IncrementalRegressionLinear Predict

Predict responses using incremental linear regression model

Since R2023b

• 

Libraries:
Statistics and Machine Learning Toolbox / Incremental Learning / Regression / Linear

Description

The IncrementalRegressionLinear Predict block predicts responses for streaming data using a trained linear regression model returned as the output of an IncrementalRegressionLinear Fit block.

Import an initial linear regression model object into the block by specifying the name of a workspace variable that contains the object. The input port mdl receives a bus signal that represents an incremental learning model fit to streaming data. The input port x receives a chunk of predictor data (observations), and the output port yfit returns predicted responses for the chunk. The optional output port CanPredict returns the prediction status of the trained model.

Examples

Perform Incremental Learning Using IncrementalRegressionLinear Fit and Predict Blocks

Perform incremental learning with the IncrementalRegressionLinear Fit block and predict responses with the IncrementalRegressionLinear Predict block.

• Since R2023b
• Open Live Script

Configure Simulink Template for Rate-Based Incremental Linear Regression

Configure the Simulink Rate-Based Incremental Learning template to perform incremental linear regression.

• Since R2024a
• Open Live Script

Configure Simulink Template for Conditionally Enabled Incremental Linear Regression

Configure the Simulink Enabled Execution Incremental Learning template to perform incremental linear regression.

• Since R2024a
• Open Live Script

Ports

Input

mdl — Incremental learning model

bus signal

Incremental learning model (incrementalRegressionLinear) fit to streaming data, specified as a bus signal (see Composite Signals (Simulink)).

x — Chunk of predictor data

numeric matrix

Chunk of predictor data, specified as a numeric matrix. The orientation of the variables and observations is specified by Predictor data observation dimension. The default orientation is rows, which indicates that observations in the predictor data are oriented along the rows of x.

Note

The block supports only numerical input predictor data. If your input data includes categorical data, you must prepare an encoded version of the categorical data. Use dummyvar to convert each categorical variable to a numeric matrix of dummy variables. Then, concatenate all dummy variable matrices and any other numeric predictors. For more details, see Dummy Variables.

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

Output

yfit — Chunk of predicted responses

floating-point vector

Chunk of predicted responses, returned as a floating-point vector. For more details, see Predicted response and the YHat argument of the predict object function.

Note

If you specify an estimation period when you create mdl, then the predicted responses are zero during the estimation period.

Data Types: single | double

CanPredict — Model status

logical

Model status for prediction, returned as logical 0 (false) or 1 (true).

Note

If you specify an estimation period when you create mdl, then the model status is 0 (false) during the estimation period.

Dependencies

To enable this port, select the check box for Add output port for status of trained machine learning model on the Main tab of the Block Parameters dialog box.

Parameters

Main

Select initial machine learning model — Initial incremental linear regression model

linearMdl (default) | incrementalRegressionLinear model object

Specify the name of a workspace variable that contains the configured incrementalRegressionLinear model object.

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). To include categorical predictors in a model, preprocess them by using dummyvar before fitting the model.

• The NumPredictors property of the initial model must be a positive integer scalar, and must be equal to the number of predictors in x.

• Before R2024a: the Solver property of the initial model must be "scale-invariant".

Programmatic Use

Block Parameter: InitialLearner

Type: workspace variable

Values: incrementalRegressionLinear model object

Default: "linearMdl"

Add output port for status of trained machine learning model — Add second output port for model status

off (default) | on

Select the check box to include the output port CanPredict in the IncrementalRegressionLinear Predict block. This check box does not appear if the workspace already contained an incremental linear regression model named linearMdl capable of prediction when you created the IncrementalRegressionLinear Predict block. Alternatively, you can specify to include the output port CanPredict by selecting the IncrementalRegressionLinear Predict block in the Simulink^® workspace and entering set_param(gcb,ShowOutputCanPredict="on") at the MATLAB command line.

Programmatic Use

Block Parameter: ShowOutputCanPredict

Type: character vector

Values: "off" | "on"

Default: "off"

Predictor data observation dimension — Observation dimension of predictor data

rows (default) | columns

Specify the observation dimension of the predictor data. The default value is rows, which indicates that observations in the predictor data are oriented along the rows of x.

Programmatic Use

Block Parameter: ObservationsIn

Type: character vector

Values: "rows" | "columns"

Default: "rows"

Sample time (–1 for inherited) — Option to specify sample time

–1 (default) | scalar

Specify the discrete interval between sample time hits or specify another type of sample time, such as continuous (0) or inherited (–1). For more options, see Types of Sample Time (Simulink).

By default, the IncrementalRegressionLinear Predict block inherits sample time based on the context of the block within the model.

Programmatic Use

Block Parameter: SystemSampleTime

Type: string scalar or character vector

Values: scalar

Default: "–1"

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 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 (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"

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"

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(1,16,0) | 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 output. 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 output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: OutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Inner product data type — Inner product data type

double (default) | Inherit: Inherit via internal rule | 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 inner product term of the predicted response. The type can be inherited, 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 inner product 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: InnerProductDataTypeStr

Type: character vector

Values: "Inherit: Inherit via internal rule" | "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: "double"

Inner product data type Minimum — Minimum of inner product term for range checking

[] (default) | scalar

Specify the lower value of the inner product term 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 Inner product data type Minimum parameter does not saturate or clip the actual inner product term value.

Programmatic Use

Block Parameter: InnerProductOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Inner product data type Maximum — Maximum of inner product term for range checking

[] (default) | scalar

Specify the upper value of the inner product term 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 Inner product data type Maximum parameter does not saturate or clip the actual inner product term value.

Programmatic Use

Block Parameter: InnerProductOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

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

More About

Predicted Response

For linear regression models, the predicted response for the observation x is

y = xβ+b

β is the estimated column vector of coefficients, and b is the estimated scalar bias. The linear regression model object specified by Select initial machine learning model contains the coefficients and bias in the Beta and Bias properties, respectively. β and b correspond to Beta and Bias, respectively.

You can specify the data types for the components required to compute predicted responses using Output data type and Inner product data type.

• Output data type determines the data type of the predicted response.

• Inner product data type determines the data type of xβ.

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 R2023b

R2024a: Incremental linear blocks support additional solvers

Starting in R2024a, the IncrementalRegressionLinear Predict block additionally supports initial machine learning models where Solver is "sgd" or "asgd".