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This page describes how to use the machine learning (aka mining) bundle.

Operators

Ensembles

You can simply combine all operators with other operators in Odysseus to create ensembles.

If we have, for examaple a stream with windspeed and power (speedandpower) and another one containing  the windspeed (e.g. a forecast).

Then, it is possible to create different regressionfunctions, use them and weight the regression results by an aggregation.

One example:

/// create the first classifier by using SMO
smo = CLASSIFICATION_LEARN({
          class='power',
          learner = 'weka',
          algorithm = ['model'='SMO-REGRESSION']
        },
        speedandpower
      )
/// create the second classifier by using gaussian processes
gaussian = CLASSIFICATION_LEARN({
                class='power',
                learner = 'weka',
                algorithm = ['model'='GAUSSIAN-PROCESSES']
              },
              speedandpower
            )
/// create the thirs classifier by using a linear regression
linear = CLASSIFICATION_LEARN({
              class='power',
              learner = 'weka',
              algorithm = ['model'='LINEAR-REGRESSION']
            },
            speedandpower
          )   
/// union them all into one stream
unioned = UNION(smo, gaussian, linear)

/// then, classify them - each tuple will be classified by using all three classifiers
ensemble = CLASSIFY(speed, unioned)

/// aggregate the clazz using average - which allows a weighted kind of voting
agg = AGGREGATE({
          aggregations=[            
            ['AVG', 'clazz', 'powerForecast', 'DOUBLE']
          ]
        },
        ensemble
      )

 

 

Examples

 

to be added

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