RegressionKernel Predict
Libraries:
Statistics and Machine Learning Toolbox /
Regression
Description
The RegressionKernel Predict block predicts responses using a kernel
regression object (RegressionKernel
).
Import a trained kernel 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 RegressionKernel Predict Block
Use the RegressionKernel Predict block for response prediction in Simulink®. The block accepts an observation (predictor data) and returns the predicted response for the observation using the trained Gaussian kernel regression model. To complete this example, you can use the provided Simulink model, or create a new model.
- Since R2024b
- Open Live Script
Ports
Input
x — Predictor data
row vector | column vector
Predictor data, specified as a row vector or column vector of one observation.
The variables in x must have the same order as the predictor variables that trained the kernel regression model specified by Select trained machine learning model.
If you specify Standardize=true
in fitrkernel
when training the kernel model, then the RegressionKernel Predict block
standardizes the values of x using the means and standard
deviations in the Mu
and Sigma
properties
(respectively) of the 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
To edit block parameters interactively, use the Property Inspector. From the Simulink® Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.
Main
Select trained machine learning model — Kernel regression model
kernelMdl
(default) | RegressionKernel
object
Specify the name of a workspace variable that contains a RegressionKernel
object.
When you train the kernel model by using fitrkernel
,
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).
Programmatic Use
Block Parameter:
TrainedLearner |
Type: character vector or string |
Values:
RegressionKernel object name |
Default:
"kernelMdl" |
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
Inherit: Inherit via internal rule
(default) | double
| 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 the parameters for Gaussian kernel approximation computation.
The type can be specified directly or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: Inherit via internal rule
, the
block uses an internal rule to determine the kernel data type. The internal rule chooses
a data type that optimizes numerical accuracy, performance, and generated code size,
while taking into account the properties of the embedded target hardware. The software
cannot always optimize efficiency and numerical accuracy at the same
time.
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: 'Inherit: Inherit via
internal rule' |
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
To predict responses using a trained
RegressionPartitionedKernel
modelMdl
with cross-validated folds, access the internalRegressionKernel
model usingMdl.Trained{i}
, wherei
is the index of the desired internal model.
Alternative Functionality
You can use a MATLAB Function (Simulink) block with the predict
object
function of a kernel regression object (RegressionKernel
).
For an example of using a MATLAB Function block, see Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the RegressionKernel 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 R2024b
See Also
Blocks
- RegressionSVM Predict | RegressionTree Predict | RegressionEnsemble Predict | RegressionNeuralNetwork Predict | RegressionGP Predict | ClassificationKernel Predict
Objects
Functions
Topics
- Predict Responses Using RegressionSVM Predict Block
- Predict Responses Using RegressionTree Predict Block
- Predict Responses Using RegressionEnsemble Predict Block
- Predict Responses Using RegressionNeuralNetwork Predict Block
- Predict Responses Using RegressionGP Predict Block
- Predict Class Labels Using MATLAB Function Block
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