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


    @Test
    public void testComputeResiduals() {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .target(new ArrayRealVector(new double[]{3,-1}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{1, 2}),
                                MatrixUtils.createRealIdentityMatrix(2)
                        );
                    }
                })
                .weight(MatrixUtils.createRealIdentityMatrix(2))
                .build()
                .evaluate(point);


        //action + verify
        Assert.assertArrayEquals(
                evaluation.getResiduals().toArray(),
                new double[]{2, -3},
                Precision.EPSILON);
    }

View Full Code Here

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


    @Test
    public void testComputeCovariance() throws IOException {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(2),
                                MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1e-2})
                        );
                    }
                })
                .weight(MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1}))
                .target(new ArrayRealVector(2))
                .build()
                .evaluate(point);


        //action
        TestUtils.assertEquals(
                "covariance",
                evaluation.getCovariances(FastMath.nextAfter(1e-4, 0.0)),
                MatrixUtils.createRealMatrix(new double[][]{{1, 0}, {0, 1e4}}),
                Precision.EPSILON
        );


        //singularity fail
        try {
            evaluation.getCovariances(FastMath.nextAfter(1e-4, 1.0));
            Assert.fail("Expected Exception");
        } catch (SingularMatrixException e) {
            //expected
        }
    }

View Full Code Here

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


    @Test
    public void testComputeValueAndJacobian() {
        //setup
        final RealVector point = new ArrayRealVector(new double[]{1, 2});
        Evaluation evaluation = new LeastSquaresBuilder()
                .weight(new DiagonalMatrix(new double[]{16, 4}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector actualPoint) {
                        //verify correct values passed in
                        Assert.assertArrayEquals(
                                point.toArray(), actualPoint.toArray(), Precision.EPSILON);
                        //return values
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{3, 4}),
                                MatrixUtils.createRealMatrix(new double[][]{{5, 6}, {7, 8}})
                        );
                    }
                })
                .target(new double[2])
                .build()
                .evaluate(point);


        //action
        RealVector residuals = evaluation.getResiduals();
        RealMatrix jacobian = evaluation.getJacobian();


        //verify
        Assert.assertArrayEquals(evaluation.getPoint().toArray(), point.toArray(), 0);
        Assert.assertArrayEquals(new double[]{-12, -8}, residuals.toArray(), Precision.EPSILON);
        TestUtils.assertEquals(
                "jacobian",
                jacobian,
                MatrixUtils.createRealMatrix(new double[][]{{20, 24},{14, 16}}),

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


        final LeastSquaresProblem lsp = builder(dataset).build();


        final double[] expected = dataset.getParametersStandardDeviations();


        final Evaluation evaluation = lsp.evaluate(lsp.getStart());
        final double cost = evaluation.getCost();
        final RealVector sig = evaluation.getSigma(1e-14);
        final int dof = lsp.getObservationSize() - lsp.getParameterSize();
        for (int i = 0; i < sig.getDimension(); i++) {
            final double actual = FastMath.sqrt(cost * cost / dof) * sig.getEntry(i);
            Assert.assertEquals(dataset.getName() + ", parameter #" + i,
                                expected[i], actual, 1e-6 * expected[i]);

View Full Code Here

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

                = StatisticalReferenceDatasetFactory.createKirby2();
        LeastSquaresProblem lsp = builder(dataset).build();
        RealVector point = new ArrayRealVector(lsp.getParameterSize());


        //action
        Evaluation evaluation = lsp.evaluate(point);


        //verify
        Assert.assertNotSame(point, evaluation.getPoint());
        point.setEntry(0, 1);
        Assert.assertEquals(evaluation.getPoint().getEntry(0), 0, 0);
    }

View Full Code Here

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

        final LeastSquaresProblem p
            = LeastSquaresFactory.create(LeastSquaresFactory.model(dummyModel(), dummyJacobian()),
                                         dummy, dummy, null, null, 0, 0, true, null);


        // Should not throw because actual evaluation is deferred.
        final Evaluation eval = p.evaluate(dummy);


        try {
            eval.getResiduals();
            Assert.fail("Exception expected");
        } catch (RuntimeException e) {
            // Expecting exception.
            Assert.assertEquals("dummyModel", e.getMessage());
        }


        try {
            eval.getJacobian();
            Assert.fail("Exception expected");
        } catch (RuntimeException e) {
            // Expecting exception.
            Assert.assertEquals("dummyJacobian", e.getMessage());
        }

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(new ArrayRealVector(currentPoint));
        double[] currentResiduals = current.getResiduals().toArray();
        double currentCost = current.getCost();


        // 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


    @Test
    public void testComputeResiduals() {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .target(new ArrayRealVector(new double[]{3,-1}))
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(new double[]{1, 2}),
                                MatrixUtils.createRealIdentityMatrix(2)
                        );
                    }
                })
                .weight(MatrixUtils.createRealIdentityMatrix(2))
                .build()
                .evaluate(point);


        //action + verify
        Assert.assertArrayEquals(
                evaluation.getResiduals().toArray(),
                new double[]{2, -3},
                Precision.EPSILON);
    }

View Full Code Here

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


    @Test
    public void testComputeCovariance() throws IOException {
        //setup
        RealVector point = new ArrayRealVector(2);
        Evaluation evaluation = new LeastSquaresBuilder()
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(2),
                                MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1e-2})
                        );
                    }
                })
                .weight(MatrixUtils.createRealDiagonalMatrix(new double[]{1, 1}))
                .target(new ArrayRealVector(2))
                .build()
                .evaluate(point);


        //action
        TestUtils.assertEquals(
                "covariance",
                evaluation.getCovariances(FastMath.nextAfter(1e-4, 0.0)),
                MatrixUtils.createRealMatrix(new double[][]{{1, 0}, {0, 1e4}}),
                Precision.EPSILON
        );


        //singularity fail
        try {
            evaluation.getCovariances(FastMath.nextAfter(1e-4, 1.0));
            Assert.fail("Expected Exception");
        } catch (SingularMatrixException e) {
            //expected
        }
    }

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