public double calculate( TupleEntry tupleEntry ) { return predictor.calculate( tupleEntry.getDouble( index ) ); } }
public boolean applies( TupleEntry tupleEntry ) { return predictor.matches( tupleEntry.getString( index ) ); } }
public ParameterExpression( Fields argumentsFields, Parameter parameter ) { this.name = parameter.getName(); this.beta = parameter.getBeta(); factorInvokers = new FactorInvoker[ parameter.getFactors().size() ]; for( int i = 0; i < parameter.getFactors().size(); i++ ) { FactorPredictor predictor = parameter.getFactors().get( i ); int pos = argumentsFields.getPos( predictor.getFieldName() ); factorInvokers[ i ] = new FactorInvoker( pos, predictor ); } covariantInvokers = new CovariantInvoker[ parameter.getCovariants().size() ]; for( int i = 0; i < parameter.getCovariants().size(); i++ ) { CovariantPredictor predictor = parameter.getCovariants().get( i ); int pos = argumentsFields.getPos( predictor.getFieldName() ); covariantInvokers[ i ] = new CovariantInvoker( pos, predictor ); } }
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" ) ) );
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" ) ) );
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.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" ) ) );