Examples of edu.stanford.nlp.util.Timing.report()

edu.stanford.nlp.util.Timing.report()
Return elapsed time (without stopping timer). @return Number of milliseconds elapsed

          endTree = shuffledSentences.size();
        }


        executeOneTrainingBatch(shuffledSentences.subList(startTree, endTree), compressedParses, sumGradSquare);


        long totalElapsed = timing.report();
        System.err.println("Finished iteration " + iter + " batch " + batch + "; total training time " + totalElapsed + " ms");


        if (maxTrainTimeMillis > 0 && totalElapsed > maxTrainTimeMillis) {
          // no need to debug output, we're done now
          break;

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          }


          ++debugCycle;
        }
      }
      long totalElapsed = timing.report();


      if (maxTrainTimeMillis > 0 && totalElapsed > maxTrainTimeMillis) {
        // no need to debug output, we're done now
        System.err.println("Max training time exceeded, exiting");
        break;

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      if (timeStep > maxIterations) {
      sayln("Stochastic Optimization complete.  Stopped after max iterations");
      break;
      }
      sayln(System.out.format("Iter %d \t batch: %d \t time=%.2f \t obj=%.4f", iter, timeStep,
        total.report() / 1000.0, totalObjValue).toString());
    }
    }
  }
  return x;
  }

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    // take it on the safe side (implicit regularization)
    besteta /= factor;
    // determine t
    t0 = (int) (1 / (besteta * lambda));
    sayln("  Taking eta=" + besteta + " t0=" + t0);
    sayln("  Tuning completed in: " + Timing.toSecondsString(timer.report()) + " s");
    return besteta;
  }


  // really this is the square of the L2 norm....
  private static double getNorm(double[] w)

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          endTree = shuffledSentences.size();
        }


        executeOneTrainingBatch(model, shuffledSentences.subList(startTree, endTree), sumGradSquare);


        long totalElapsed = timing.report();
        System.err.println("Finished epoch " + epoch + " batch " + batch + "; total training time " + totalElapsed + " ms");


        if (maxTrainTimeMillis > 0 && totalElapsed > maxTrainTimeMillis) {
          // no need to debug output, we're done now
          break;

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          }


          ++debugCycle;
        }
      }
      long totalElapsed = timing.report();


      if (maxTrainTimeMillis > 0 && totalElapsed > maxTrainTimeMillis) {
        System.err.println("Max training time exceeded, exiting");
        break;
      }

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