private Node[] Epoch(int t, Drink inputDrink, Node[] nodesArray)
{
BestMatchingNodeMultiThreadedSearcher bestMatchingNodeMultiThreadedSearcher = new BestMatchingNodeMultiThreadedSearcher(nodesArray, processorsCount, inputDrink.FeaturesArray);
int winningNodeIndex = bestMatchingNodeMultiThreadedSearcher.GetBestMatchingNodeIndex();
double neighbourhoodRadius = Radius(t);
double learningRate = LearningRate(t);
potentialsArray[winningNodeIndex] -= (_minNeuronPotential + maxNeuronRestTimeInv);
UpdatePotentialsArray(maxNeuronRestTimeInv);
System.Threading.Tasks.Parallel.ForEach(nodesArray, node =>
{
double theta = Theta(t, nodesArray[winningNodeIndex], node, neighbourhoodRadius);
if (theta > 0)
{
node.AdjustWeights(theta, learningRate, inputDrink.FeaturesArray);
node.DrinkID = inputDrink.ID;
}
});
return nodesArray;
}
private void RefreshListViewSimilarDrinks(Drink[] similarDrinksArray)
{
lv_SimilarDrinks.BeginUpdate();
lv_SimilarDrinks.Items.Clear();
lv_SimilarDrinks.LargeImageList.Images.Clear();
System.Collections.Concurrent.ConcurrentDictionary<int, Image> dictImages = new System.Collections.Concurrent.ConcurrentDictionary<int, Image>();
System.Threading.Tasks.Parallel.ForEach( similarDrinksArray, drink =>
{
dictImages.TryAdd(drink.ID,GetDrinkImage(drink).ResizeImage(SIMILAR_DRINK_IMAGE_SIZE));
});
foreach(Drink drink in similarDrinksArray)
{
lv_SimilarDrinks.LargeImageList.Images.Add(drink.ID.ToString(),dictImages[drink.ID]);
ListViewItem lvi = new ListViewItem(drink.Name);
lvi.Tag = drink.Url;
lvi.ImageKey = drink.ID.ToString();
lv_SimilarDrinks.Items.Add(lvi);
}
/*
for (int i = 0; i < similarDrinksArray.Length; i++)
{
lv_SimilarDrinks.LargeImageList.Images.Add(GetDrinkImage(similarDrinksArray[i]).ResizeImage(SIMILAR_DRINK_IMAGE_SIZE));
ListViewItem lvi = new ListViewItem(similarDrinksArray[i].Name);
lvi.Tag = similarDrinksArray[i].Url;
lvi.ImageIndex = i;
lv_SimilarDrinks.Items.Add(lvi);
}
*/
lv_SimilarDrinks.EndUpdate();
}
private Node[] InitializeNodes(Drink[] trainingDataBaseArray)
{
List<Drink> trainingData = trainingDataBaseArray.ToList();
int nodesNumber = neuralMapWidth * neuralMapHeight;
Node[] nodesArray = new Node[nodesNumber];
for (int i = 0; i < nodesNumber; i++)
{
if( trainingData.Count == 0)
trainingData = trainingDataBaseArray.ToList();
int index = randomizer.Next(trainingData.Count);
Drink drink = trainingData[index];
nodesArray[i] = new Node(trainingData[index].FeaturesArray,(i % neuralMapWidth) * distanceBetweenNeurons, (i / neuralMapWidth) * distanceBetweenNeurons, drink.ID);
trainingData.RemoveAt(index);
}
return nodesArray;
}
private Image GetDrinkImage(Drink drink)
{
if (drinksImagesDictionary.ContainsKey(drink.ID))
{
return drinksImagesDictionary[drink.ID];
}
else
{
Image drinkImage = GetImageFromUrl(drink.ImageUrl);
drinksImagesDictionary.Add(drink.ID, drinkImage);
return drinkImage;
}
}
