AdaBoost

An implementation of the AdaBoost MH (Adaptive Boosting) algorithm for classification.

%% kleines Nutzung Beispiel

Available Predicates

links/resources

initModelWithTraining/9

Needs to be called first before all other predicates exept initModelNoTraining!

Initiates the Adaboostmodel and trains it, so classify can be used immediately.

initModelWithTraining(  +pointer(float_array), +integer, +integer, 
                        +pointer(float_array), +integer, 
                        +integer, 
                        +string, 
                        +integer , +float32).

Parameters

Name	Type	Description	Default
data	+matrix	Dataset for training AdaBoost	-
labels	+vector	Labels for the training set	-
numClasses	+integer	The number of classes to classify data into	-
learner	+string	The type of weak learner to use: “decision_stump”, or “perceptron”	“decision_stump”
iterations	+integer	The maximum number of boosting iterations to be run (0 will run until convergence.)	100
tolerance	+float	The tolerance for change in values of the weighted error during training	1e-6

initModelNoTraining/2

Needs to be called first before all other predicates exept initModelWithTraining!

Initiates the Adaboostmodel but doesn’t train it, so train has to be used first before classify can be used.

%% part of the predicate definition
initModelNoTraining(+string, +float32).

Parameters

Name	Type	Description	Default
tolerance	+float	The tolerance for change in values of the weighted error during training	1e-6
learner	+string	The type of weak learner to use: “decision_stump”, or “perceptron”	“decision_stump”

classify/8

Classifies the given data into the number of classes the model was trained for.

%% part of the predicate definition 
classify( +pointer(float_array), +integer, +integer, 
          -pointer(float_array), -integer, 
          -pointer(float_array), -integer, -integer).

Parameters

Name	Type	Description	Default
test	+matrix	Test dataset	-
predictions	-vector	Predicted labels for the test set	-
probabilities	-matrix	Predicted class probabilities for each point in the test set	-

numClasses/1

Returns the amount of classes defined in the model for classification.

%% part of the predicate definition 
numClasses([-integer]).

Parameters

Name	Type	Description	Default
classesNum	-integer	Number of classes	-

getTolerance/1

Returns the tolerance of the model.

%% part of the predicate definition
getTolerance([-float32]).

Parameters

Name	Type	Description	Default
tolerance	-float32	returns the current tolerance	-

modifyTolerance/1

Modifies the tolerance of the model.

%% part of the predicate definition 
modifyTolerance(+float32).

Parameters

Name	Type	Description	Default
tolerance	+float32	changes the tolerance	1e-6

train/10

Kurze beschreibung mit evt. hinweisen

%% part of the predicate definition 
train(  +pointer(float_array), +integer, +integer, 
        +pointer(float_array), +integer, 
        +integer, 
        +string, 
        +integer , +float32, 
        [-float32]).

Parameters

Name	Type	Description	Default
data	+matrix	Dataset for training AdaBoost	-
labels	+vector	Labels for the training set	-
numClasses	+integer	The number of classes to classify data into	-
learner	+string	The type of weak learner to use: “decision_stump”, or “perceptron”	“decision_stump”
iterations	+integer	The maximum number of boosting iterations to be run (0 will run until convergence.)	100
tolerance	+float	The tolerance for change in values of the weighted error during training	1e-6
error	-float	double upper bound training error	-

Connected Links/Resources

If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and about what the parameters do.

added some of the links from the python documentation

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