ClassificationECOC Predict
Classify observations using error-correcting output codes (ECOC) classification model
Since R2023a
Libraries:
Statistics and Machine Learning Toolbox /
Classification
Description
The ClassificationECOC Predict block classifies observations using an
error-correcting output codes (ECOC) classification model (ClassificationECOC
or CompactClassificationECOC
) for multiclass
classification.
Import a trained classification object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port label returns predicted class labels for the observation. You can add optional output ports score and pbscore, where score returns predicted class scores (negated average binary losses), and pbscore returns positive-class scores for binary learners.
Examples
Predict Class Labels Using ClassificationECOC Predict Block
Train an ECOC classification model, and then use the ClassificationECOC Predict block for label prediction.
- Since R2023a
- Open Live Script
Ports
Input
x — Predictor data
row vector | column vector
Predictor data, specified as a row or column vector of one observation.
The variables in x must have the same order as the predictor variables that trained the model specified by Select trained machine learning model.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Output
label — Predicted class label
scalar
Predicted class label, returned as a scalar. label is the
class yielding the highest score. For more details, see the label
argument of the predict
object function.
The block supports two decoding schemes that specify how the block aggregates the binary losses to compute the classification scores, and how the block determines the predicted class for each observation. For details, see Decoding scheme and Binary Loss and Decoding Scheme.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
| enumerated
score — Predicted class scores or posterior probabilities
row vector
Predicted class scores (negated average binary losses) or posterior probabilities, returned as a row vector of size 1-by-K, where K is the number of classes in the ECOC model.
To check the order of the classes, use the ClassNames
property of the model specified by Select trained machine
learning model.
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.
The block supports two decoding schemes that specify how the block aggregates the binary losses to compute the classification scores, and how the block determines the predicted class for each observation. For details, see Decoding scheme and Binary Loss and Decoding Scheme.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| fixed point
pbscore — Positive-class scores of binary learners
row vector
Positive-class scores of binary learners, returned as a row vector of size 1-by-B, where B is the number of binary learners in the ECOC model.
To check the class assignment codes for the binary learners, use the
CodingMatrix
property of the model specified by Select trained machine
learning model. For more details, see Coding Design
of a ClassificationECOC
object.
Dependencies
To enable this port, select the check box for Add output port for positive-class scores of binary learners on the Main tab of the Block Parameters dialog box.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| fixed point
Parameters
Main
Select trained machine learning model — ECOC classification model
ecocMdl
(default) | ClassificationECOC
object | CompactClassificationECOC
object
Specify the name of a workspace variable that contains a ClassificationECOC
or CompactClassificationECOC
model object.
When you train the model by using fitcecoc
, the following restrictions apply:
You must train an ECOC model using either SVM learners or linear learners.
For SVM learners, you can specify the
Learners
name-value argument as"svm"
, an SVM template object created by usingtemplateSVM
, or a cell array of SVM template objects.For linear learners, you can specify the
Learners
name-value argument as"linear"
, a linear template object created by usingtemplateLinear
, or a cell array of linear template objects. TheLambda
value (regularization term strength) of the template object must be a numeric scalar.
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
). Also, you cannot use theCategoricalPredictors
name-value argument. To include categorical predictors in a model, preprocess them by usingdummyvar
before fitting the model.
Programmatic Use
Block Parameter:
TrainedLearner |
Type: workspace variable |
Values:
ClassificationECOC object |
CompactClassificationECOC object |
Default:
'ecocMdl' |
Add output port for predicted class scores (negated average binary losses) — Add second output port
off
(default) | on
Select the check box to include the second output port score in the ClassificationECOC Predict block.
Programmatic Use
Block Parameter:
ShowOutputScore |
Type: character vector |
Values:
'off' | 'on' |
Default:
'off' |
Add output port for positive-class scores of binary learners — Add third output port
off
(default) | on
Select the check box to include the third output port pbscore in the ClassificationECOC Predict block.
Programmatic Use
Block Parameter:
ShowOutputPBScore |
Type: character vector |
Values:
'off' | 'on' |
Default:
'off' |
Binary learner loss function — Binary learner loss function
hinge
(default) | binodeviance
| exponential
| hamming
| linear
| logit
| quadratic
Specify the binary learner loss function as
binodeviance
, exponential
,
hamming
, hinge
,
linear
, logit
, or
quadratic
.
