Package cascading.pattern.model.generalregression

Examples of cascading.pattern.model.generalregression.RegressionTable.addParameter()

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    regressionTable.addParameter( new Parameter( "intercept", 86.7061379450354d ) );
    regressionTable.addParameter( new Parameter( "p0", -11.3336819785783d, new CovariantPredictor( "sepal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p1", -40.8601511206805d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 38.439099544679d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", -12.2920287460217d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( regressionTable );
    }

    {
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    }

    {
    RegressionTable regressionTable = new RegressionTable( "virginica" );

    regressionTable.addParameter( new Parameter( "intercept", -111.666532867146d ) );
    regressionTable.addParameter( new Parameter( "p0", -47.1170644419116d, new CovariantPredictor( "sepal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p1", -51.6805606658275d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 108.27736751831d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", 54.0277175236148d, new CovariantPredictor( "petal_width" ) ) );
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    {
    RegressionTable regressionTable = new RegressionTable( "virginica" );

    regressionTable.addParameter( new Parameter( "intercept", -111.666532867146d ) );
    regressionTable.addParameter( new Parameter( "p0", -47.1170644419116d, new CovariantPredictor( "sepal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p1", -51.6805606658275d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 108.27736751831d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", 54.0277175236148d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( regressionTable );
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    {
    RegressionTable regressionTable = new RegressionTable( "virginica" );

    regressionTable.addParameter( new Parameter( "intercept", -111.666532867146d ) );
    regressionTable.addParameter( new Parameter( "p0", -47.1170644419116d, new CovariantPredictor( "sepal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p1", -51.6805606658275d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 108.27736751831d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", 54.0277175236148d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( regressionTable );
    }
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    RegressionTable regressionTable = new RegressionTable( "virginica" );

    regressionTable.addParameter( new Parameter( "intercept", -111.666532867146d ) );
    regressionTable.addParameter( new Parameter( "p0", -47.1170644419116d, new CovariantPredictor( "sepal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p1", -51.6805606658275d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 108.27736751831d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", 54.0277175236148d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( regressionTable );
    }
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    regressionTable.addParameter( new Parameter( "intercept", -111.666532867146d ) );
    regressionTable.addParameter( new Parameter( "p0", -47.1170644419116d, new CovariantPredictor( "sepal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p1", -51.6805606658275d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 108.27736751831d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", 54.0277175236148d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( regressionTable );
    }

    {
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    }

    {
    RegressionTable regressionTable = new RegressionTable( "setosa" );

    regressionTable.addParameter( new Parameter( "intercept", 0d ) );

    regressionSpec.addRegressionTable( regressionTable );
    }

    CategoricalRegressionFunction regressionFunction = new CategoricalRegressionFunction( regressionSpec );
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