Package cascading.pattern.model.generalregression

Examples of cascading.pattern.model.generalregression.PredictionRegressionFunction


    LinkFunction linkFunction = LinkFunction.getFunction( model.getLinkFunction().value() );

    GeneralRegressionSpec modelParam = new GeneralRegressionSpec( modelSchema, regressionTable, linkFunction );

    return create( tail, modelSchema, new PredictionRegressionFunction( modelParam ) );
    }
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    regressionSpec.setLinkFunction( LinkFunction.NONE );

    regressionSpec.addRegressionTable( RegressionUtil.createTable( regressionTable ) );

    return create( tail, modelSchema, new PredictionRegressionFunction( regressionSpec ) );
    }
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    table.addParameter( new Parameter( "p3", -20.0880078273996d, new CovariantPredictor( "petal_length" ) ) );
    table.addParameter( new Parameter( "p4", -21.6076488529538d, new CovariantPredictor( "petal_width" ) ) );

    regressionSpec.addRegressionTable( table );

    PredictionRegressionFunction regressionFunction = new PredictionRegressionFunction( regressionSpec );

    TupleEntry tupleArguments = new TupleEntry( expectedFields, new Tuple( 5.1d, 3.8d, 1.6d, 0.2d ) );

    TupleListCollector collector = invokeFunction( regressionFunction, tupleArguments, predictedFields );
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    regressionTable.addParameter( new Parameter( "p5", -0.43150751368126d, new FactorPredictor( "species", "versicolor" ) ) );
    regressionTable.addParameter( new Parameter( "p6", -0.61868924203063d, new FactorPredictor( "species", "virginica" ) ) );

    regressionSpec.addRegressionTable( regressionTable );

    PredictionRegressionFunction regressionFunction = new PredictionRegressionFunction( regressionSpec );

    TupleEntry tupleArguments = new TupleEntry( expectedFields, new Tuple( 3d, 1.3d, 0.2d, "setosa" ) );

    TupleListCollector collector = invokeFunction( regressionFunction, tupleArguments, predictedFields );
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Related Classes of cascading.pattern.model.generalregression.PredictionRegressionFunction

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