The recommended binary loss function depends on the score ranges returned by the binary learners. The following table lists some common cases:
Description | Recommended Function |
---|---|
All binary learners are linear classification models of logistic regression learners. | quadratic |
All binary learners are SVMs or linear classification models of SVM learners. | hinge |
You specify to predict class posterior probabilities by setting
when you train the ECOC
model. | quadratic |
For definitions of the loss functions, see Binary Loss and Decoding Scheme.
Programmatic Use
Block Parameter:
BinaryLoss |
Type: character vector |
Values:
'binodeviance' | 'exponential' |
'hamming' | 'hinge' |
'linear' | 'logit' |
'quadratic' |
Default:
'hinge' |
Decoding scheme — Decoding scheme
lossweighted
(default) | lossbased
Specify the decoding scheme that aggregates the binary losses as
lossweighted
or
lossbased
.
The definition of the score values depends on the Decoding scheme value.
If you specify
lossweighted
, then the kth element in score is the sum of the binary losses divided by the number of binary learners for the kth class.If you specify
lossbased
, then the kth element in score is the sum of the binary losses divided by the total number of binary learners.
For more details, see Binary Loss and Decoding Scheme.
Programmatic Use
Block Parameter:
Decoding |
Type: character vector |
Values:
'lossweighted' | 'lossbased' |
Default:
'lossweighted' |
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
| 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>
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 trained 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
Inherit: auto
(default),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 trained 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" | "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: 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 trained 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 trained 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: "[]" |
Positive-class score data type — Positive-class score data type
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 pbscore output. This data type also determines the data type for the classification scores of binary learners. 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: PBScoreDataTypeStr |
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" |
Positive-class score data type Minimum — Minimum value of pbscore
output for range checking
[]
(default) | scalar
Specify the lower value of the pbscore 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 Positive-class score data type Minimum parameter does not saturate or clip the actual pbscore signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter: PBScoreOutMin |
Type: character vector |
Values: '[]' | scalar |
Default: '[]' |
Positive-class score data type Maximum — Maximum value of pbscore
output for range checking
[]
(default) | scalar
Specify the upper value of the pbscore 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 Positive-class score data type Maximum parameter does not saturate or clip the actual pbscore signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter: PBScoreOutMax |
Type: character vector |
Values: '[]' | scalar |
Default: '[]' |
Kernel data type — Binary learner kernel computation data type
double
(default) | single
| half
| int8
| uint8
| int16
| uint16
| int32
| int64
| uint64
| uint32
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type of a parameter for kernel computation of binary learners.
The type can be 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).
Dependencies
You can specify this parameter only if the model specified by Select trained machine learning model uses SVM learners. If the model uses linear learners, then specify Inner product data type instead.
The Kernel data type parameter specifies the data type of a
different parameter depending on the type of kernel function of the specified SVM
learners. You specify the kernel function type by using the KernelFunction
name-value argument of the templateSVM
function. You must pass
the output of templateSVM
as the value for the Learners
name-value
argument of the fitcecoc
function.
KernelFunction Value | Data Type |
---|---|
'gaussian' or 'rbf' | The parameter specifies the data type of the squared distance for the Gaussian kernel , where x is the predictor data for an observation and s is a support vector. |
'linear' | The parameter specifies the data type for the output of the linear kernel function , where x is the predictor data for an observation and s is a support vector. |
'polynomial' | The parameter specifies the data type for the output of the polynomial kernel function , where x is the predictor data for an observation, s is a support vector, and p is a polynomial kernel function order. |
Programmatic Use
Block Parameter:
KernelDataTypeStr |
Type: character vector |
Values: 'double' |
'single' | 'half' |
'int8' | 'uint8' |
'int16' | 'uint16' |
'int32' | 'uint32' |
'uint64' | 'int64' |
'boolean' | 'fixdt(1,16,0)' |
'fixdt(1,16,2^0,0)' | '<data type
expression>' |
Default:
'double' |
Kernel data type Minimum — Minimum kernel computation value for range checking
[]
(default) | scalar
Specify the lower value of the kernel computation internal variable 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 Kernel data type Minimum parameter does not saturate or clip the actual kernel computation value signal.
Programmatic Use
Block Parameter:
KernelOutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Kernel data type Maximum — Maximum kernel computation value for range checking
[]
(default) | scalar
Specify the upper value of the kernel computation internal variable 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 Kernel data type Maximum parameter does not saturate or clip the actual kernel computation value signal.
