Package au.com.bytecode.opencsv

Examples of au.com.bytecode.opencsv.CSVWriter

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      header[i] = metaData.getColumnName(i + 1);
    }
    File file = new File(destination);
    IOHelper.checkDirectory(file);
    PrintWriter pw = new PrintWriter(file);
    CSVWriter writer = new CSVWriter(pw);
    writer.writeNext(header);
    while (rs.next()) {
      String[] row = new String[columnCount];
      for (int i = 0; i < columnCount; i++) {
        String string = rs.getString(i + 1);
        if (string == null) {
          string = "";
        }
        row[i] = string;
      }
      writer.writeNext(row);
    }
  }
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    setExportFilename(promptUserforSave());

    try
    {
      CSVWriter writer = new CSVWriter(new FileWriter(getExportFilename()), ',');
      writer.writeAll(temp, true);
      writer.close();
    } catch (Exception e)
    {
      result = false;
      setErrorMessage(e.getMessage());
    }
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                    stmt.setString(2, autolabview.getPrinterObj().getPrinterID());
                    stmt.setFetchSize(50);

                    rs = stmt.executeQuery();

                    CSVWriter writer = new CSVWriter(new FileWriter(exportFilename), ',');
                    writer.writeAll(rs, true);

                    rs.close();

                    stmt.close();

                    writer.close();
                  } catch (Exception e)
                  {
                    messageProcessedOK = false;
                    messageError = e.getMessage();
                  }
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                path = path + java.io.File.separator;
              }
            }
            String filename = path + "LabelData_"+labdata.getLine() + "_" + unique + ".csv";

            CSVWriter writer = new CSVWriter(new FileWriter(filename), ',');
            writer.writeNext(heading);
            writer.writeNext(data);
            writer.close();
            result = true;
            il.write(gmh, GenericMessageHeader.msgStatusSuccess, "Processed OK", "File Write", filename);
            setErrorMessage("");
          }
        }
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    {
      rs = getResultSet();
     
      StringWriter sw = new StringWriter();
     
      CSVWriter writer = new CSVWriter(sw);
      writer.writeAll(rs, false);
      writer.close();
     
      System.out.println(sw);
    }
    catch (Exception ex)
    {
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    // Try writing it back out as CSV to the console
    CSVReader reader2 = new CSVReader(new FileReader(ADDRESS_FILE));
    List<String[]> allElements = reader2.readAll();
    StringWriter sw = new StringWriter();
    CSVWriter writer = new CSVWriter(sw);
    writer.writeAll(allElements);
   
    System.out.println("\n\nGenerated CSV File:\n\n");
    System.out.println(sw.toString());
   
   
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    wfFilter.setNumParticles(N);
    wfFilter.setResampleOnly(false);
    rsFilter.setNumParticles(N);

    CSVWriter writer = new CSVWriter(new FileWriter(outputFilename), ',');
    String[] header = "rep,t,filter.type,measurement.type,resample.type,measurement".split(",");
    writer.writeNext(header);

    GaussianArHmmClassEvaluator wfClassEvaluator = new GaussianArHmmClassEvaluator("wf-pl",
        writer);
    GaussianArHmmClassEvaluator mkfClassEvaluator = new GaussianArHmmClassEvaluator("mkf",
        writer);
    GaussianArHmmRmseEvaluator wfRmseEvaluator = new GaussianArHmmRmseEvaluator("wf-pl",
        writer);
    GaussianArHmmRmseEvaluator mkfRmseEvaluator = new GaussianArHmmRmseEvaluator("mkf",
        writer);

    RingAccumulator<MutableDouble> mkfLatency =
        new RingAccumulator<MutableDouble>();
    RingAccumulator<MutableDouble> wfLatency =
        new RingAccumulator<MutableDouble>();
    Stopwatch mkfWatch = new Stopwatch();
    Stopwatch wfWatch = new Stopwatch();


    for (int k = 0; k < K; k++) {
      log.info("Processing replication " + k);
      CountedDataDistribution<GaussianArTransitionState> wfDistribution =
          (CountedDataDistribution<GaussianArTransitionState>) wfFilter.getUpdater().createInitialParticles(N);
//          ((HmmPlFilter.HmmPlUpdater<Double>) wfFilter.getUpdater()).baumWelchInitialization(sample.getFirst(), N);

      DataDistribution<GaussianArTransitionState> rsDistribution =
          rsFilter.getUpdater().createInitialParticles(N);

      final long numPreRuns = -1l;//wfDistribution.getMaxValueKey().getTime();
     
