IncrementalRegressionLinear Predict
Libraries:
Statistics and Machine Learning Toolbox /
Incremental Learning /
Regression /
Linear
Description
The IncrementalRegressionLinear Predict block predicts responses for streaming data using a trained linear regression model returned as the output of an IncrementalRegressionLinear Fit block.
Import an initial linear regression 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 yfit returns predicted responses for the chunk. The optional output port CanPredict returns the prediction status of the trained model.
Examples
Perform Incremental Learning Using IncrementalRegressionLinear Fit and Predict Blocks
Perform incremental learning with the IncrementalRegressionLinear Fit block and predict responses with the IncrementalRegressionLinear Predict block.
- Since R2023b
- Open Live Script
Configure Simulink Template for Rate-Based Incremental Linear Regression
Configure the Simulink Rate-Based Incremental Learning template to perform incremental linear regression.
- Since R2024a
- Open Live Script
Configure Simulink Template for Conditionally Enabled Incremental Linear Regression
Configure the Simulink Enabled Execution Incremental Learning template to perform incremental linear regression.
- Since R2024a
- Open Live Script
Ports
Input
mdl — Incremental learning model
bus signal
Incremental learning model (incrementalRegressionLinear
) 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 numerical 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
yfit — Chunk of predicted responses
floating-point vector
Chunk of predicted responses, returned as a floating-point vector. For more details, see
Predicted response and the YHat
argument of the
predict
object
function.
Note
If you specify an estimation period when you create mdl, then the predicted responses are zero during the estimation period.
Data Types: single
| double
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 regression model
linearMdl
(default) | incrementalRegressionLinear
model object
Specify the name of a workspace variable that contains the configured
incrementalRegressionLinear
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
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:
incrementalRegressionLinear model
object |
Default:
"linearMdl" |
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
IncrementalRegressionLinear Predict block. This check
box does not appear if the workspace already contained an incremental
linear regression model named linearMdl
capable of
prediction when you created the IncrementalRegressionLinear
Predict block. Alternatively, you can specify to include the
output port CanPredict by selecting the
IncrementalRegressionLinear 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 IncrementalRegressionLinear 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" |
Output data type — Data type of yfit 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 yfit 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: OutDataTypeStr |
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" |
Output data type Minimum — Minimum value of yfit output for range checking
[]
(default) | scalar
Specify the lower value of the yfit 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 Output data type Minimum parameter does not saturate or clip the actual yfit output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
OutMin |
Type: character vector |
Values:
"[]" | scalar |
Default:
"[]" |
Output data type Maximum — Maximum value of yfit output for range checking
[]
(default) | scalar
Specify the upper value of the yfit 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 Output data type Maximum parameter does not saturate or clip the actual yfit output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
OutMax |
Type: character vector |
Values:
"[]" | scalar |
Default:
"[]" |
Inner product data type — Inner product data type
double
(default) | Inherit: Inherit via internal rule
| 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
Predicted Response
For linear regression models, the predicted response for the observation x is
y = xβ+b
β is the estimated column vector of coefficients, and
b is the estimated scalar bias. The linear regression model
object specified by Select initial
machine learning model contains the coefficients and bias in the
Beta
and Bias
properties, respectively.
β and b correspond to
Beta
and Bias
, respectively.
You can specify the data types for the components required to compute predicted responses using Output data type and Inner product data type.
Output data type determines the data type of the predicted response.
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 IncrementalRegressionLinear Predict block additionally supports initial machine learning
models where Solver
is "sgd"
or
"asgd"
.
See Also
Blocks
Objects
Functions
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