RegressionSVM Predict

Predict responses using support vector machine (SVM) regression model

• Library:

• 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.

Ports

Input

x — Predictor data

row vector | column vector

Predictor data, specified as a column vector or row vector of one observation.

Dependencies

• 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, 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 the categorical predictors by using dummyvar 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 Parameters

Integer 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 (Fixed-Point Designer).

Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression using a MATLAB^® rounding function into the mask field.

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 (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 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 overflow causing value 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'

Lock output data type setting against changes by the fixed-point tools — Prevent 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'

Data Type

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) | 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 Signal Data Types (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)' | 'fixdt(1,16,0)' | 'fixdt(1,16,2^0,0)' | '<data type expression>'

Default: 'Inherit: auto'

Output minimum — Minimum value of yfit output for range checking

[] (default) | scalar

Lower value of the yfit output range that Simulink^® checks.

Simulink uses the minimum value to perform:

• Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Automatic scaling of fixed-point data types.

• 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 minimum parameter does not saturate or clip the actual yfit signal. Use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: OutMin

Type: character vector

Values: '[]' | scalar

Default: '[]'

Output maximum — Maximum value of yfit output for range checking

[] (default) | scalar

Upper value of the yfit output range that Simulink checks.

Simulink uses the minimum value to perform:

• Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Automatic scaling of fixed-point data types.

• 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 maximum parameter does not saturate or clip the actual yfit signal. 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) | 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 $$D^2={\Vert x-s\Vert }^2$$ for the Gaussian kernel $$G\left(x,s\right)=\exp \left(-D^2\right)$$, 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 $$G\left(x,s\right)=xs\text{'}$$, 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 $$G\left(x,s\right)={\left(1+xs\text{'}\right)}^p$$, 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 Signal Data Types (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)' | 'fixdt(1,16,0)' | 'fixdt(1,16,2^0,0)' | '<data type expression>'

Default: 'double'

Kernel minimum — Minimum kernel computation value for range checking

[] (default) | scalar

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

Simulink uses the minimum value to perform:

• Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Automatic scaling of fixed-point data types.

• 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 minimum parameter does not saturate or clip the actual kernel computation value signal.

Programmatic Use

Block Parameter: KernelOutMin

Type: character vector

Values: '[]' | scalar

Default: '[]'

Kernel maximum — Maximum kernel computation value for range checking

[] (default) | scalar

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

Simulink uses the maximum value to perform:

• Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.

• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).

• Automatic scaling of fixed-point data types.

• 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 maximum parameter does not saturate or clip the actual kernel computation value signal.

Programmatic Use

Block Parameter: KernelOutMax

Type: character vector

Values: '[]' | scalar

Default: '[]'

Model 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.

Open Live Script

Block Characteristics

Data Types	Boolean | double | fixed point | half | integer | single
Direct Feedthrough	yes
Multidimensional Signals	no
Variable-Size Signals	no
Zero-Crossing Detection	no

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 or CompactRegressionSVM object by using discardSupportVectors.

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.

Compatibility Considerations

Specify Kernel data type as a data type name or data type object

Behavior changed in R2021a

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.

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™.