      /*
       * Recurse through the particle filter
       */
      for (int i = 0; i < T; i++) {
 
        final double x = simulation.get(i).getClassId();
        final Double y = simulation.get(i).getObservedValue().getElement(0);
        // lame hack need until I refactor to use DlmHiddenMarkovModel in the filters
        final ObservedValue<Double, Void> obsState = new SimHmmObservedValue<Double, Double>(i,
           (int)x , simulation.get(i).getState().getElement(0), y);

        if (i > 0) {
          mkfWatch.reset();
          mkfWatch.start();
          rsFilter.update(rsDistribution, obsState);
          mkfWatch.stop();
          final long latency = mkfWatch.elapsed(TimeUnit.MILLISECONDS);
          mkfLatency.accumulate(new MutableDouble(latency));
          writer.writeNext(new String[] {
              Integer.toString(k), Integer.toString(i),
              "mkf", "latency", "NA",
              Long.toString(latency)
          });
        }

        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, obsState);
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
            writer.writeNext(new String[] {
                Integer.toString(k), Integer.toString(i),
                "wf-pl", "latency", "NA",
                Long.toString(latency)
            });
          }
         
          wfClassEvaluator.evaluate(k, simulation.get(i), wfDistribution);
          mkfClassEvaluator.evaluate(k, simulation.get(i), rsDistribution);
          wfRmseEvaluator.evaluate(k, simulation.get(i), wfDistribution);
          mkfRmseEvaluator.evaluate(k, simulation.get(i), rsDistribution);
        }

        if ((i+1) % (T/4d) < 1) {
          log.info("avg. mkf latency=" + mkfLatency.getMean().value);
          log.info("avg. wf latency=" + wfLatency.getMean().value);
          log.info("avg. mkfRmse=" + mkfRmseEvaluator.getTotalRate().getMean().value);
          log.info("avg. wfRmse=" + wfRmseEvaluator.getTotalRate().getMean().value);
          log.info("avg. mkfClassRate=" + mkfClassEvaluator.getTotalRate().getMean().value);
          log.info("avg. wfClassRate=" + wfClassEvaluator.getTotalRate().getMean().value);
        }
      }

    }

    writer.close();
  }
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    List<SimHmmObservedValue<Vector, Vector>> simulation = trueHmm1.sample(random, T);

    wfFilter.setNumParticles(N);
    wfFilter.setResampleOnly(false);

    CSVWriter writer = new CSVWriter(new FileWriter(outputFilename), ',');
    String[] header = "rep,t,filter.type,measurement.type,resample.type,measurement".split(",");
    writer.writeNext(header);

    GaussianArHmmClassEvaluator wfClassEvaluator = new GaussianArHmmClassEvaluator("wf-pl",
        writer);
    GaussianArHmmRmseEvaluator wfRmseEvaluator = new GaussianArHmmRmseEvaluator("wf-pl",
        writer);
    GaussianArHmmPsiLearningEvaluator wfPsiEvaluator = new GaussianArHmmPsiLearningEvaluator("wf-pl",
        truePsis, writer);

    RingAccumulator<MutableDouble> wfLatency =
        new RingAccumulator<MutableDouble>();
    Stopwatch wfWatch = new Stopwatch();


    for (int k = 0; k < K; k++) {
      log.info("Processing replication " + k);
      CountedDataDistribution<GaussianArHpTransitionState> wfDistribution =
          (CountedDataDistribution<GaussianArHpTransitionState>) wfFilter.getUpdater().createInitialParticles(N);


      final long numPreRuns = -1l;//wfDistribution.getMaxValueKey().getTime();
     
      /*
       * Recurse through the particle filter
       */
      for (int i = 0; i < T; i++) {
 
        final double x = simulation.get(i).getClassId();
        final Vector y = simulation.get(i).getObservedValue();

        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulation.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
            writer.writeNext(new String[] {
                Integer.toString(k), Integer.toString(i),
                "wf-pl", "latency", "NA",
                Long.toString(latency)
            });
          }
         
          wfClassEvaluator.evaluate(k, simulation.get(i), wfDistribution);
          wfRmseEvaluator.evaluate(k, simulation.get(i), wfDistribution);
          wfPsiEvaluator.evaluate(k, simulation.get(i), wfDistribution);
        }

        if ((i+1) % (T/4d) < 1) {
          log.info("avg. wf latency=" + wfLatency.getMean().value);
          log.info("avg. wfRmse=" + wfRmseEvaluator.getTotalRate().getMean().value);
          log.info("avg. wfClassRate=" + wfClassEvaluator.getTotalRate().getMean().value);
          log.info("avg. wfPsi=" + wfPsiEvaluator.getTotalRate());
        }
      }

    }

    writer.close();
  }
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        List<Integer> viterbiResults = hmm.viterbi(obsValues);
       
        RingAccumulator<MutableDouble> viterbiRate = new RingAccumulator<MutableDouble>();
        RingAccumulator<MutableDouble> pfRunningRate = new RingAccumulator<MutableDouble>();
   
