public bool checkFood()
{
bool check = true;
List <Meal> positive = new DisplayDiet().GetMeals().ToList();
foreach (var i in positive)
{
if (i.decision.Equals("positive"))
{
check = true;
}
else
{
check = false;
}
}
return(check);
}
public List <Meal> ID3(dynamic food, List <User> users)
{
//dtable();
// Dictionary<string,List<Category>> FoodCategory = new Dictionary<string,List<Category>>();
List <Meal> category = new List <Meal> {
};
foreach (User u in users)
{
BMI = u.bmi;
User = u.user;
}
//Iterate through food and add each food with range of macronutrients to category list
foreach (var nw in food)
{
var value = (nw.Category.Predicted).Split(",");
category.Add(new Meal {
food = nw.Food.Food, protein = value[0], carb = value[1], fat = value[2], calorie = value[3], fiber = value[4], nprotein = nw.Food.Total_Protein, ncarb = nw.Food.Total_Carb, nfat = nw.Food.Total_Fat, ncalorie = nw.Food.Num_Calorie, nfiber = nw.Food.Fiber, serving = nw.Food.Serving, img = nw.Food.img, fgroup = nw.Food.fgroup, PrefID = nw.Food.ID, servingQty = nw.Food.ServingQty.ToString(), decision = ""
});
}
var csv = new CsvReader(File.OpenText(path));
var myCustomObjects = csv.GetRecords <MealData>();
DataTable dt = new DataTable("FoodDBSample");
DataRow row;
dt.Columns.Add("Category", "Carb", "Protein", "Fat", "Calorie", "Fiber", "Decision");
foreach (var record in myCustomObjects)
{
row = dt.NewRow();
row["Category"] = record.Category;
row["Carb"] = record.Carb;
row["Protein"] = record.Protein;
row["Fat"] = record.Fat;
row["Calorie"] = record.Calorie;
row["Fiber"] = record.Fiber;
row["Decision"] = record.Outcome;
dt.Rows.Add(row);
}
var codebook = new Codification(dt);
DataTable symbols = codebook.Apply(dt);
int[][] inputs = symbols.ToJagged <int>("Category", "Carb", "Protein", "Fat", "Calorie", "Fiber");
int[] outputs = symbols.ToArray <int>("Decision");
//specify which columns to use for making decisions
var id3learning = new ID3Learning()
{
new DecisionVariable("Category", 4),
new DecisionVariable("Carb", 2),
new DecisionVariable("Protein", 2),
new DecisionVariable("Fat", 2),
new DecisionVariable("Calorie", 2),
new DecisionVariable("Fiber", 2)
};
// Learn the training instances!
DecisionTree tree = id3learning.Learn(inputs, outputs);
// Compute the training error when predicting training instances
double error = new ZeroOneLoss(outputs).Loss(tree.Decide(inputs));
List <dynamic> predict = new List <dynamic> {
};
//iterate through data structure category and make prediction for each food
foreach (var item in category)
{
predict.Add(codebook.Transform(new[, ]
{
{ "Category", $"{BMI}" },
{ "Carb", $"{item.carb}" },
{ "Protein", $"{item.protein}" },
{ "Fat", $"{item.fat}" },
{ "Calorie", $"{item.calorie}" },
{ "Fiber", $"{item.fiber}" }
}));
}
//Accord.IO.Serializer.Save(tree, Path.Combine(basePath, "ID3TreeModel.bin"));
//int predicted = tree.Decide(query);
List <string> q = new List <string>();
foreach (var i in predict)
{
q.Add(codebook.Revert("Decision", tree.Decide(i)));
}
// We can translate it back to strings using
//string answer = codebook.Revert("Decision", predicted);
//foreach (var item in q)
//{
// System.Diagnostics.Debug.WriteLine(item);
//}
var foods = category.Zip(q, (n, w) => new { Food = n, Decision = w });
List <Meal> positive = new List <Meal> {
};
List <Meal> negative = new List <Meal> {
};
foreach (var nw in foods)
{
nw.Food.decision = nw.Decision;
}
foreach (var item in foods)
{
//System.Diagnostics.Debug.WriteLine(item.Food.food + "\n" + item.Food.nprotein + "\n" + item.Food.ncarb + "\n" + item.Food.nfat + "\n" + item.Food.ncalorie + "\n" + item.Food.serving + "\n" + item.Food.img + "\n" + item.Food.fgroup+"\n"+item.Decision);
//System.Diagnostics.Debug.WriteLine(item.Food.food+"\n"+item.Decision);
if (item.Decision.Equals("positive"))
{
positive.Add(new Meal {
CurrentUser = User, food = item.Food.food, nprotein = item.Food.nprotein, ncarb = item.Food.ncarb, nfat = item.Food.nfat, ncalorie = item.Food.ncalorie, nfiber = item.Food.nfiber, img = item.Food.img, serving = item.Food.serving, fgroup = item.Food.fgroup, PrefID = item.Food.PrefID, servingQty = item.Food.servingQty, decision = item.Decision
});
}
if (item.Decision.Equals("negative"))
{
negative.Add(new Meal {
CurrentUser = User, food = item.Food.food, nprotein = item.Food.nprotein, ncarb = item.Food.ncarb, nfat = item.Food.nfat, ncalorie = item.Food.ncalorie, nfiber = item.Food.nfiber, img = item.Food.img, serving = item.Food.serving, fgroup = item.Food.fgroup, PrefID = item.Food.PrefID, servingQty = item.Food.servingQty, decision = item.Decision
});
}
}
//foreach (var item in positive)
//{
// //System.Diagnostics.Debug.WriteLine(item.food + "\n" + item.nprotein + "\n" + item.ncarb + "\n" + item.nfat + "\n" + item.ncalorie + "\n" + item.serving + "\n" + item.img + "\n" + item.fgroup + "\n" + item.decision);
// System.Diagnostics.Debug.WriteLine(item.food + "\n" + "Protein: "+item.nprotein + "\n" + "Carb: " + item.ncarb + "\n" + "Fat: " + item.nfat + "\n" + "Calorie: " + item.ncalorie+"\n"+ "Fiber: "+item.nfiber+"\n"+ "Outcome: " + item.decision);
//}
//foreach (var item in negative)
//{
// //System.Diagnostics.Debug.WriteLine(item.food + "\n" + item.nprotein + "\n" + item.ncarb + "\n" + item.nfat + "\n" + item.ncalorie + "\n" + item.serving + "\n" + item.img + "\n" + item.fgroup + "\n" + item.decision);
// System.Diagnostics.Debug.WriteLine(item.food + "\n" + "Protein: " + item.nprotein + "\n" + "Carb: " + item.ncarb + "\n" + "Fat: " + item.nfat + "\n" + "Calorie: " + item.ncalorie + "\n" + "Fiber: " + item.nfiber + "\n" + "Outcome: " + item.decision);
//}
// Validation purposes only
//var cm = GeneralConfusionMatrix.Estimate(tree, inputs, outputs);
//double err = cm.Error;
//double acc = cm.Accuracy;
//double kappa = cm.Kappa;
//validation();
//System.Diagnostics.Debug.WriteLine("error: " + err);
//System.Diagnostics.Debug.WriteLine("accuracy: " + acc);
DisplayDiet diet = new DisplayDiet();
diet.GetFoods(positive);
diet.GetUser(users);
//diet.CreateDiet(positive);
return(positive);
}
