Package cz.zcu.fav.liks.ml.featuresel.validation

Examples of cz.zcu.fav.liks.ml.featuresel.validation.CrossValidator.multiValidate()

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        List<Review> data = dataConn.getReviewsNo(1000 * i);
        System.out.println("Datasize " + data.size());

        print("Naive bayes");
        SupervisedTrainer trainer = new NaiveBayesGaussianTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);

        print("Maximum Entropy");
        trainer = new MaxEntLBFGSTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);

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        SupervisedTrainer trainer = new NaiveBayesGaussianTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);

        print("Maximum Entropy");
        trainer = new MaxEntLBFGSTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);

        print("SVM");
        trainer = new MySVMTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);
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        trainer = new MaxEntLBFGSTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);

        print("SVM");
        trainer = new MySVMTrainer();
        cv.multiValidate(features, trainer, 10, data, null, 0);

      }
    } catch (SQLException e) {
      e.printStackTrace();
    } catch (InvalidAttributeValueException e) {
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      SupervisedTrainer trainer = new MaxEntLBFGSTrainer();
      double weight = 0.1;

      for (int i = 0; i < 10; i++)
        cv.multiValidate(features, trainer, 10, data, filter, weight * i);

      dataConn.close();

    } catch (ParseException e) {
      System.err.println("Parsing failed.  Reason: " + e.getMessage());
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      SupervisedTrainer trainer = new MaxEntLBFGSTrainer();
      List<Review> data = dataConn.getReviews();

      print("no selector");
      cv.multiValidate(features, trainer, 10, data, null, 0);

      print("RS(0,1)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.1);

      print("RS(0,5)");
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      print("no selector");
      cv.multiValidate(features, trainer, 10, data, null, 0);

      print("RS(0,1)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.1);

      print("RS(0,5)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.5);

      print("RS(0,6)");
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      print("RS(0,1)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.1);

      print("RS(0,5)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.5);

      print("RS(0,6)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.6);

      print("RS(0,7)");
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      print("RS(0,5)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.5);

      print("RS(0,6)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.6);

      print("RS(0,7)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.7);

      print("MI(0,6)");
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      print("RS(0,6)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.6);

      print("RS(0,7)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.7);

      print("MI(0,6)");
      cv.multiValidate(features, trainer, 10, data, new MutualInformation(), 0.6);

      print("MI(0,7)");
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      print("RS(0,7)");
      cv.multiValidate(features, trainer, 10, data, new RelevancyScore(), 0.7);

      print("MI(0,6)");
      cv.multiValidate(features, trainer, 10, data, new MutualInformation(), 0.6);

      print("MI(0,7)");
      cv.multiValidate(features, trainer, 10, data, new MutualInformation(), 0.7);

      print("MI(0,8)");
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