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RegressionKernel Predict

Predict responses using Gaussian kernel regression model

Since R2024b

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  • RegressionKernel Predict Block Icon

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

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Predict Responses Using RegressionKernel Predict Block

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.

Ports

Input

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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

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Predicted response, returned as a scalar.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Parameters

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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

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, or cell). If you supply training data in a table, the predictors must be numeric (double or single). Also, you cannot use the CategoricalPredictors name-value argument. To include categorical predictors in a model, preprocess them by using dummyvar 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 Parameters

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"

Specify whether overflows saturate or wrap.

ActionRationaleImpact on OverflowsExample

Select this check box (on).

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 int8 (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box selected, the block output saturates at 127. Similarly, the block output saturates at a minimum output value of –128.

Clear this check box (off).

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 int8 (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box cleared, the software interprets the value causing the overflow as int8, which can produce an unintended result. For example, a block result of 130 (binary 1000 0010) expressed as int8 is –126.

Programmatic Use

Block Parameter: SaturateOnIntegerOverflow
Type: character vector
Values: "off" | "on"
Default: "off"

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"
Data Type

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"

Specify the lower value of the yfit output range that Simulink checks.

Simulink uses the minimum value to perform:

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: '[]'

Specify the upper value of the yfit output range that Simulink checks.

Simulink uses the maximum value to perform:

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: '[]'

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'

Specify the lower value of the kernel computation internal variable range that Simulink checks.

Simulink uses the minimum value to perform:

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: '[]'

Specify the upper value of the kernel computation internal variable range that Simulink checks.

Simulink uses the maximum value to perform:

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

Boolean | double | enumerated | fixed point | half | integer | single

Direct Feedthrough

yes

Multidimensional Signals

no

Variable-Size Signals

no

Zero-Crossing Detection

no

Tips

  • To predict responses using a trained RegressionPartitionedKernel model Mdl with cross-validated folds, access the internal RegressionKernel model using Mdl.Trained{i}, where i 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

Objects

Functions

Topics