updatePerceptronClassifier (Stream Processor)

This extension builds/updates a linear binary classification Perceptron model.

Syntax

streamingml:updatePerceptronClassifier(<STRING> model.name, <BOOL|STRING> model.label, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updatePerceptronClassifier(<STRING> model.name, <BOOL|STRING> model.label, <DOUBLE> learning.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)

Query Parameters

Name	Description	Default Value	Possible Data Types	Optional	Dynamic
model.name	The name of the model to be built/updated.		STRING	No	No
model.label	The attribute of the label or the class of the dataset.		BOOL STRING	No	Yes
learning.rate	The learning rate of the Perceptron algorithm.	0.1	DOUBLE	Yes	No
model.feature	Features of the model that need to be attributes of the stream.		DOUBLE FLOAT INT LONG	No	Yes

Extra Return Attributes

Name	Description	Possible Types
featureWeight	Weight of the feature.name of the model.	DOUBLE

Example 1

CREATE STREAM StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 string );

insert all events into outputStream
from StreamA#streamingml:updatePerceptronClassifier('model1', attribute_4, 0.01, attribute_0, attribute_1, attribute_2, attribute_3);

This query builds/updates a Perceptron model named model1 with a 0.01 learning rate using attribute_0, attribute_1, attribute_2, and attribute_3 as features, and attribute_4 as the label. Updated weights of the model are emitted to the OutputStream stream.

Example 2

CREATE STREAM StreamA (attribute_0 double, attribute_1 double, attribute_2 double,attribute_3 double, attribute_4 string );

insert all events into outputStream
from StreamA#streamingml:updatePerceptronClassifier('model1', attribute_4, attribute_0, attribute_1, attribute_2, attribute_3);

This query builds/updates a Perceptron model named model1 with a default 0.1 learning rate using attribute_0, attribute_1, attribute_2, and attribute_3 as features, and attribute_4 as the label. The updated weights of the model are appended to the outputStream.