        CSVWriter writer = new CSVWriter(new FileWriter(outputFilename), ',');
        String[] header = "rep,t,measurement.type,filter.type,resample.type,measurement".split(",");
        writer.writeNext(header);
   
        for (int k = 0; k < K; k++) {
          CountedDataDistribution<P> wfDistribution =
              ((HmmPlFilter.HmmPlUpdater<H, P, T>) wfFilter.getUpdater()).baumWelchInitialization(obsValues, N);
   
          CountedDataDistribution<P> rsDistribution =
              (CountedDataDistribution<P>)
              rsFilter.getUpdater().createInitialParticles(N);
   
          final long numPreRuns = wfDistribution.getMaxValueKey().getTime();
         
          /*
           * Recurse through the particle filter
           */
          for (int i = 0; i < T; i++) {
     
            final double x = DoubleMath.roundToInt(samples.get(i).getState(), RoundingMode.HALF_EVEN);
            viterbiRate.accumulate(new MutableDouble((x == viterbiResults.get(i) ? 1d : 0d)));
   
            final T y = samples.get(i).getObservedValue();
            final ObservedValue<T, Void> obsState = ObservedValue.create(i, y);
   
            rsFilter.update(rsDistribution, obsState);
   
            /*
             * Compute and output RS forward errors
             */
            ResampleType rsResampleType = rsDistribution.getMaxValueKey().getResampleType();
            Vector rsStateProbDiffs = computeStateDiffs(i, hmm.getNumStates(), rsDistribution, forwardResults);
            String[] rsLine = {Integer.toString(k), Integer.toString(i), "p(x_t=0|y^t)",
               rsResampleType.toString(),
               Double.toString(rsStateProbDiffs.getElement(0))};
            writer.writeNext(rsLine);
            log.info("rsStateProbDiffs=" + rsStateProbDiffs);
   
     
            if (i > numPreRuns) {
              wfFilter.update(wfDistribution, obsState);
       
              RingAccumulator<MutableDouble> pfAtTRate = new RingAccumulator<MutableDouble>();
              for (P state : wfDistribution.getDomain()) {
                final double err = (x == state.getClassId()) ? wfDistribution.getFraction(state) : 0d;
                pfAtTRate.accumulate(new MutableDouble(err));
              }
              pfRunningRate.accumulate(new MutableDouble(pfAtTRate.getSum()));
       
              ResampleType wfResampleType = wfDistribution.getMaxValueKey().getResampleType();
              Vector wfStateProbDiffs = computeStateDiffs(i, hmm.getNumStates(), wfDistribution, forwardResults);
              String[] wfLine = {Integer.toString(k), Integer.toString(i), "p(x_t=0|y^t)", "water-filling",
                 wfResampleType.toString(),
                 Double.toString(wfStateProbDiffs.getElement(0))};
              writer.writeNext(wfLine);
              log.info("wfStateProbDiffs=" + wfStateProbDiffs);
   
            }
     
          }
   
          log.info("viterbiRate:" + viterbiRate.getMean());
          log.info("pfRunningRate:" + pfRunningRate.getMean());
     
    //      RingAccumulator<MutableDouble> pfRate2 = new RingAccumulator<MutableDouble>();
    //      for (HMMTransitionState<Integer> state : distribution.getDomain()) {
    //        final double chainLogLikelihood = distribution.getLogFraction(state);
    //        RingAccumulator<MutableDouble> pfAtTimeRate = new RingAccumulator<MutableDouble>();
    //        for (int i = 0; i < T; i++) {
    //          final double x = DoubleMath.roundToInt(sample.getSecond().get(i), RoundingMode.HALF_EVEN);
    //          final double err;
    //          if (i < T - 1) {
    //            final WeightedValue<Integer> weighedState = state.getStateHistory().get(i);
    //            err = (x == weighedState.getValue() ? 1d : 0d);
    //          } else {
    //            err = (x == state.getState() ? 1d : 0d);
    //          }
    //          pfAtTimeRate.accumulate(new MutableDouble(err));
    //        }
    //        pfRate2.accumulate(new MutableDouble(pfAtTimeRate.getMean().doubleValue()
    //            * Math.exp(chainLogLikelihood)));
    //      }
    //      log.info("pfChainRate:" + pfRate2.getSum());
   
          /*
           * Loop through the smoothed trajectories and compute the
           * class probabilities for each state.
           */
          for (int t = 0; t < T; t++) {
   
