List of estimate() Examples

Examples of estimate()

cc.mallet.topics.HDP.estimate()
cc.mallet.topics.HDPInferencer.estimate()
cc.mallet.topics.HierarchicalLDA.estimate()
cc.mallet.topics.LDAStream.estimate()
cc.mallet.topics.ParallelTopicModel.estimate()
com.ipeirotis.gal.scripts.DawidSkene.estimate()
edu.brown.designer.MemoryEstimator.estimate()
gr.ntua.irmos.stubs.MapService_instance.MapPortType.estimate()
org.apache.commons.math.estimation.GaussNewtonEstimator.estimate()
Solve an estimation problem using a least squares criterion.
This method set the unbound parameters of the given problem starting from their current values through several iterations. At each step, the unbound parameters are changed in order to minimize a weighted least square criterion based on the measurements of the problem.

The iterations are stopped either when the criterion goes below a physical threshold under which improvement are considered useless or when the algorithm is unable to improve it (even if it is still high). The first condition that is met stops the iterations. If the convergence it not reached before the maximum number of iterations, an {@link EstimationException} isthrown.
@param problem estimation problem to solve @exception EstimationException if the problem cannot be solved @see EstimationProblem
org.apache.commons.math.estimation.LevenbergMarquardtEstimator.estimate()
etlib.org/minpack/lmder.f">lmder routine. The algorithm settings must have been set up before this method is called with the {@link #setInitialStepBoundFactor}, {@link #setMaxCostEval}, {@link #setCostRelativeTolerance}, {@link #setParRelativeTolerance} and {@link #setOrthoTolerance} methods.If these methods have not been called, the default values set up by the {@link #LevenbergMarquardtEstimator() constructor} will be used.
The authors of the original fortran function are:
- Argonne National Laboratory. MINPACK project. March 1980
- Burton S. Garbow
- Kenneth E. Hillstrom
- Jorge J. More
Luc Maisonobe did the Java translation.
@param problem estimation problem to solve @exception EstimationException if convergence cannot bereached with the specified algorithm settings or if there are more variables than equations @see #setInitialStepBoundFactor @see #setCostRelativeTolerance @see #setParRelativeTolerance @see #setOrthoTolerance
org.apache.commons.math.optimization.general.LevenbergMarquardtEstimator.estimate()
org.apache.commons.math3.stat.descriptive.rank.PSquarePercentile.PSquareMarkers.estimate()
Helper method to create a multivariate normal mixture model which can be used to initialize {@link #fit(MixtureMultivariateNormalDistribution)}. This method uses the data supplied to the constructor to try to determine a good mixture model at which to start the fit, but it is not guaranteed to supply a model which will find the optimal solution or even converge. @param data Data to estimate distribution @param numComponents Number of components for estimated mixture @return Multivariate normal mixture model estimated from the data @throws NumberIsTooLargeException if {@code numComponents} is greaterthan the number of data rows. @throws NumberIsTooSmallException if {@code numComponents < 2}. @throws NotStrictlyPositiveException if data has less than 2 rows @throws DimensionMismatchException if rows of data have different numbersof columns
org.apache.hadoop.hive.ql.metadata.InputEstimator.estimate()
Estimate input size based on filter and projection on table scan operator @param remaining Early exit condition. If it has positive value, further estimationcan be canceled on the point of exceeding it. In this case, return any bigger length value then this (Long.MAX_VALUE, for eaxmple).
org.rhq.server.metrics.migrator.workers.RawDataMigrator.estimate()
stallone.api.stat.IParameterEstimator.estimate()

Examples of cc.mallet.topics.HDP.estimate()

    //setup HDP parameters(alpha, beta, gamma, initialTopics)
    HDP hdp = new HDP(1.0, 0.1, 1.0, 10);
    hdp.initialize(instances);
    
    //set number of iterations, and display result or not 
    hdp.estimate(2000);
    
    //get topic distribution for first instance
    double[] distr = hdp.topicDistribution(0);
    //print out
    for(int j=0; j<distr.length ; j++){

