public async Task SaveClustersAsync()
{
var trainSamples = await _samplesService.GetTrainPhotosAsync();
var clusters = await BuildClustersAsync(_numberOfClusters, trainSamples);
await _clusterSamplePhotoRepository.DeleteAsync(x => true);
await _clusterRepository.DeleteAsync(x => true);
foreach (var cluster in clusters)
{
await _clusterRepository.CreateAsync(cluster);
}
_logger.LogInfo("Old clusters removed and new clusters added.");
}
public async Task KMeansResearchAsync()
{
var stepSize = (_kmeansClustersEnd - _kmeansClustersStart) / (_kmeansStages - 1);
var trainSamples = await _samplesService.GetTrainPhotosAsync();
var testSamples = await _samplesService.GetTestPhotosAsync();
var results = new List <KMeansResearchResult>();
for (var numOfClusters = _kmeansClustersStart; numOfClusters <= _kmeansClustersEnd; numOfClusters += stepSize)
{
var watch = Stopwatch.StartNew();
var clustersBuildWatch = Stopwatch.StartNew();
var clusters = await _clusterService.BuildClustersAsync(numOfClusters, trainSamples);
clustersBuildWatch.Stop();
var result = 0d;
var photoSearchWatch = Stopwatch.StartNew();
foreach (var testPhoto in testSamples)
{
var similarPhoto = await _photoService.FindMostSimilarPhotoAsync(testPhoto.Prediction, clusters);
result += CompareDescriptions(testPhoto.Description, similarPhoto.Description);
}
photoSearchWatch.Stop();
result = result / testSamples.Count();
watch.Stop();
results.Add(new KMeansResearchResult(numOfClusters, result,
watch.ElapsedMilliseconds, clustersBuildWatch.ElapsedMilliseconds, photoSearchWatch.ElapsedMilliseconds));
}
await _kMeansResearchRepository.DeleteAsync(x => true);
foreach (var kMeansResearchResult in results)
{
await _kMeansResearchRepository.CreateAsync(kMeansResearchResult);
}
}
