IncrementalClassificationLinear Predict
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
, orcell
). If you supply training data in a table, the predictors must be numeric (double
orsingle
). To include categorical predictors in a model, preprocess them by usingdummyvar
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 ParametersInteger 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
( | 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 |
Clear this check box
( | 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 |
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" |
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 ismyMdl
, and the class labels areclass 1
andclass 2
. Then, the corresponding label values aremyMdl_enumLabels.class_1
andmyMdl_enumLabels.class_2
. The block converts the class labels to valid MATLAB identifiers by using thematlab.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 |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
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 R2023bR2024a: 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"
.
See Also
Blocks
Objects
Functions
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