perceptronClassifier (Stream Processor)

This extension predicts using a linear binary classification Perceptron model.

Syntax

streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE> model.bias, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE> model.threshold, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE> model.bias, <DOUBLE> model.threshold, <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 used.		STRING	No	No
model.bias	The bias of the Perceptron algorithm.	0.0	DOUBLE	Yes	No
model.threshold	The threshold that separates the two classes. The value specified must be between zero and one.	0.5	DOUBLE	Yes	No
model.feature	The 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
prediction	The predicted value (true/false).	BOOL
confidenceLevel	The probability of the prediction.	DOUBLE

Example 1

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

insert all events into OutputStream
from StreamA#streamingml:perceptronClassifier('model1',0.0,0.5, attribute_0, attribute_1, attribute_2, attribute_3);

This query uses a Perceptron model named model1 with a 0.0 bias and a 0.5 threshold learning rate to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The predicted label (true/false) is emitted to the OutputStream streamalong with the prediction confidence level(probability) and the feature vector. As a result, the OutputStream stream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double).

Example 2

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

insert all events into OutputStream
from StreamA#streamingml:perceptronClassifier('model1',0.0, attribute_0, attribute_1, attribute_2, attribute_3);

This query uses a Perceptron model named model1 with a 0.0 bias to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The prediction(true/false) is emitted to the OutputStreamstream along with the prediction confidence level(probability) and the feature. As a result, the OutputStream stream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double).

Example 3

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

insert all events into OutputStream
from StreamA#streamingml:perceptronClassifier(`model1`, attribute_0, attribute_1, attribute_2);

This query uses a Perceptron model named model1 with a default 0.0 bias to predict the label of the feature vector represented by attribute_0, attribute_1, and attribute_2. The predicted probability is emitted to the OutputStream stream along with the feature vector. As a result, the OutputStream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double).