private void LoadTrainingData(IDBLoader loader)
{
this.trainingData = new Dictionary<Guid, IRecipeClassification>();
this.breakfastIndex = new RecipeIndex();
this.lunchIndex = new RecipeIndex();
this.dinnerIndex = new RecipeIndex();
this.dessertIndex = new RecipeIndex();
var data = loader.LoadTrainingData();
foreach (var recipeClassification in data)
{
this.trainingData.Add(recipeClassification.Recipe.Id, recipeClassification);
if (recipeClassification.IsBreakfast)
{
this.breakfastIndex.Add(recipeClassification.Recipe);
}
if (recipeClassification.IsLunch)
{
this.lunchIndex.Add(recipeClassification.Recipe);
}
if (recipeClassification.IsDinner)
{
this.dinnerIndex.Add(recipeClassification.Recipe);
}
if (recipeClassification.IsDessert)
{
this.dessertIndex.Add(recipeClassification.Recipe);
}
}
}
private void Compete(Recipe entry, RecipeIndex first, RecipeIndex second, Ranking rankBreakfast, Ranking rankLunch, Ranking rankDinner, Ranking rankDessert)
{
var chance = this.GetPrediction(entry, first, second);
if (chance > 0.5f - Tolerance && chance < 0.5f + Tolerance)
{
return; // No winner
}
var diff = (float)Math.Abs(chance - 0.5);
var winner = chance < 0.5 ? second : first;
if (winner == this.breakfastIndex)
{
rankBreakfast.Score += diff;
}
if (winner == this.lunchIndex)
{
rankLunch.Score += diff;
}
if (winner == this.dinnerIndex)
{
rankDinner.Score += diff;
}
if (winner == this.dessertIndex)
{
rankDessert.Score += diff;
}
}
private float GetPrediction(Recipe recipe, RecipeIndex first, RecipeIndex second)
{
// Reset probability and invertedProbability
this.invertedProbability = 0;
this.probability = 0;
var tokens = Tokenizer.Tokenize(recipe);
foreach (var token in tokens)
{
var firstRITokensCount = first.GetTokenCount(token);
var secondRITokensCount = second.GetTokenCount(token);
this.CalcProbability(firstRITokensCount, first.EntryCount, secondRITokensCount, second.EntryCount);
}
var prediction = this.probability / (this.probability + this.invertedProbability);
return prediction;
}
