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);
CREATE SINK STREAM OutputStreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double);
@info(name = 'perceptronClassifierQuery1')
INSERT ALL EVENTS INTO OutputStreamA
SELECT attribute_0, attribute_1, attribute_2, attribute_3, prediction, confidenceLevel
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 to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The predicted label (true/false) and the prediction confidence level (probability) are emitted to the OutputStreamA stream along with the feature vector.
Example 2
CREATE STREAM StreamB (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double);
CREATE SINK STREAM OutputStreamB (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double);
@info(name = 'perceptronClassifierQuery2')
INSERT ALL EVENTS INTO OutputStreamB
SELECT attribute_0, attribute_1, attribute_2, attribute_3, prediction, confidenceLevel
FROM StreamB#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 and the default threshold to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The predicted label (true/false) and the prediction confidence level (probability) are emitted to the OutputStreamB stream along with the feature vector.
Example 3
CREATE STREAM StreamC (attribute_0 double, attribute_1 double, attribute_2 double);
CREATE SINK STREAM OutputStreamC (attribute_0 double, attribute_1 double, attribute_2 double, prediction bool, confidenceLevel double);
@info(name = 'perceptronClassifierQuery3')
INSERT ALL EVENTS INTO OutputStreamC
SELECT attribute_0, attribute_1, attribute_2, prediction, confidenceLevel
FROM StreamC#streamingml:perceptronClassifier('model1', attribute_0, attribute_1, attribute_2);
This query uses a Perceptron model named model1 with default bias and threshold to predict the label of the feature vector represented by attribute_0, attribute_1, and attribute_2. The predicted label (true/false) and the prediction confidence level (probability) are emitted to the OutputStreamC stream along with the feature vector.
Example 4
CREATE STREAM StreamD (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double);
CREATE SINK STREAM OutputStreamD (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double, prediction bool, confidenceLevel double);
@info(name = 'perceptronClassifierQuery4')
INSERT ALL EVENTS INTO OutputStreamD
SELECT attribute_0, attribute_1, attribute_2, attribute_3, attribute_4, prediction, confidenceLevel
FROM StreamD#streamingml:perceptronClassifier('model1', 0.1, 0.4, attribute_0, attribute_1, attribute_2, attribute_3, attribute_4);
This query uses a Perceptron model named model1 with a 0.1 bias and a 0.4 threshold to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, attribute_3, and attribute_4. The predicted label (true/false) and the prediction confidence level (probability) are emitted to the OutputStreamD stream along with the feature vector.