List of keepTraining() Examples

Examples of keepTraining()

org.grouplens.lenskit.iterative.TrainingLoopController.keepTraining()
Query whether the computation should stop. @param error The error of the last iteration completed (use {Gustav Lindqvist Double#POSITIVE_INFINITY} before the first iteration). @return {@code false} if the computation is finished.

Examples of org.grouplens.lenskit.iterative.TrainingLoopController.keepTraining()

            double uoff[] = new double[snapshot.getUserIds().size()];
            double ioff[] = new double[snapshot.getItemIds().size()];


            final TrainingLoopController trainingController = stoppingCondition.newLoop();
            double rmse = 0.0;
            while (trainingController.keepTraining(rmse)) {
                double sse = 0;
                for (IndexedPreference r : ratings) {
                    final int uidx = r.getUserIndex();
                    final int iidx = r.getItemIndex();
                    final double p = mean + uoff[uidx] + ioff[iidx];

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Examples of org.grouplens.lenskit.iterative.TrainingLoopController.keepTraining()

                                FeatureInfo.Builder fib) {
        double rmse = Double.MAX_VALUE;
        double trail = initialValue * initialValue * (featureCount - feature - 1);
        TrainingLoopController controller = rule.getTrainingLoopController();
        Collection<IndexedPreference> ratings = snapshot.getRatings();
        while (controller.keepTraining(rmse)) {
            rmse = doFeatureIteration(estimates, ratings, userFeatureVector, itemFeatureVector, trail);
            fib.addTrainingRound(rmse);
            logger.trace("iteration {} finished with RMSE {}", controller.getIterationCount(), rmse);
        }
    }

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Examples of org.grouplens.lenskit.iterative.TrainingLoopController.keepTraining()

            }
        }


        double rmse = Double.MAX_VALUE;
        TrainingLoopController controller = rule.getTrainingLoopController();
        while (controller.keepTraining(rmse)) {
            rmse = doFeatureIteration(user, uprefs, ratings, estimates, feature, tails);
        }
    }


    private double doFeatureIteration(long user, AVector uprefs,

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