IncrementalClassificationLinear Fit
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
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(
is the
response of observation j (row or column) in
x.j
)
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 fromNumPredictors
, 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
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 IncrementalClassificationLinear Fit block supports binary classification only.
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 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
mdl — Updated incremental learning model parameters
bus signal
Updated parameters of the incremental learning model fit to streaming data (including
Beta
and Bias
), returned as a bus signal (see Composite
Signals
(Simulink)).
Parameters
Main
Select initial machine learning model — Initial incremental linear 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 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 IncrementalClassificationLinear Fit block.
Programmatic Use
Block Parameter:
ShowInputWeights |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Predictor data observation dimension — Observation dimension of predictor data
rows
(default) | columns
Specify the observation dimension of the predictor data. The default value is
rows
, which indicates that observations in the predictor data are
oriented along the rows of x.
Programmatic Use
Block Parameter:
ObservationsIn |
Type: character vector |
Values:
"rows" | "columns" |
Default:
"rows" |
Sample time (–1 for inherited) — Option to specify sample time
–1
(default) | scalar
Specify the discrete interval between sample time hits or specify another type of sample
time, such as continuous (0
) or inherited (–1
). For more
options, see Types of Sample Time (Simulink).
By default, the 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 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 (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" |
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:
BetaDataTypeStr |
Type: character vector |
Values:
"Inherit: auto" | "double" |
"single" | "half" | "int8" |
"uint8" | "int16" | "uint16" |
"int32" | "uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | |
"<data type expression>" |
Default:
"Inherit: auto"
|
Beta data type Minimum — Minimum value of beta for range checking
[]
(default) | scalar
Specify the lower value of the beta output range that Simulink® checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Beta data type Minimum parameter does not saturate or clip the actual beta output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
BetaOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Beta data type Maximum — Maximum value of beta for range checking
[]
(default) | scalar
Specify the upper value of the beta output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Beta data type Maximum parameter does not saturate or clip the actual beta output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
BetaOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Bias data type — Data type of intercept estimates output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the intercept estimates (bias) output. The
type can be inherited, specified as an enumerated data
type, or expressed as a data
type object such as Simulink.NumericType
.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block
Parameter:
BiasDataTypeStr |
Type: character vector |
Values:
"Inherit: auto" | "double" |
"single" | "half" | "int8" |
"uint8" | "int16" | "uint16" |
"int32" | "uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" | |
"<data type expression>" |
Default:
"Inherit: auto"
|
Bias data type Minimum — Minimum value of bias for range checking
[]
(default) | scalar
Specify the lower value of the bias output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Bias data type Minimum parameter does not saturate or clip the actual bias output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
BiasOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Bias data type Maximum — Maximum value of bias for range checking
[]
(default) | scalar
Specify the upper value of the bias output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Bias data type Maximum parameter does not saturate or clip the actual bias output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
BiasOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Internal states data type — Data type of internal states output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the internal states output. The type can be inherited,
specified as an enumerated data type, or expressed as
a data type object such as Simulink.NumericType
.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
StatesDataTypeStr |
Type: character vector |
Values: "Inherit: auto"
| "double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"
| | "<data type expression>" |
Default: "Inherit: auto"
|
Internal states data type Minimum — Minimum value of internal states for range checking
[]
(default) | scalar
Specify the lower value of the internal states output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Internal states data type Minimum parameter does not saturate or clip the actual internal states output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
StatesOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Internal states data type Maximum — Maximum value of internal states for range checking
[]
(default) | scalar
Specify the upper value of the internal states output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Internal states data type Maximum parameter does not saturate or clip the actual internal states output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
StatesOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Prior data type — Data type of prior output
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 prior 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: PriorDataTypeStr |
Type: character vector |
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: "Inherit: auto" |
Prior data type Minimum — Minimum value of prior for range checking
[]
(default) | scalar
Specify the lower value of the prior 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 Prior data type Minimum parameter does not saturate or clip the actual prior output. 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 for range checking
[]
(default) | scalar
Specify the upper value of the prior 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 Prior data type Maximum parameter does not saturate or clip the actual prior output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter: PriorOutMax |
Type: character vector |
Values: "[]" | scalar |
Default: "[]" |
Mu data type — Data type of mu output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the mu (predictor means) output. The type
can be inherited, specified as an enumerated data
type, or expressed as a data
type object such as Simulink.NumericType
.
If you do not specify Standardize
="true"
when you
create the initial model mdl, then the 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 |
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"
|
Mu data type Minimum — Minimum value of mu for range checking
[]
(default) | scalar
Specify the lower value of the mu output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Mu data type Minimum parameter does not saturate or clip the actual mu output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
MuOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Mu data type Maximum — Maximum value of mu for range checking
[]
(default) | scalar
Specify the upper value of the mu output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Mu data type Maximum parameter does not saturate or clip the actual mu output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
MuOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Sigma data type — Data type of sigma output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| Enum: <class name>
| <data type expression>
Specify the data type for the sigma (predictor standard deviations)
output. The type can be inherited, specified as an enumerated
data type, or expressed as a
data type object such as
Simulink.NumericType
.
If you do not specify Standardize
=true
when you
create the initial model mdl, then the 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 |
Values:
"Inherit: auto" | "double" |
"single" | "half" | "int8" |
"uint8" | "int16" | "uint16" |
"int32" | "uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" |
"Enum: <class name>" | "<data type
expression>" |
Default:
"Inherit: auto"
|
Sigma data type Minimum — Minimum value of sigma for range checking
[]
(default) | scalar
Specify the lower value of the sigma output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Sigma data type Minimum parameter does not saturate or clip the actual sigma output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
SigmaOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Sigma data type Maximum — Maximum value of sigma for range checking
[]
(default) | scalar
Specify the upper value of the sigma output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Sigma data type Maximum parameter does not saturate or clip the actual sigma output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
SigmaOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
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
The IncrementalClassificationLinear Fit block now additionally supports initial machine learning models
where Solver
is "sgd"
or
"asgd"
.
See Also
Blocks
Objects
Functions
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
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