            CountedDataDistribution<Integer> wfStateSums = new CountedDataDistribution<Integer>(true);
            CountedDataDistribution<Integer> rsStateSums = new CountedDataDistribution<Integer>(true);
            if (t < T - 1) {
              for (HmmTransitionState<T, H> state : wfDistribution.getDomain()) {
                final WeightedValue<Integer> weighedState = state.getStateHistory().get(t);
                wfStateSums.increment(weighedState.getValue(), weighedState.getWeight());
              }
              for (HmmTransitionState<T, H> state : rsDistribution.getDomain()) {
                final WeightedValue<Integer> weighedState = state.getStateHistory().get(t);
                rsStateSums.increment(weighedState.getValue(), weighedState.getWeight());
              }
            } else {
              for (P state : wfDistribution.getDomain()) {
                wfStateSums.adjust(state.getClassId(), wfDistribution.getLogFraction(state), wfDistribution.getCount(state));
              }
              for (P state : rsDistribution.getDomain()) {
                rsStateSums.adjust(state.getClassId(), rsDistribution.getLogFraction(state), rsDistribution.getCount(state));
              }
            }
   
            Vector wfStateProbDiffs = VectorFactory.getDefault().createVector(hmm.getNumStates());
            Vector rsStateProbDiffs = VectorFactory.getDefault().createVector(hmm.getNumStates());
            for (int j = 0; j < hmm.getNumStates(); j++) {
              /*
               * Sometimes all the probability goes to one class...
               */
              final double wfStateProb;
              if (!wfStateSums.getDomain().contains(j))
                wfStateProb = 0d;
              else
                wfStateProb = wfStateSums.getFraction(j);
              wfStateProbDiffs.setElement(j, gammas.get(t).getElement(j) - wfStateProb);
   
              final double rsStateProb;
              if (!rsStateSums.getDomain().contains(j))
                rsStateProb = 0d;
              else
                rsStateProb = rsStateSums.getFraction(j);
              rsStateProbDiffs.setElement(j, gammas.get(t).getElement(j) - rsStateProb);
            }
            String[] wfLine = {Integer.toString(k), Integer.toString(t), "p(x_t=0|y^T)", "water-filling",
                  wfDistribution.getMaxValueKey().getResampleType().toString(),
                  Double.toString(wfStateProbDiffs.getElement(0))};
            writer.writeNext(wfLine);
            String[] rsLine = {Integer.toString(k), Integer.toString(t), "p(x_t=0|y^T)", "resample",
                rsDistribution.getMaxValueKey().getResampleType().toString(),
                Double.toString(rsStateProbDiffs.getElement(0))};
            writer.writeNext(rsLine);
          }
        }
        writer.close();
      }
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        new RingAccumulator<MutableDouble>();
    RingAccumulator<MutableDouble> wfSigma2RMSEs =
        new RingAccumulator<MutableDouble>();

    String outputFilename = args[0] + "/nar-" + N + "-" + K + "-wf.csv";
    CSVWriter writer = new CSVWriter(new FileWriter(outputFilename), ',');
    String[] header = "rep,t,filter.type,measurement.type,resample.type,measurement".split(",");
    writer.writeNext(header);

    for (int k = 0; k < K; k++) {
      log.info("Processing replication " + k);

      GaussianArHpEvaluator wfEvaluator = new GaussianArHpEvaluator("wf-pl",
          truePsi, trueSigma2, writer);
      CountedDataDistribution<GaussianArHpWfParticle> wfDistribution =
          (CountedDataDistribution<GaussianArHpWfParticle>) wfFilter.getUpdater().createInitialParticles(N);

      final long numPreRuns = -1l;//wfDistribution.getMaxValueKey().getTime();
     
      /*
       * Recurse through the particle filter
       */
      for (int i = 0; i < T; i++) {
 
        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulations.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
          }
         
          wfEvaluator.evaluate(k, simulations.get(i), wfDistribution);
        }

        if ((i+1) % (T/4d) < 1) {
          log.info(Joiner.on("\t").join(new String[] {
              Integer.toString(k),
              Integer.toString(i),
              Double.toString(wfLatency.getMean().value),
              Double.toString(wfEvaluator.getStateLastRmse()),
              Double.toString(wfEvaluator.getSigma2LastRmse()),
              Double.toString(wfEvaluator.getPsiLastRmse())
              }));
          log.info(Joiner.on("\t").join(new String[] {
              Integer.toString(k),
              Integer.toString(i),
              Double.toString(wfLatency.getMean().value),
              "% " + Double.toString(wfEvaluator.getStateLastErrRate()),
              "% " + Double.toString(wfEvaluator.getSigma2LastErrRate()),
              "% " + Double.toString(wfEvaluator.getPsiLastErrRate())
              }));
        }
      }
      wfStateRMSEs.accumulate(new MutableDouble(wfEvaluator.getStateLastErrRate()));
      wfPsiRMSEs.accumulate(new MutableDouble(wfEvaluator.getPsiLastErrRate()));
      wfSigma2RMSEs.accumulate(new MutableDouble(wfEvaluator.getSigma2LastErrRate()));

    }

    writer.close();
  }
View Full Code Here
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