How to use

RegressionTable

in

cascading.pattern.model.generalregression

@Override
public Comparable apply( RegressionTable regressionTable )
 {
 return categories.indexOf( regressionTable.getTargetCategory() );
 }
} );

ExpressionEvaluator bind( Fields argumentFields )
 {
 if( isNoOp() )
  return new ExpressionEvaluator( targetCategory );
 ParameterExpression[] expressions = new ParameterExpression[ parameters.size() ];
 int count = 0;
 for( Parameter parameter : parameters.values() )
  expressions[ count++ ] = parameter.createExpression( argumentFields );
 return new ExpressionEvaluator( targetCategory, expressions );
 }

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" ) ) );

public ExpressionEvaluator[] getRegressionTableEvaluators( Fields argumentFields )
 {
 List<RegressionTable> tables = new ArrayList<RegressionTable>( regressionTables );
 final DataField predictedField = getModelSchema().getPredictedField( getModelSchema().getPredictedFieldNames().get( 0 ) );
 // order tables in category order as this is the declared field name order
 if( predictedField instanceof CategoricalDataField )
  {
  Ordering<RegressionTable> ordering = Ordering.natural().onResultOf( new Function<RegressionTable, Comparable>()
  {
  private List<String> categories = ( (CategoricalDataField) predictedField ).getCategories();
  @Override
  public Comparable apply( RegressionTable regressionTable )
   {
   return categories.indexOf( regressionTable.getTargetCategory() );
   }
  } );
  Collections.sort( tables, ordering );
  }
 ExpressionEvaluator[] evaluators = new ExpressionEvaluator[ tables.size() ];
 for( int i = 0; i < tables.size(); i++ )
  evaluators[ i ] = tables.get( i ).bind( argumentFields );
 return evaluators;
 }

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

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" ) ) );