IncrementalClassificationECOC Fit

Fit incremental ECOC classification model

Since R2024a

• 

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

Description

The IncrementalClassificationECOC Fit block fits a configured incremental error-correcting output codes (ECOC) classification model (incrementalClassificationECOC) for multiclass classification to streaming data.

Import an initial ECOC classification 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 returns an updated incrementalClassificationECOC model mdl. The optional input port w receives a chunk of observation weights and the optional input port reset resets the learned parameters.

Examples

Perform Incremental Learning Using IncrementalClassificationECOC Fit and Predict Blocks

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

• Since R2024a
• Open Live Script

Configure Simulink Template for Rate-Based Incremental Linear Classification

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

• Since R2024a
• Open Live Script

Configure Simulink Template for Conditionally Enabled Incremental Linear Classification

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

• Since R2024a
• 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.

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

y — Chunk of class labels

numeric vector | logical vector | enumerated vector

Chunk of class labels to which the model is trained, specified as a numeric, logical, or enumerated 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.

• Each label must correspond to one row of the array.

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

w — Chunk of observation weights

vector of positive values

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

reset — Reset signal

0 (false) | 1 (true) | numeric scalar

Since R2025a

Reset signal, specified as 0 (false) or 1 (true) or a numeric scalar. When the reset signal is a positive scalar (greater than 0), the block resets the learned parameters, if any, of the incremental learning model. If any hyperparameters of mdl are estimated during incremental training, those get reset as well. mdl.NumPredictors are always preserved.

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

Output

mdl — Updated incremental learning model parameters

bus signal

Updated incremental learning model parameters fit to streaming data, returned as a bus signal (see Composite Signals (Simulink)).

Parameters

Main

Select initial machine learning model — Initial incremental classification ECOC model

ecocMdl (default) | incrementalClassificationECOC model object

Specify the name of a workspace variable that contains the configured incrementalClassificationECOC model object. The initial model must have following properties specified:

• The NumPredictors property which must be a positive integer scalar equal to the number of predictors in x.

• The ClassNames property.

Programmatic Use

Block Parameter: InitialLearner

Type: workspace variable

Values: incrementalClassificationECOC model object

Default: "ecocMdl"

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 IncrementalClassificationECOC Fit block.

Programmatic Use

Block Parameter: ShowInputWeights

Type: character vector

Values: "off" | "on"

Default: "off"

Add input port for resetting trained learner — Add input port for resetting trained incremental learner

off (default) | on

Since R2025a

Select the check box to include the input port reset for the reset signal in the IncrementalClassificationECOC Fit block.

Programmatic Use

Block Parameter: ShowInputReset

Type: character vector or string

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 IncrementalClassificationECOC 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 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

Binary learner 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) | <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: BinaryBetaDataTypeStr

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"

Binary learner 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 Binary learner 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: BinaryBetaOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner 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 Binary learner 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: BinaryBetaOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner 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) | <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: BinaryBiasDataTypeStr

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"

Binary learner 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 Binary learner 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: BinaryBiasOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner 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 Binary learner 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: BinaryBiasOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner internal states data type — Data type of binary learner 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) | <data type expression>

Specify the data type for the binary learner 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: BinaryStatesDataTypeStr

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"

Binary learner internal states data type Minimum — Minimum value of binary learner internal states for range checking

[] (default) | scalar

Specify the lower value of the binary learner internal states 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).

Programmatic Use

Block Parameter: BinaryStatesOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner internal states data type Maximum — Maximum value of binary learner internal states for range checking

[] (default) | scalar

Specify the upper value of the binary learner internal states 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 software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

Programmatic Use

Block Parameter: BinaryStatesOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Prior data type — Data type of prior term

double (default) | 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 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"

Prior data type Minimum — Minimum value of prior term for range checking

[] (default) | scalar

Specify the lower value of the prior 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).

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: "[]"

Prior data type Maximum — Maximum value of prior term for range checking

[] (default) | scalar

Specify the upper value of the prior 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).

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: "[]"

Binary learner mu data type — Data type of binary learner 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) | <data type expression>

Specify the data type for the binary learner 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.

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: Binary 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)" | "<data type expression>"

Default: "Inherit: auto"

Binary learner mu data type Minimum — Minimum value of binary learner mu for range checking

[] (default) | scalar

Specify the lower value of the binary learner mu 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 Binary learner 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: BinaryMuOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner mu data type Maximum — Maximum value of binary learner mu for range checking

[] (default) | scalar

Specify the upper value of the binary learner mu 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 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 Binary learner 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: BinaryMuOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner sigma data type — Data type of binary learner 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) | <data type expression>

Specify the data type for the binary learner 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.

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: Binary 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)"|"<data type expression>"

Default: "Inherit: auto"

Binary learner sigma data type Minimum — Minimum value of binary learner sigma for range checking

[] (default) | scalar

Specify the lower value of the binary learner sigma 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 Binary learner 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: BinarySigmaOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Binary learner sigma data type Maximum — Maximum value of binary learner sigma for range checking

[] (default) | scalar

Specify the upper value of the binary learner sigma 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 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 Binary learner 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: BinarySigmaOutMax

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 R2024a

R2025a: New reset inport

The IncrementalClassificationECOC Fit has a new reset inport that allows the resetting of the learned parameters, if any, of the incremental learning model. If any hyperparameters of the model are estimated during incremental training, those get reset as well. Use the reset signal when performing drift-aware learning to reset the model parameters when drift is detected.