Ejemplo n.º 1
0
 /// <summary>
 /// Priors from posteriors.
 /// </summary>
 /// <param name="posteriors">The posteriors.</param>
 /// <param name="noiseVariance">The noise variance.</param>
 /// <returns>
 /// The <see cref="Priors" />
 /// </returns>
 internal static Priors FromPosteriors(Posteriors posteriors, double noiseVariance)
 {
     return(new Priors
     {
         Weights = posteriors.Weights,
         Threshold = posteriors.Threshold,
         NoiseVariance = noiseVariance
     });
 }
Ejemplo n.º 2
0
 /// <summary>
 /// Priors from posteriors with possible missing features.
 /// </summary>
 /// <param name="posteriors">The posteriors.</param>
 /// <param name="featureSet">The feature set.</param>
 /// <param name="noiseVariance">The noise variance.</param>
 /// <returns>The <see cref="Priors" /></returns>
 internal static Priors FromPosteriors(Posteriors posteriors, FeatureSet featureSet, double noiseVariance)
 {
     return(new Priors
     {
         Weights =
             featureSet.FeatureBuckets.ToDictionary(
                 ia => ia,
                 ia =>
                 posteriors.Weights.ContainsKey(ia)
                                ? posteriors.Weights[ia]
                                : Gaussian.FromMeanAndVariance(0.0, noiseVariance)),
         Threshold = posteriors.Threshold,
         NoiseVariance = noiseVariance,
     });
 }
Ejemplo n.º 3
0
        /// <summary>
        /// Runs the experiment.
        /// </summary>
        /// <param name="trainModel">The train model.</param>
        /// <param name="testModel">The test model.</param>
        /// <param name="inputs">The inputs.</param>
        /// <param name="priors">The priors.</param>
        /// <param name="thresholdAndNoiseVariance">The threshold and noise variance.</param>
        /// <param name="limit">The limit.</param>
        /// <param name="trainMode">The train mode.</param>
        /// <param name="testMode">The test mode.</param>
        public void Run(
            ReplyToModelBase trainModel,
            ReplyToModelBase testModel,
            Inputs inputs,
            Priors priors,
            double thresholdAndNoiseVariance,
            int limit           = -1,
            InputMode trainMode = InputMode.Training,
            InputMode testMode  = InputMode.Validation | InputMode.Testing)
        {
            this.ModelName      = trainModel.Name;
            this.FeatureSetName = inputs.FeatureSet.ToString();
            trainModel.ConstructModel();
            testModel.ConstructModel();

            int numTrain = trainMode == InputMode.Training ? inputs.Train.Count : inputs.TrainAndValidation.Count;

            if (limit > 0)
            {
                numTrain = Math.Min(numTrain, limit);
            }

            if (this.BatchSize == -1)
            {
                this.BatchSize = numTrain;
            }

            int numBatches = 1;

            if (this.Mode == ExperimentMode.Incremental || this.Mode == ExperimentMode.Online)
            {
                numBatches = numTrain / this.BatchSize;
            }

            for (int index = 0; index < numBatches; index++)
            {
                var batchResults = new Results();
                var batch        = this.GetBatch(inputs, index, trainMode);

                if (numBatches > 1)
                {
                    Console.WriteLine(@"{0} batch {1}/{2}, batch size {3}", this.UserName, index + 1, numBatches, batch.Count);
                }

                var stopwatch = new Stopwatch();
                stopwatch.Start();

                trainModel.SetObservedValues(batch, inputs.FeatureSet, InputMode.Training, priors);

                try
                {
                    trainModel.DoInference(inputs.FeatureSet, ref batchResults);
                }
                catch (Exception e)
                {
                    Console.WriteLine(e.Message);
                    return;
                }

                stopwatch.Stop();
                this.TrainTimings.Add(stopwatch.Elapsed);

                // Save the last set of posteriors
                this.Posteriors = batchResults.Posteriors;

                // Set the priors to be the posteriors from the previous run
                var newPriors = Priors.FromPosteriors(this.Posteriors, inputs.FeatureSet, thresholdAndNoiseVariance);

                if (this.Mode == ExperimentMode.Online || this.Mode == ExperimentMode.Incremental)
                {
                    if (this.Mode == ExperimentMode.Online)
                    {
                        priors = newPriors;
                    }

                    // clear posteriors to save on memory
                    batchResults.Posteriors = null;
                }

                stopwatch.Restart();

                // Validation and Test only
                testModel.Apply(inputs, inputs.FeatureSet, newPriors, ref batchResults, testMode);

                stopwatch.Stop();

                this.TestTimings.Add(stopwatch.Elapsed);

                this.Results.Add(batchResults);
                this.Metrics.Add(new Metrics(inputs, batchResults, this.Mode));

                this.Count++;
            }
        }