Package cascading.pattern.model.generalregression.predictor

Examples of cascading.pattern.model.generalregression.predictor.CovariantPredictor

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      String name = predictor.getName().getValue();
      int exponent = predictor.getExponent();

      double coefficient = predictor.getCoefficient();

      generalRegressionTable.addParameter( new Parameter( "f" + count++, coefficient, new CovariantPredictor( name, exponent ) ) );
      }

    return generalRegressionTable;
    }
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      cascading.pattern.model.generalregression.predictor.Predictor predictor;

      if( factorsList.contains( predictorName ) )
        predictor = new FactorPredictor( predictorName, value );
      else if( covariateList.contains( predictorName ) )
        predictor = new CovariantPredictor( predictorName, Long.parseLong( value ) );
      else
        throw new IllegalStateException( "unknown predictor name: " + predictorName );

      regressionTable.getParameter( parameterName ).addPredictor( predictor );
      }
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    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 );
      }
    }
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    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 );
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    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" ) ) );
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    {
    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 );
    }

    {
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Related Classes of cascading.pattern.model.generalregression.predictor.CovariantPredictor

  • cascading.pattern.model.generalregression.expression.ParameterExpression
  • cascading.pattern.model.ModelTest
  • cascading.pattern.pmml.GeneralRegressionUtil
  • cascading.pattern.pmml.RegressionUtil
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