using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.ComponentModel.DataAnnotations;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Reflection;
using Studia.Models;

namespace Studia
{
    public static class Program
    {
        public static bool Logging { get; set; }

        public static void Log(string message)
        {
            if(Logging)
                File.AppendAllLines("log.txt", new List<string>() { DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss") + ": " + message });
        }

        public static List<InputData> ParseData()
        {
            var lines = File.ReadAllLines(Path.GetDirectoryName(Assembly.GetEntryAssembly().Location) + @"\data.dat");
            return lines.Select(line => line.Split(','))
                .Select(inputParameters => new InputData()
                {
                    Preg = double.Parse(inputParameters[0], CultureInfo.InvariantCulture),
                    Plas = double.Parse(inputParameters[1], CultureInfo.InvariantCulture),
                    Pres = double.Parse(inputParameters[2], CultureInfo.InvariantCulture),
                    Skin = double.Parse(inputParameters[3], CultureInfo.InvariantCulture),
                    Insu = double.Parse(inputParameters[4], CultureInfo.InvariantCulture),
                    Mass = double.Parse(inputParameters[5], CultureInfo.InvariantCulture),
                    Pedi = double.Parse(inputParameters[6], CultureInfo.InvariantCulture),
                    Age = double.Parse(inputParameters[7], CultureInfo.InvariantCulture),
                    Result = (inputParameters[8].ToLower().Trim() == "tested_positive")
                })
                .ToList();
        }

        public static double EuklidesDistance(InputData a, InputData b) => Math.Pow((a.GetWeight - b.GetWeight), 2);
        public static double ManhatanDistance(InputData a, InputData b) => Math.Abs(a.GetWeight - b.GetWeight);

        public static List<InputData> GetKnn(this List<InputData> data, InputData measuredData, int k, Func<InputData, InputData, double> calculateDistanceMethod)
        {
            data.ForEach(measure =>
            {
                measure.Distance = calculateDistanceMethod(measure, measuredData);
                Log($"For measure: {measure.ToString()} distance is {measure.Distance}");
            });
            var orderedData = data.OrderBy(measure => measure.Distance).ToList();
            k = orderedData.Count - k > 0 ? k : 0;
            var neighbours = orderedData.Take(5).ToList();
            Log($"SELECTED {k} neighbors:");
            foreach (var neighbor in neighbours)
                Log($"For neighbor: {neighbor} distance is {neighbor.Distance}");
            return neighbours;
        }

        public static InputData ReadPatientsData()
        {
            var request = new InputData();
            typeof(InputData).GetProperties()
                .Where(prop => prop.GetCustomAttributes(typeof(RangeAttribute)).Any())
                .ToList()
                .ForEach(prop =>
                {
                    var description = prop.GetCustomAttribute<DescriptionAttribute>();
                    Console.WriteLine(description.Description);
                    var range = prop.GetCustomAttribute<RangeAttribute>();
                    Console.WriteLine($"Range between <{(double)range.Minimum};{(double)range.Maximum}>");
                    Console.WriteLine("Enter value: ");

                    double value;
                    while (!double.TryParse(Console.ReadLine(), out value) || value < (double)range.Minimum || value > (double)range.Maximum)
                    {
                        Console.WriteLine("Wrong value, try again");
                    }
                    prop.SetValue(request, value);
                    Log($"Patients data, entered property value {value} for {prop.Name} in range <{(double)range.Minimum};{(double)range.Maximum}>" );
                });
            return request;
        }


        static void Main(string[] args)
        {
            Logging = args.Any() && args[0] == "log";

            Console.WriteLine("From National Institute of Diabetes and Digestive and Kidney Diseases. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. ");
            Console.WriteLine("The class label represents if the person has not diabetes (tested_negative) or the person has diabetes (tested_positive). ");

            var data = ParseData();
            Console.Write("Enter k (kNN) parameter: ");
            var k = int.Parse(Console.ReadLine());
            var patientsData = ReadPatientsData();

            // TestCase k = 5 100%
            // var measure = new InputData()
            // {
            //     Preg = 3,
            //     Plas = 150,
            //     Pres = 76,
            //     Skin = 0,
            //     Insu = 0,
            //     Mass = 21,
            //     Pedi = 0.207,
            //     Age = 37
            // };

            Console.WriteLine("Choose distance algorithm");
            Console.WriteLine("1.Manhatan");
            Console.WriteLine("2.Euklides");
            Func<InputData, InputData, double> distanceMethod = null;
            if (Console.ReadLine() == "1")
                distanceMethod = ManhatanDistance;
            else
                distanceMethod = EuklidesDistance;

            var neighbors = data.GetKnn(patientsData, k, distanceMethod);
            Log($"For measurements: {patientsData}");

            var positive = neighbors.Count(x => x.Result);
            var negative = neighbors.Count(x => !x.Result);
            if(positive > negative)
                Console.WriteLine("tested_positive");
            else if (positive == negative)
                Console.WriteLine("50%");
            else
                Console.WriteLine("tested_negative");
            Console.WriteLine($"Positive: {positive}, Negative: {negative}");
            Log($"Positive: {positive}, Negative: {negative}");
            Console.ReadLine();
        }
    }
}




using System.ComponentModel;
using System.ComponentModel.DataAnnotations;

namespace Studia.Models
{
    public class InputData
    {
        [Description("Number of times pregnant ")]
        [Range(0.0, 17.0)]
        public double Preg { get; set; }
        [Description("Plasma glucose concentration a 2 hours in an oral glucose tolerance test ")]
        [Range(0.0, 199.0)]
        public double Plas { get; set; }
        [Description("Diastolic blood pressure (mm Hg) ")]
        [Range(0.0, 122.0)]
        public double Pres { get; set; }
        [Description("Triceps skin fold thickness (mm)  ")]
        [Range(0.0, 99.0)]
        public double Skin { get; set; }
        [Description("2-Hour serum insulin (mu U/ml) ")]
        [Range(0.0, 846.0)]
        public double Insu { get; set; }
        [Description("Body mass index (weight in kg/(height in m)^2) ")]
        [Range(0.0, 67.1)]
        public double Mass { get; set; }
        [Description("Diabetes pedigree function")]
        [Range(0.078, 2.42)]
        public double Pedi { get; set; }
        [Description("Age (years)")]
        [Range(21.0, 81.0)]
        public double Age { get; set; }
        public bool Result { get; set; }

        public override string ToString()
        {
            return $"Preg: {Preg}; Plas: {Plas}; Pres: {Pres}; Skin: {Skin}; Insu: {Insu}; Mass:{Mass}; Pedi:{Pedi}; Age:{Age}";
        }

        public double GetWeight => (Preg + Plas + Pres + Skin + Insu + Mass + Pedi + Age);
        public double Distance { get; set; }
    }
}