IncrementalClassificationLinear Predict

Classify observations using incremental linear classification model

Since R2023b

• 

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

Description

The IncrementalClassificationLinear Predict block classifies observations using a trained linear classification model returned as the output of an IncrementalClassificationLinear Fit block.

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 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 label returns predicted class labels for the chunk. The optional output port score returns predicted class scores or posterior probabilities. The optional output port CanPredict returns the prediction status of the trained model.

Examples

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.

• Since R2023b
• 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

mdl — Incremental learning model

bus signal

Incremental learning model (incrementalClassificationLinear) 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 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

Output

label — Chunk of predicted class labels

column vector

Chunk of predicted class labels, returned as a column vector. The label label(i) represents the class yielding the highest score for the observation x(i). For more details, see the Label argument of the predict object function.

Note

If you specify an estimation period when you create mdl, then during the estimation period the predicted class labels are the majority class, which makes up the largest proportion of the training labels in x.

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

score — Predicted class scores or posterior probabilities

matrix

Predicted class scores or posterior probabilities, returned as a matrix. If the model was trained using a logistic learner, the classification scores are posterior probabilities. The classification score score(i) represents the posterior probability that the observation in x belongs to class i. For more details, see Classification Score.

To check the order of the classes, use the ClassNames property of the linear classification model specified by Select initial machine learning model.

Note

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

Dependencies

To enable this port, select the check box for Add output port for predicted class scores on the Main tab of the Block Parameters dialog box.

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

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 classification model

linearMdl (default) | incrementalClassificationLinear model object

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"

Add output port for predicted class scores — Add second output port for predicted class scores

off (default) | on

Select the check box to include the output port score for predicted class scores in the incremental ClassificationLinear Predict block.

Programmatic Use

Block Parameter: ShowOutputScore

Type: character vector

Values: "off" | "on"

Default: "off"

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 IncrementalClassificationLinear Predict block. This check box does not appear if the workspace already contained an incremental linear classification model named linearMdl capable of prediction when you created the IncrementalClassificationLinear Predict block. Alternatively, you can specify to include the output port CanPredict by selecting the IncrementalClassificationLinear 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 IncrementalClassificationLinear 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

Label data type — Data type of label output

Inherit: Inherit via back propagation | 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 label output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

The supported data types depend on the labels used in the model specified by Select initial machine learning model.

• If the model uses numeric or logical labels, the supported data types are Inherit: Inherit via back propagation (default), double, single, half, int8, uint8, int16, uint16, int32, uint32, int64, uint64, boolean, fixed point, and a data type object.

• If the model uses nonnumeric labels, the supported data types are Enum: <class name> and a data type object.

When you select an inherited option, the software behaves as follows:

• Inherit: Inherit via back propagation (default for numeric and logical labels) — Simulink automatically determines the Label data type of the block during data type propagation (see Data Type Propagation (Simulink)). In this case, the block uses the data type of a downstream block or signal object.

• Inherit: auto (default for nonnumeric labels) — The block uses an autodefined enumerated data type variable. For example, suppose the workspace variable name specified by Select initial machine learning model is myMdl, and the class labels are class 1 and class 2. Then, the corresponding label values are myMdl_enumLabels.class_1 and myMdl_enumLabels.class_2. The block converts the class labels to valid MATLAB identifiers by using the matlab.lang.makeValidName function.

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: LabelDataTypeStr

Type: character vector

Values: "Inherit: Inherit via back propagation" | "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: Inherit via back propagation" (for numeric and logical labels) | "Inherit: auto" (for nonnumeric labels)

Label data type Minimum — Minimum value of label output for range checking

[] (default) | scalar

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

Dependencies

You can specify this parameter only if the model specified by Select initial machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Label data type Maximum — Maximum value of label output for range checking

[] (default) | scalar

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

Dependencies

You can specify this parameter only if the model specified by Select initial machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Score data type — Data type of score 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 score 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: ScoreDataTypeStr

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"

Score data type Minimum — Minimum value of score output for range checking

[] (default) | scalar

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

Programmatic Use

Block Parameter: ScoreOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Score data type Maximum — Maximum value of score output for range checking

[] (default) | scalar

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

Programmatic Use

Block Parameter: ScoreOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Inner product data type — Inner product data type

Inherit: Inherit via internal rule (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 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 | enumerated | fixed point | half | integer | single
Direct Feedthrough	yes
Multidimensional Signals	no
Variable-Size Signals	no
Zero-Crossing Detection	no

More About

Classification Score

For linear classification models, the raw classification score for classifying the observation x into the positive class is defined by

f(x) = xβ+b

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

The raw classification score for classifying x into the negative class is –f(x). The software classifies observations into the class that yields the positive score.

If the linear classification model uses no score transformations, then the raw classification score is the same as the classification score. If the model consists of logistic regression learners, then the software applies the "logit" score transformation to the raw classification scores.

You can specify the data types for the components required to compute classification scores using Score data type, Raw score data type, and Inner product data type.

• Score data type determines the data type of the classification score.

• Raw score data type determines the data type of the raw classification score f if the model uses a score transformation other than "none" or "identity".

• 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 IncrementalClassificationLinear Predict block additionally supports initial machine learning models where Solver is "sgd" or "asgd".