Programmatic Use
Block Parameter:
KernelOutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Inner product data type — Inner product data type for binary learners
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 classification score of
binary learners. 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 output data type. The internal rule
chooses a data type that optimizes numerical accuracy, performance, and generated code
size, while considering 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).
Dependencies
You can specify this parameter only if the model specified by Select trained machine learning model uses linear learners. If the model uses SVM learners, then specify Kernel data type instead.
For linear classification learners, the 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. Each linear classification
learner in the ECOC model object contains the coefficients and bias in the
Beta
and Bias
properties,
respectively.
If the model consists of logistic regression learners, then the software applies
the 'logit'
score transformation to the classification scores of
binary learners. The classification score for classifying x into
the negative class is –f(x). The software
classifies observations into the class that yields a positive score for each binary
learner.
Use Inner product data type to determine the data type of xβ, and use Positive-class score data type to determine the data type of the classification scores of binary learners.
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: 'Inherit: Inherit via
internal rule' |
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 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 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
Binary Loss and Decoding Scheme
The binary loss is a function of the class and classification score that determines how well a binary learner classifies an observation into the class. The decoding scheme of an ECOC model specifies how the software aggregates the binary losses and determines the predicted class for each observation.
Assume the following:
mkj is element (k,j) of the coding design matrix M—that is, the code corresponding to class k of binary learner j. M is a K-by-B matrix, where K is the number of classes, and B is the number of binary learners.
sj is the score of binary learner j for an observation.
g is the binary loss function.
is the predicted class for the observation.
The ClassificationECOC Predict block supports two decoding schemes:
Loss-based decoding [2] (Decoding scheme is
lossbased
) — The predicted class of an observation corresponds to the class that produces the minimum average of the binary losses over all binary learners.Loss-weighted decoding [3] (Decoding scheme is
lossweighted
) — The predicted class of an observation corresponds to the class that produces the minimum average of the binary losses over the binary learners for the corresponding class.The denominator corresponds to the number of binary learners for class k. As suggested in [1], loss-weighted decoding improves classification accuracy by keeping loss values for all classes in the same dynamic range.
The block returns the negated value of the objective function of
argmin
as the second output port (score
) for each
observation and class.
This table summarizes the supported binary loss functions, where yj is a class label for a particular binary learner (in the set {–1,1,0}), sj is the score for observation j, and g(yj,sj) is the binary loss function. You can specify a binary loss function by using Binary learner loss function.
Value | Description | Score Domain | g(yj,sj) |
---|---|---|---|
"binodeviance" | Binomial deviance | (–∞,∞) | log[1 + exp(–2yjsj)]/[2log(2)] |
"exponential" | Exponential | (–∞,∞) | exp(–yjsj)/2 |
"hamming" | Hamming | [0,1] or (–∞,∞) | [1 – sign(yjsj)]/2 |
"hinge" | Hinge | (–∞,∞) | max(0,1 – yjsj)/2 |
"linear" | Linear | (–∞,∞) | (1 – yjsj)/2 |
"logit" | Logistic | (–∞,∞) | log[1 + exp(–yjsj)]/[2log(2)] |
"quadratic" | Quadratic | [0,1] | [1 – yj(2sj – 1)]2/2 |
The software normalizes binary losses so that the loss is 0.5 when yj = 0, and aggregates using the average of the binary learners [1].
Alternative Functionality
You can use a MATLAB Function block with the predict
object function of an ECOC classification object (ClassificationECOC
or CompactClassificationECOC
). For an example, see Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the ClassificationECOC Predict block in the
Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict
function, consider the
following:
If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.
Support for variable-size arrays must be enabled for a MATLAB Function block with the
predict
function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.
References
[1] Allwein, E., R. Schapire, and Y. Singer. “Reducing multiclass to binary: A unifying approach for margin classifiers.” Journal of Machine Learning Research. Vol. 1, 2000, pp. 113–141.
[2] Escalera, S., O. Pujol, and P. Radeva. “Separability of ternary codes for sparse designs of error-correcting output codes.” Pattern Recog. Lett. Vol. 30, Issue 3, 2009, pp. 285–297.
[3] Escalera, S., O. Pujol, and P. Radeva. “On the decoding process in ternary error-correcting output codes.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 32, Issue 7, 2010, pp. 120–134.
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 R2023a
See Also
Blocks
Objects
Functions
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