/// <summary>Takes a model generated from the modeling engine and loads necessary data from the database to deliver relevance to a user interface.</summary>
/// <param name="model">Model from modeling engine</param>
/// <returns>CompiledModel object which contains full recipe information about the provided set.</returns>
public CompiledModel Compile(Model model)
{
var results = new CompiledModel();
var recipes = context.ReadRecipes(model.RecipeIds, ReadRecipeOptions.None);
results.RecipeIds = model.RecipeIds;
results.Pantry = model.Pantry;
results.Briefs = recipes.Select(r => { return new RecipeBrief(r); }).ToArray();
results.Recipes = recipes.Select(r => new SuggestedRecipe
{
Id = r.Id,
Ingredients = context.AggregateRecipes(r.Id).ToArray()
}).ToArray();
return results;
}
/// <summary>
/// Generates a model with the specified number of recipes and returns the recipe IDs in the optimal order.
/// </summary>
/// <param name="recipes">Number of recipes to generate</param>
/// <param name="scale">Scale indicating importance of optimal ingredient usage vs. user trend usage. 1 indicates ignore user trends, return most efficient set of recipes. 5 indicates ignore pantry and generate recipes user is most likely to rate high.</param>
/// <returns>An array up to size "recipes" containing recipes from DBSnapshot.</returns>
public Model Generate(int recipes, byte scale)
{
if (recipes > MaxSuggestions)
{
throw new ArgumentException("Modeler can only generate " + MaxSuggestions.ToString() + " recipes at a time.");
}
double temperature = 10000.0;
double deltaScore = 0;
const double AbsoluteTemperature = 0.00001;
this.totals = new Dictionary<IngredientNode, IngredientUsage>(IngredientNode.NextKey);
// Current set of recipes
var currentSet = new RecipeNode[recipes];
// Set to compare with current
var nextSet = new RecipeNode[recipes];
// Initialize with n random recipes
this.InitializeSet(currentSet);
// Check initial score
var score = this.GetScore(currentSet, scale);
var timer = new Stopwatch();
timer.Start();
while (temperature > AbsoluteTemperature)
{
nextSet = this.GetNextSet(currentSet); // Swap out a random recipe with another one from the available pool
deltaScore = this.GetScore(nextSet, scale) - score;
// if the new set has a smaller score (good thing)
// or if the new set has a higher score but satisfies Boltzman condition then accept the set
if ((deltaScore < 0) || (score > 0 && Math.Exp(-deltaScore / temperature) > this.random.NextDouble()))
{
nextSet.CopyTo(currentSet, 0);
score += deltaScore;
}
// cool down the temperature
temperature *= CoolingRate;
}
timer.Stop();
Log.InfoFormat("Generating set of {0} recipes took {1}ms.", recipes, timer.ElapsedMilliseconds);
var model = new Model(currentSet, this.profile.Pantry, score);
return model;
}
