IncrementalRegressionLinear Fit

Fit incremental linear regression model

Since R2023b

• 

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

Description

The IncrementalRegressionLinear Fit block fits a configured incremental model for linear regression (incrementalRegressionLinear) to streaming data.

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 x receives a chunk of predictor data (observations), and the input port y receives a chunk of responses to which the model is fit. The output port mdl returns an updated incrementalRegressionLinear model. The optional input port w receives a chunk of observation weights.

Examples

New

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

Ports

Input

x — Chunk of predictor data

numeric matrix

Chunk of predictor data to which the model is fit, 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 the observations in the predictor data are oriented along the rows of x.

The length of the observation responses y and the number of observations in x must be equal; y(j) is the response of observation j (row or column) in x.

Note

• The number of predictor variables in x must be equal to the NumPredictors property value of the initial model. If the number of predictor variables in the streaming data changes from NumPredictors, the block issues an error.

• The IncrementalRegressionLinear Fit block supports only numeric 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

y — Chunk of responses

numeric vector

Chunk of responses to which the model is fit, specified as a numeric vector.

The length of the observation responses y and the number of observations in x must be equal; y(j) is the response of observation j (row or column) in x. For more information, see Predicted Response.

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

w — Chunk of observation weights

vector of positive values

Chunk of observation weights, specified as a vector of positive values. The IncrementalRegressionLinear Fit block weights the observations in x with the corresponding values in w. The size of w must be equal to the number of observations in x.

Dependencies

To enable this port, select the check box for Add input port for observation weights on the Main tab of the Block Parameters dialog box.

Data Types: single | double

Output

mdl — Updated incremental learning model parameters

bus signal

Updated parameters of the incremental learning model fit to streaming data (including Beta and Bias), returned as a bus signal (see Composite Signals (Simulink)).

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

• 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 input port for observation weights — Add second input port for observation weights

off (default) | on

Select the check box to include the input port w for observation weights in the IncrementalRegressionLinear Fit block.

Programmatic Use

Block Parameter: ShowInputWeights

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 Fit 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 (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"

Beta data type — Data type of linear coefficient estimates 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) | Enum: <class name> | <data type expression>

Specify the data type for the linear coefficient estimates (beta) output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

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: BetaDataTypeStr

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)" | "Enum: <class name>" | "<data type expression>"

Default: "Inherit: auto"

Beta data type Minimum — Minimum value of beta for range checking

[] (default) | scalar

Specify the lower value of the beta 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 Beta data type Minimum parameter does not saturate or clip the actual beta output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BetaOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Beta data type Maximum — Maximum value of beta for range checking

[] (default) | scalar

Specify the upper value of the beta 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 Beta data type Maximum parameter does not saturate or clip the actual beta output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BetaOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Bias data type — Data type of intercept estimates 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) | Enum: <class name> | <data type expression>

Specify the data type for the intercept estimates (bias) output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

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: BiasDataTypeStr

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)" | "Enum: <class name>" | "<data type expression>"

Default: "Inherit: auto"

Bias data type Minimum — Minimum value of bias for range checking

[] (default) | scalar

Specify the lower value of the bias 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 Bias data type Minimum parameter does not saturate or clip the actual bias output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BiasOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Bias data type Maximum — Maximum value of bias for range checking

[] (default) | scalar

Specify the upper value of the bias 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 Bias data type Maximum parameter does not saturate or clip the actual bias output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BiasOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Internal states data type — Data type of internal states 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) | Enum: <class name> | <data type expression>

Specify the data type for the internal states output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

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: StatesDataTypeStr

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)" | "Enum: <class name>" | "<data type expression>"

Default: "Inherit: auto"

Internal states data type Minimum — Minimum value of internal states for range checking

[] (default) | scalar

Specify the lower value of the internal states 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 Internal states data type Minimum parameter does not saturate or clip the actual internal states output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: StatesOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Internal states data type Maximum — Maximum value of internal states for range checking

[] (default) | scalar

Specify the upper value of the internal states 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 Internal states data type Maximum parameter does not saturate or clip the actual internal states output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: StatesOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Mu data type — Data type of mu 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) | Enum: <class name> | <data type expression>

Specify the data type for the mu (predictor means) output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

If you do not specify Standardize="true" when you create the initial model mdl, then the IncrementalRegressionLinear Fit block sets mu to 0.

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: MuDataTypeStr

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)" | "Enum: <class name>" | "<data type expression>"

Default: "Inherit: auto"

Mu data type Minimum — Minimum value of mu for range checking

[] (default) | scalar

Specify the lower value of the mu 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 Mu data type Minimum parameter does not saturate or clip the actual mu output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: MuOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Mu data type Maximum — Maximum value of mu for range checking

[] (default) | scalar

Specify the upper value of the mu 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 Mu data type Maximum parameter does not saturate or clip the actual mu output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: MuOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Sigma data type — Data type of sigma 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) | Enum: <class name> | <data type expression>

Specify the data type for the sigma (predictor standard deviations) output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

If you do not specify Standardize=true when you create the initial model mdl, then the IncrementalRegressionLinear Fit block sets sigma to 0.

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: SigmaDataTypeStr

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)" | "Enum: <class name>" | "<data type expression>"

Default: "Inherit: auto"

Sigma data type Minimum — Minimum value of sigma for range checking

[] (default) | scalar

Specify the lower value of the sigma 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 Sigma data type Minimum parameter does not saturate or clip the actual sigma output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: SigmaOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Sigma data type Maximum — Maximum value of sigma for range checking

[] (default) | scalar

Specify the upper value of the sigma 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 Sigma data type Maximum parameter does not saturate or clip the actual sigma output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: SigmaOutMax

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

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.

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