Examples of cascading.pattern.model.generalregression.GeneralRegressionSpec

Package cascading.pattern.model.generalregression

Examples of cascading.pattern.model.generalregression.GeneralRegressionSpec

cascading.pattern.model.generalregression.GeneralRegressionSpec
Class GeneralRegressionSpec is used to define a general regression model for classification or prediction, and can be used with {@link CategoricalRegressionFunction} and {@link PredictionRegressionFunction} respectively.
General regression applies a {@link RegressionTable} to each Tuple. If only one table is specified, this speccan be used with the PredictionRegressionFunction. If multiple tables are specified, each must have a unique category and should be used with CategoricalRegressionFunction.
By default, no link function or normalization method is used.


    RegressionTable regressionTable = GeneralRegressionUtil.createPPMatrix( model, parameterList, factorsList, covariateList );


    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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    List<String> predictedCategories = new ArrayList<String>( modelSchema.getPredictedCategories( modelSchema.getPredictedFieldNames().get( 0 ) ) );


    if( predictedCategories.isEmpty() )
      throw new PatternException( "no categories specified" );


    GeneralRegressionSpec regressionSpec = new GeneralRegressionSpec( modelSchema );


    regressionSpec.setNormalization( RegressionUtil.getNormalizationMethod( model ) );


    for( org.dmg.pmml.RegressionTable regressionTable : model.getRegressionTables() )
      regressionSpec.addRegressionTable( RegressionUtil.createTable( regressionTable ) );


    return create( tail, modelSchema, new CategoricalRegressionFunction( regressionSpec ) );
    }

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    ModelSchema modelSchema = createModelSchema( model );


    org.dmg.pmml.RegressionTable regressionTable = model.getRegressionTables().get( 0 );


    GeneralRegressionSpec regressionSpec = new GeneralRegressionSpec( modelSchema );


    regressionSpec.setLinkFunction( LinkFunction.NONE );


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


    return create( tail, modelSchema, new PredictionRegressionFunction( regressionSpec ) );
    }

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      .append( new Fields( "petal_length", double.class ) )
      .append( new Fields( "petal_width", double.class ) );


    ModelSchema modelSchema = new ModelSchema( expectedFields, predictedFields );


    GeneralRegressionSpec regressionSpec = new GeneralRegressionSpec( modelSchema );


    regressionSpec.setLinkFunction( LinkFunction.LOGIT );


    RegressionTable table = new RegressionTable();


    table.addParameter( new Parameter( "p0", -16.9456960387809d ) );
    table.addParameter( new Parameter( "p1", 11.7592159418536d, new CovariantPredictor( "sepal_length" ) ) );
    table.addParameter( new Parameter( "p2", 7.84157781514097d, new CovariantPredictor( "sepal_width" ) ) );
    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 ) );

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      .append( new Fields( "petal_width", double.class ) )
      .append( new Fields( "species", String.class ) );


    ModelSchema modelSchema = new ModelSchema( expectedFields, predictedFields );


    GeneralRegressionSpec regressionSpec = new GeneralRegressionSpec( modelSchema );


    RegressionTable regressionTable = new RegressionTable();


    regressionTable.addParameter( new Parameter( "intercept", 2.24166872421148d ) );


    regressionTable.addParameter( new Parameter( "p1", 0.53448203205212d, new CovariantPredictor( "sepal_width" ) ) );
    regressionTable.addParameter( new Parameter( "p2", 0.691035562908626d, new CovariantPredictor( "petal_length" ) ) );
    regressionTable.addParameter( new Parameter( "p3", -0.21488157609202d, new CovariantPredictor( "petal_width" ) ) );


    regressionTable.addParameter( new Parameter( "p4", 0d, new FactorPredictor( "species", "setosa" ) ) );
    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" ) );

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    ModelSchema modelSchema = new ModelSchema( expectedFields, predictedFields );


    modelSchema.setPredictedCategories( "species", "setosa", "versicolor", "virginica" );


    GeneralRegressionSpec regressionSpec = new GeneralRegressionSpec( modelSchema );


    regressionSpec.setNormalization( new SoftMaxNormalization() );


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


    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 );
    }


    {
    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 );
    }


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


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


    regressionSpec.addRegressionTable( regressionTable );
    }


    CategoricalRegressionFunction regressionFunction = new CategoricalRegressionFunction( regressionSpec );


    {

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Related Classes of cascading.pattern.model.generalregression.GeneralRegressionSpec

cascading.pattern.datafield.DataField

cascading.pattern.model.ModelTest

cascading.pattern.pmml.PMMLPlanner

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