Examples of Evaluation

com.mobixess.jodb.soda.api.Evaluation
for implementation of callback evaluations.

To constrain a {@link Query} node with your own callbackEvaluation, construct an object that implements the Evaluation interface and register it by passing it to {@link Query#constrain(Object) Query#constrain(Object)}.

Evaluations are called as the last step during query execution, after all other constraints have been applied. Evaluations in higher level {@link Query} nodes in the query graph are called first.use {@code com.mobixess.jodb.query.api.Evaluation} instead
cross.reputation.model.Evaluation
edu.stanford.nlp.sempre.Evaluation
An Evaluation measures how well the system is doing on a set of examples. Formally, it is just a collection of arbitrary statistics. @author Percy Liang
net.sf.mp.demo.conference.domain.conference.Evaluation

Title: Evaluation

Description: Domain Object describing a Evaluation entity
ognl.Evaluation
An Evaluation is and object that holds a node being evaluated and the source from which that node will take extract its value. It refers to child evaluations that occur as a result of the nodes' evaluation.
org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation
Weight matrix of the residuals between model and observations.
Immutable class. @version $Id: Weight.java 1416643 2012-12-03 19:37:14Z tn $ @since 3.1
org.fenixedu.academic.domain.Evaluation
org.fnlp.ml.eval.Evaluation
评测类给定数据集，计算分类器的分类性能。 @author xpqiu
org.neo4j.graphdb.traversal.Evaluation
org.ocpsoft.rewrite.bind.Evaluation
@author Lincoln Baxter, III
org.terrier.evaluation.Evaluation
An abstract class for evaluating the retrieval results. @author Gianni Amati, Ben He, Vassilis Plachouras
teammates.jdo.Evaluation
Evaluation is a persistent data class that holds information pertaining to an evaluation on Teammates. @author Gerald GOH
teammates.testing.object.Evaluation
tv.floe.metronome.eval.Evaluation
types.Evaluation
weka.classifiers.Evaluation
Class for evaluating machine learning models.
-------------------------------------------------------------------
General options when evaluating a learning scheme from the command-line:
-t filename
Name of the file with the training data. (required)
-T filename
Name of the file with the test data. If missing a cross-validation is performed.
-c index
Index of the class attribute (1, 2, ...; default: last).
-x number
The number of folds for the cross-validation (default: 10).
-no-cv
No cross validation. If no test file is provided, no evaluation is done.
-split-percentage percentage
Sets the percentage for the train/test set split, e.g., 66.
-preserve-order
Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').
-s seed
Random number seed for the cross-validation and percentage split (default: 1).
-m filename
The name of a file containing a cost matrix.
-l filename
Loads classifier from the given file. In case the filename ends with ".xml", a PMML file is loaded or, if that fails, options are loaded from XML.
-d filename
Saves classifier built from the training data into the given file. In case the filename ends with ".xml" the options are saved XML, not the model.
-v
Outputs no statistics for the training data.
-o
Outputs statistics only, not the classifier.
-i
Outputs information-retrieval statistics per class.
-k
Outputs information-theoretic statistics.
-classifications "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
Uses the specified class for generating the classification output. E.g.: weka.classifiers.evaluation.output.prediction.PlainText or : weka.classifiers.evaluation.output.prediction.CSV -p range
Outputs predictions for test instances (or the train instances if no test instances provided and -no-cv is used), along with the attributes in the specified range (and nothing else). Use '-p 0' if no attributes are desired.
Deprecated: use "-classifications ..." instead.
-distribution
Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).
Deprecated: use "-classifications ..." instead.
-r
Outputs cumulative margin distribution (and nothing else).
-g
Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).
-xml filename | xml-string
Retrieves the options from the XML-data instead of the command line.
-threshold-file file
The file to save the threshold data to. The format is determined by the extensions, e.g., '.arff' for ARFF format or '.csv' for CSV.
-threshold-label label
The class label to determine the threshold data for (default is the first label)
-------------------------------------------------------------------
Example usage as the main of a classifier (called FunkyClassifier):
```
 public static void main(String [] args) { runClassifier(new FunkyClassifier(), args); } 
```
------------------------------------------------------------------
Example usage from within an application:
```
 Instances trainInstances = ... instances got from somewhere Instances testInstances = ... instances got from somewhere Classifier scheme = ... scheme got from somewhere Evaluation evaluation = new Evaluation(trainInstances); evaluation.evaluateModel(scheme, testInstances); System.out.println(evaluation.toSummaryString()); 
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 7228 $

Examples of com.mobixess.jodb.soda.api.Evaluation

                } catch (Exception e) {
                    throw new JodbIOException(e);
                }
            }
            if(activeObject!=null){
                Evaluation evaluation = (Evaluation) getObject();
                CandidateImpl candidateImpl = _candidateImpl.get();
                candidateImpl.init(activeObject);
                evaluation.evaluate(candidateImpl);
                if(candidateImpl.isInclude()){
                    processCompareResult(COMPARE_RESULT.EQUAL);
                }else{
                    processCompareResult(COMPARE_RESULT.SMALLER);
                }

View Full Code Here

Examples of com.mobixess.jodb.soda.api.Evaluation

                Object obj = PrimitiveJavaTypesUtil.getAsWrappedPrimitive(fieldTypeEnum,record._primitiveRawDataBuffer);
                evaluateActiveObject(obj, null, context);
                return;
            }
            if(dataContainer!=null){
                Evaluation evaluation = (Evaluation) getObject();
                int activationDepth = evaluation.getActivationDepth() >= 0 ? evaluation.getActivationDepth() : JODBConfig.getDefaultActivationDepth();
                Object obj = context.getSession().getObjectForOffset(dataContainer.getOffset(), activationDepth);
