Contents
This sample demonstrates the use of the Spotfire Streaming Multilayer Perceptron Classification operator.
The provided StreamBase modules for these operators use the IRIS Flower data - irisdat.csv. SEPALLEN, SEPALLWID, PETALLEN, PETALWID features are selected as predictors. IRISTYPE is selected as the response. Data is fed into the Matrix operator to be collected and emitted every 100 rows. Once the matrix operator has collected the required number of rows of data, the SetReady output stream sets a dynamic variable indicating the Threshold condition has been met. The first N tuples are not scored because the operator is yet to be trained. This operator uses the collected data and options (from the provided schema) as inputs.
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In StreamBase Studio, import this sample with the following steps:
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From the top-level menu, click >.
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In the search field, type
mlpclassificationto narrow the list of options. -
Select Multilayer Perceptron (MLP) neural network classification from the Streaming Datascience Operators category.
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Click .
StreamBase Studio creates a single project containing the sample files.
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In the Project Explorer view, double-click to open the mlpclassification application. Make sure the application is the currently active tab in the EventFlow Editor.
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Click the Run button. This opens the SB Test/Debug perspective and starts the application.
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Click on the Feed Simulations tab, click the
mlpclassification.sbfs, then click the Run button to start feeding the data. -
The Multilayer Perceptron Classification operator starts taking data from the feed simulation and emitting the results after 100 rows are collected.
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When done, press F9 or click the Stop Running Application button.
