Main Content

IncrementalClassificationLinear Fit

Fit incremental linear binary classification model

Since R2023b

expand all in page
  • IncrementalClassificationLinear Fit Block Icon

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

Description

The IncrementalClassificationLinear Fit block fits a configured incremental model for linear binary classification (incrementalClassificationLinear) to streaming data.

Import an initial linear classification 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 (labels) to which the model is fit. The output port mdl returns an updated incrementalClassificationLinear model. The optional input port w receives a chunk of observation weights.

Examples

Perform Incremental Learning Using IncrementalClassificationLinear Fit and Predict Blocks

Perform Incremental Learning Using IncrementalClassificationLinear Fit and Predict Blocks

Perform incremental learning with the IncrementalClassificationLinear Fit block and predict labels with the IncrementalClassificationLinear Predict block.

Configure Simulink Template for Rate-Based Incremental Linear Classification

Configure Simulink Template for Rate-Based Incremental Linear Classification

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

Configure Simulink Template for Conditionally Enabled Incremental Linear Classification

Configure Simulink Template for Conditionally Enabled Incremental Linear Classification

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

Ports

Input

expand all

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.

The following restrictions apply:

  • 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 IncrementalClassificationLinear 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

Chunk of class labels to which the model is trained, specified as a numeric, logical, or enumerated vector. The following restrictions apply:

  • The IncrementalClassificationLinear Fit block supports only binary classification.

  • 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.

  • Each label must correspond to one row of the observation matrix.

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

Chunk of observation weights, specified as a vector of positive values. The IncrementalClassificationLinear 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

expand all

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

expand all

Main

Specify the name of a workspace variable that contains the configured incrementalClassificationLinear 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 ScoreTransform property of the initial model cannot be "invlogit" or an anonymous function.

  • 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: incrementalClassificationLinear model object
Default: "linearMdl"

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

Programmatic Use

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

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"

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 IncrementalClassificationLinear 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

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 linear coefficient estimates (beta) in the mdl output bus signal. The type can be inherited, specified directly, 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 or string
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 beta range that Simulink® checks.

Simulink uses the minimum value to perform:

Note

The Beta data type Minimum parameter does not saturate or clip the actual beta estimate. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BetaOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the beta range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Beta data type Maximum parameter does not saturate or clip the actual beta estimate. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BetaOutMax
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the data type for the intercept estimates (bias) in the mdl output bus signal. The type can be inherited, specified directly, 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 or string
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 bias range that Simulink checks.

Simulink uses the minimum value to perform:

Note

The Bias data type Minimum parameter does not saturate or clip the actual bias estimate. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BiasOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the bias range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Bias data type Maximum parameter does not saturate or clip the actual bias estimate. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: BiasOutMax
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the data type for the internal states in the mdl output bus signal. The type can be inherited, specified directly, 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 or string
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 internal states range that Simulink checks.

Simulink uses the minimum value to perform:

Note

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

Programmatic Use

Block Parameter: StatesOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the internal states range that Simulink checks.

Simulink uses the maximum value to perform:

Note

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

Programmatic Use

Block Parameter: StatesOutMax
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the data type for the internal prior term. The type can be inherited, specified directly, 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: PriorDataTypeStr
Type: character vector or string
Values: "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"

Specify the lower value of the prior term range that Simulink checks.

Simulink uses the minimum value to perform:

Note

The Prior data type Minimum parameter does not saturate or clip the actual prior term value. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: PriorOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the prior term range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Prior data type Maximum parameter does not saturate or clip the actual prior term value. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: PriorOutMax
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the data type for the predictor means (mu) in the mdl output bus signal. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

If you do not specify Standardize="true" when you create the initial model, then the IncrementalClassificationLinear 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 or string
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 mu range that Simulink checks.

Simulink uses the minimum value to perform:

Note

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

Programmatic Use

Block Parameter: MuOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the mu range that Simulink checks.

Simulink uses the maximum value to perform:

Note

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

Programmatic Use

Block Parameter: MuOutMax
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the data type for the predictor standard deviations (sigma) in the mdl output bus signal. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

If you do not specify Standardize=true when you create the initial model, then the IncrementalClassificationLinear 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 or string
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 sigma range that Simulink checks.

Simulink uses the minimum value to perform:

Note

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

Programmatic Use

Block Parameter: SigmaOutMin
Type: character vector
Values: "[]" | scalar
Default: "[]"

Specify the upper value of the sigma range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Sigma data type Maximum parameter does not saturate or clip the actual sigma value. 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

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

expand all

See Also

Blocks

Objects

Functions

Topics