/// <summary>
/// Processes the specified args.
/// </summary>
/// <param name="args">The args.</param>
public override void Process([NotNull] ServicePipelineArgs args)
{
Assert.ArgumentNotNull(args, "args");
using (var nopServiceClient = this.GetClient())
{
var categories = nopServiceClient.GetAllCategories();
var productClassifications =
categories.Select(
x =>
{
Classification classification = this.InstantiateEntity <Classification>();
classification.ExternalId = x.Id;
classification.ExternalParentId = x.ParentCategoryId;
classification.Description = x.Description;
classification.Name = x.Name;
classification.Created = x.CreatedOnUtc;
classification.Updated = x.UpdatedOnUtc;
return(classification);
}).ToList();
ClassificationGroup classificationGroup = this.InstantiateEntity <ClassificationGroup>();
classificationGroup.Classifications = productClassifications.AsReadOnly();
classificationGroup.Name = this.productClassificationGroupName;
classificationGroup.ExternalId = this.productClassificationGroupExternalId;
classificationGroup.Updated = DateTime.UtcNow;
args.Request.Properties["ClassificationGroups"] = new List <ClassificationGroup>
{
classificationGroup
};
nopServiceClient.Close();
}
}
public static ClassificationResult Classify(IList <Color> colors)
{
if (colors == null || colors.Count() != 96)
{
throw new Exception("Impossible d'obtenir le cube car une quantité différente de 96 couleurs a été transmise au classifier");
}
var groups = new List <ClassificationGroup>(6);
var points = new List <ClassifiedPoint>(96);
for (int i = 0; i < 96; i++)
{
points.Add(new ClassifiedPoint(colors[i]));
}
// distances entre points deux à deux trier par ordre croissant
var distances = new List <Distance>();
for (int i = 0; i < 96; i++)
{
for (int j = i + 1; j < 96; j++)
{
var dx = (points[i].MeasuredColor.R - points[j].MeasuredColor.R);
var dy = (points[i].MeasuredColor.G - points[j].MeasuredColor.G);
var dz = (points[i].MeasuredColor.B - points[j].MeasuredColor.B);
var d = dx * dx + dy * dy + dz * dz;
points[i].Distance[points[j]] = d;
points[j].Distance[points[i]] = d;
distances.Add(new Distance(points[i], points[j], d));
}
}
distances.Sort((x, y) => x.PointDistance - y.PointDistance);
for (int igGrp = 0; igGrp < 6; igGrp++)
{
// trouver la paire de point la plus proche qui n'appartient pas encore à un groupe
var nearest = distances.FirstOrDefault((x) => x.Point1.Group == null && x.Point2.Group == null);
var group = new ClassificationGroup(igGrp);
// les ajouter au groupe
group.Add(nearest.Point1);
group.Add(nearest.Point2);
// ajouter les 14 plus proches
for (int i = 0; i < 14; i++)
{
group.AddNearest();
}
groups.Add(group);
}
var res = new ClassificationResult();
res.Groups = groups;
res.Points = points;
return(res);
}