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Examples of cc.mallet.topics.HDPInferencer.estimate()

    }
    
    //for inferencer
    HDPInferencer inferencer = hdp.getInferencer();
    inferencer.setInstance(testInstances);
    inferencer.estimate(100);
    //get topic distribution for first test instance
    distr = inferencer.topicDistribution(0);    
    //print out
    for(int j=0; j<distr.length ; j++){
      System.out.print(distr[j] + " ");

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Examples of cc.mallet.topics.HierarchicalLDA.estimate()

    }


    // Initialize and start the sampler


    hlda.initialize(instances, testing, numLevels.value(), random);
    hlda.estimate(numIterations.value());
    
    // Output results


    if (stateFile.value() != null) {
      hlda.printState(new PrintWriter(stateFile.value()));

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Examples of cc.mallet.topics.LDAStream.estimate()


        if (outputStateInterval.value != 0) {
            lda.setSaveState(outputStateInterval.value, stateFile.value);
        }


    lda.estimate();
    //save the model, we need typeTopicCounts and tokensPerTopic for empirical likelihood
    lda.write(new File (inputFile.value + ".model"));


    if (topicKeysFile.value != null) {
      lda.printTopWords(new File(topicKeysFile.value), topWords.value, false);

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Examples of cc.mallet.topics.ParallelTopicModel.estimate()

        ParallelTopicModel model = new ParallelTopicModel(numTopics, 1.0, 0.01);
        model.addInstances(instances);
        model.setNumThreads(1); // Important, since this is being run in the reduce, just use one thread
        model.setTopicDisplay(0,0);
        model.setNumIterations(2000);
        model.estimate();


        // Get the results
        Alphabet dataAlphabet = instances.getDataAlphabet();
        ArrayList<TopicAssignment> assignments = model.getData();

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Examples of com.ipeirotis.gal.scripts.DawidSkene.estimate()

              AssignedLabel l = new AssignedLabel( vote.worker, vote.unit, vote.label);
              ds.addAssignedLabel(l);
            }
        }


        ds.estimate( 20 );
        
        return ds.getMajorityVote();
    }
}

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Examples of edu.brown.designer.MemoryEstimator.estimate()

        for (SiteEntry site : this.sites) {
            long site_size = 0l;
            for (FragmentEntry fragment : site.getFragments()) {
                Table catalog_tbl = fragment.getTable(catalog_db);
                Column partition_col = catalog_tbl.getPartitioncolumn();
                long size = estimator.estimate(catalog_tbl, partition_col, fragment.getHashKey());
                site_size += size;
                fragment.setEstimatedSize(size);
            } // FOR
            site.setEstimatedSize(site_size);
        } // FOR

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Examples of gr.ntua.irmos.stubs.MapService_instance.MapPortType.estimate()

      
      Estimate input=new Estimate();
      input.setAscid(args[1]);
      //change dynamically to whatever input in the following format [input1;input2;...;inputn]
      input.setHlinput("[1;1;1;1]");
      String result=map.estimate(input);
      System.out.println("Current result: "+result);




    } catch (Exception e) {
      e.printStackTrace();

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Examples of org.apache.commons.math.estimation.GaussNewtonEstimator.estimate()

                              new EstimatedParameter[] {
                                 new EstimatedParameter("p0", 0)
                              }, 3.0)
      });
    GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
    estimator.estimate(problem);
    assertEquals(0, estimator.getRMS(problem), 1.0e-10);
    assertEquals(1.5,
                 problem.getUnboundParameters()[0].getEstimate(),
                 1.0e-10);
   }

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Examples of org.apache.commons.math.estimation.GaussNewtonEstimator.estimate()

                            new EstimatedParameter[] { x[0], x[1] },
                            1.0)
    });


    GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
    estimator.estimate(problem);
    assertEquals(0, estimator.getRMS(problem), 1.0e-10);
    assertEquals(7.0, x[0].getEstimate(), 1.0e-10);
    assertEquals(3.0, x[1].getEstimate(), 1.0e-10);


  }

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