-Point Operational ParametersInteger rounding mode — Rounding mode for fixed-point operations
Floor
(default) | Ceiling
| Convergent
| Nearest
| Round
| Simplest
| Zero
Specify the rounding mode for fixed-point operations. For more information, see Rounding Modes (Fixed-Point Designer).
Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression into the mask field using a MATLAB® rounding function.
Programmatic Use
Block Parameter:
RndMeth |
Type: character vector |
Values:
"Ceiling" | "Convergent" | "Floor" | "Nearest" | "Round" | "Simplest" |
"Zero" |
Default:
"Floor" |
Saturate on integer overflow — Method of overflow action
off
(default) | on
Specify whether overflows saturate or wrap.
Action | Rationale | Impact on Overflows | Example |
---|---|---|---|
Select this check box
( | Your model has possible overflow, and you want explicit saturation protection in the generated code. | Overflows saturate to either the minimum or maximum value that the data type can represent. | The maximum value that the |
Clear this check box
( | You want to optimize the efficiency of your generated code. You want to avoid overspecifying how a block handles out-of-range signals. For more information, see Troubleshoot Signal Range Errors (Simulink). | Overflows wrap to the appropriate value that the data type can represent. | The maximum value that the |
Programmatic Use
Block Parameter:
SaturateOnIntegerOverflow |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type
off
(default) | on
Select this parameter to prevent the fixed-point tools from overriding the data type you specify for the block. For more information, see Use Lock Output Data Type Setting (Fixed-Point Designer).
Programmatic Use
Block Parameter:
LockScale |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Output data type — Data type of yfit output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the yfit output. The type can be inherited,
specified directly, or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that inherits a data type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter: OutDataTypeStr |
Type: character vector |
Values: "Inherit: auto" |
"double" |
"single" |
"half" |
"int8" |
"uint8" |
"int16" |
"uint16" |
"int32" |
"uint32" |
"int64" |
"uint64" |
"boolean" |
"fixdt(1,16,0)" |
"fixdt(1,16,2^0,0)" |
"<data type
expression>" |
Default: "Inherit: auto" |
Output data type Minimum — Minimum value of yfit output for range checking
[]
(default) | scalar
Specify the lower value of the yfit output range that Simulink® checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Output data type Minimum parameter does not saturate or clip the actual yfit signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
OutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Output data type Maximum — Maximum value of yfit output for range checking
[]
(default) | scalar
Specify the upper value of the yfit output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Output data type Maximum parameter does not saturate or clip the actual yfit signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
OutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Kernel data type — Kernel computation data type
double
(default) | single
| half
| int8
| uint8
| int16
| uint16
| int32
| int64
| uint64
| uint32
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type of a parameter for kernel computation. The type can be specified
directly or expressed as a data type object such as
Simulink.NumericType
.
The Kernel data type parameter specifies the data type of a
different parameter depending on the type of kernel function of the specified SVM model.
You specify the KernelFunction
name-value argument when training the
SVM model.
'KernelFunction' value | Data Type |
---|---|
'gaussian' or 'rbf' | Kernel data type specifies the data type of the squared distance for the Gaussian kernel , where x is the predictor data for an observation and s is a support vector. |
'linear' | Kernel data type specifies the data type for the output of the linear kernel function , where x is the predictor data for an observation and s is a support vector. |
'polynomial' | Kernel data type specifies the data type for the output of the polynomial kernel function , where x is the predictor data for an observation, s is a support vector, and p is a polynomial kernel function order. |
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
KernelDataTypeStr |
Type: character vector |
Values: 'double' |
'single' | 'half' |
'int8' | 'uint8' |
'int16' | 'uint16' |
'int32' | 'uint32' |
'uint64' | 'int64' |
'boolean' | 'fixdt(1,16,0)' |
'fixdt(1,16,2^0,0)' | '<data type
expression>' |
Default:
'double' |
Kernel data type Minimum — Minimum kernel computation value for range checking
[]
(default) | scalar
Specify the lower value of the kernel computation internal variable range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Kernel data type Minimum parameter does not saturate or clip the actual kernel computation value.
Programmatic Use
Block Parameter:
KernelOutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Kernel data type Maximum — Maximum kernel computation value for range checking
[]
(default) | scalar
Specify the upper value of the kernel computation internal variable range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Kernel data type Maximum parameter does not saturate or clip the actual kernel computation value.
Programmatic Use
Block Parameter:
KernelOutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
Tips
If you are using a linear SVM model and it has many support vectors, then prediction can be slow. To efficiently predict responses based on a linear SVM model, remove the support vectors from the
RegressionSVM
orCompactRegressionSVM
object by usingdiscardSupportVectors
.
Alternative Functionality
You can use a MATLAB Function block with the predict
object function of an SVM regression object (RegressionSVM
or CompactRegressionSVM
). For an example, see
Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the RegressionSVM Predict block in the
Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict
function, consider
the following:
If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.
Support for variable-size arrays must be enabled for a MATLAB Function block with the
predict
function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.
Version History
Introduced in R2020bR2021a: Specify Kernel data type as a data type name or data type object
Starting in R2021a, the Kernel data type parameter does not support inherited options. You can specify Kernel data type as a supported data type name or data type object.
See Also
Blocks
- RegressionTree Predict | RegressionEnsemble Predict | RegressionNeuralNetwork Predict | RegressionGP Predict | ClassificationSVM Predict
Objects
Functions
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)