                evaluateActiveObject(obj, null, context);
                return;
            }
        }

View Full Code Here

Examples of cross.reputation.model.Evaluation

          continue;
        }
        if(reputationMap.size() == 1 && community.getMetrics().size() == 1) {
          Metric metric = (Metric)community.getMetrics().toArray()[0];
          for(Object value : reputationMap.values()) {
            GlobalModel.addEvaluation(new Evaluation(community, entity,
                metric,value));
            System.out.println("Ent:"+entity.getUniqueIdentificator()+" Com: "
            +community.getName()+ " url:"+communityEntity.get(community).getUrl()
            +" met:"+metric.getIdentifier()+" rep:"+value);
          }
        } else {
          for(Metric metric : reputationMap.keySet()) {
            Metric sourceMetric = null;
            for(Metric comMetric : community.getMetrics()) {
              if(metric == null || !metric.getIdentifier().equalsIgnoreCase(
                  comMetric.getIdentifier())) {
                continue;                    
              }
              sourceMetric = comMetric;
              break;
            }
            if(sourceMetric == null) {              
              System.out.println("ERROR: metric parsed("+(metric==null?null:metric.getIdentifier())
                +") does not correspond to any metric of the community("+community.getName()+
                "):"+community.getMetrics()+". Its score is ignored");
              continue;
            }
            GlobalModel.addEvaluation(new Evaluation(community,
                entity,sourceMetric,reputationMap.get(metric)));
            System.out.println("Ent:"+entity.getUniqueIdentificator()+" Com:"
            +community.getName()+ " url:"+communityEntity.get(community).getUrl()
            +" met:"+sourceMetric.getIdentifier()+" rep:"+reputationMap.get(metric));            
          }

View Full Code Here

Examples of edu.stanford.nlp.sempre.Evaluation

  }


  private Evaluation processParsingExamples(int iter, String group,
      List<ParsingExample> parsingExamples, boolean updateWeights) {


    Evaluation totalEval = new Evaluation();


    if (parsingExamples.size() == 0)
      return totalEval;


    final String prefix = "parsing_iter=" + iter + "." + group;

View Full Code Here

Examples of net.sf.mp.demo.conference.domain.conference.Evaluation

      return referEvaluationUseCache (evaluation, false);
   }
          
   public Evaluation referEvaluationUseCache (Evaluation evaluation, boolean isAssign) {
    String key = getCacheKey(null, evaluation, null, "assignEvaluation");
      Evaluation evaluationCache = (Evaluation)simpleCache.get(key);
      if (evaluationCache==null) {
         evaluationCache = referEvaluation (evaluation, isAssign);
         if (key!=null)
           simpleCache.put(key, evaluationCache);
      }

View Full Code Here

Examples of ognl.Evaluation

      // values[i] = 2l in a List<Long>.
      // we all just looooove ognl.
      OgnlContext ctx = (OgnlContext) context;
      // if direct injecting, cannot find out what to do, use string
      if (ctx.getRoot() != target) {
        Evaluation eval = ctx.getCurrentEvaluation();
        Evaluation previous = eval.getPrevious();
        String fieldName = previous.getNode().toString();
        Object origin = previous.getSource();
        Method getter = ReflectionBasedNullHandler.findMethod(origin.getClass(), "get"
            + Info.capitalize(fieldName), origin.getClass(), null);
        Type genericType = getter.getGenericReturnType();
                Class type;
                if (genericType instanceof ParameterizedType) {

View Full Code Here

Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation




        // Evaluate the function at the starting point and calculate its norm.
        evaluationCounter.incrementCount();
        //value will be reassigned in the loop
        Evaluation current = problem.evaluate(problem.getStart());
        double[] currentResiduals = current.getResiduals().toArray();
        double currentCost = current.getCost();
        double[] currentPoint = current.getPoint().toArray();


        // Outer loop.
        boolean firstIteration = true;
        while (true) {
            iterationCounter.incrementCount();


            final Evaluation previous = current;


            // QR decomposition of the jacobian matrix
            final InternalData internalData
                    = qrDecomposition(current.getJacobian(), solvedCols);
            final double[][] weightedJacobian = internalData.weightedJacobian;

View Full Code Here

Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation

        }


        RealVector currentPoint = lsp.getStart();


        // iterate until convergence is reached
        Evaluation current = null;
        while (true) {
            iterationCounter.incrementCount();


            // evaluate the objective function and its jacobian
            Evaluation previous = current;
            // Value of the objective function at "currentPoint".
            evaluationCounter.incrementCount();
            current = lsp.evaluate(currentPoint);
            final RealVector currentResiduals = current.getResiduals();
            final RealMatrix weightedJacobian = current.getJacobian();

View Full Code Here

Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation

    /** check {@link ConvergenceChecker#converged(int, Object, Object)}. */
    @Test
    public void testConverged() {
        //setup
        ConvergenceChecker<Evaluation> checker = new EvaluationRmsChecker(0.1, 1);
        Evaluation e200 = mockEvaluation(200);
        Evaluation e1 = mockEvaluation(1);


        //action + verify
        //just matches rel tol
        Assert.assertEquals(true, checker.converged(0, e200, mockEvaluation(210)));
        //just matches abs tol

View Full Code Here

Examples of org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation

     *
     * @param rms the evaluation's rms.
     * @return a new mock evaluation.
     */
    private static Evaluation mockEvaluation(final double rms) {
        return new Evaluation() {
            public RealMatrix getCovariances(double threshold) {
                return null;
            }


            public RealVector getSigma(double covarianceSingularityThreshold) {

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.