Examples of BaseGenerator

Examples of org.apache.beehive.netui.compiler.BaseGenerator

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        assert _sourceFileInfo != null;     // process() should guarantee this.
       
        if ( decl instanceof ClassDeclaration )
        {
            ClassDeclaration classDecl = ( ClassDeclaration ) decl;
            BaseGenerator generator = getGenerator( classDecl, this );
            if ( generator != null ) generator.generate( classDecl );
        }
    }
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Examples of org.apache.beehive.netui.compiler.BaseGenerator

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        assert _sourceFileInfo != null;     // process() should guarantee this.
       
        if ( decl instanceof ClassDeclaration )
        {
            ClassDeclaration classDecl = ( ClassDeclaration ) decl;
            BaseGenerator generator = getGenerator( classDecl, this );
            if ( generator != null ) generator.generate( classDecl );
        }
    }
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Examples of org.apache.beehive.netui.compiler.BaseGenerator

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        assert _sourceFileInfo != null;     // process() should guarantee this.
       
        if ( decl instanceof ClassDeclaration )
        {
            ClassDeclaration classDecl = ( ClassDeclaration ) decl;
            BaseGenerator generator = getGenerator( classDecl, this );
            if ( generator != null ) generator.generate( classDecl );
        }
    }
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Examples of org.fnlp.ml.feature.BaseGenerator

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    System.out.println("Class Number: " + al.size());

    float c = 1.0f;
    int round = 20;
   
    BaseGenerator featureGen = new BaseGenerator();
    ZeroOneLoss loss = new ZeroOneLoss();
    Inferencer msolver = new MultiLinearMax(featureGen, al, null,2);

    PATrainer trainer = new PATrainer(msolver, featureGen, loss, round,c, null);
    Linear pclassifier = trainer.train(train, null);
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Examples of org.fnlp.ml.feature.BaseGenerator

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    System.out.println("Class Number: " + lf.size());

    float c = 1.0f;
    int round = 10;
   
    BaseGenerator featureGen = new BaseGenerator();
    ZeroOneLoss loss = new ZeroOneLoss();
    Inferencer msolver = new MultiLinearMax(featureGen, lf, null,2);

    PATrainer trainer = new PATrainer(msolver, featureGen, loss, round,c, null);
    Linear pclassifier = trainer.train(trainset, null);
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