json flattener and naivebayes inference

jf_nbi.cs

void Main()
{
	var exampleJson = @"
{
	""random"": 37,
	""random float"": 51.886,
	""bool"": true,
	""date"": ""1982-05-19"",
	""regEx"": ""hellooooooooo to you"",
	""enum"": ""json"",
	""firstname"": ""Henriette"",
	""lastname"": ""Solitta"",
	""city"": ""Melekeok"",
	""country"": ""United States"",
	""countryCode"": ""SD"",
	""email uses current data"": ""Henriette.Solitta@gmail.com"",
	""email from expression"": ""Henriette.Solitta@yopmail.com"",
	""array"": [
		""Tani"",
		""Tilly"",
		""Blake"",
		""Lusa"",
		""Codie""
	],
	""array of objects"": [
		{
			""index"": 0,
			""index start at 5"": 5
		},
		{
			""index"": 1,
			""index start at 5"": 6
		},
		{
			""index"": 2,
			""index start at 5"": 7
		}
	]
}
";
	new JsonFlattener().FlattenText(exampleJson).Dump();
	
}

void NaiveBayes(List<List<string>> causes, List<List<string>> effects)
{
	Dictionary<T, int> countEvents<T>(IEnumerable<IEnumerable<T>> collection, IEnumerable<T> distinctEvents)
	{
		return distinctEvents
			.Select(e => (e, collection.Where(t => t.Contains(e)).Count()))
			.ToDictionary(p => p.Item1, p => p.Item2);
	}

	var causeEvents = causes.SelectMany(x => x).Distinct();
	var effectEvents = effects.SelectMany(x => x).Distinct();

	var causeEventCount = countEvents(causes, causeEvents);
	var effectEventCount = countEvents(effects, effectEvents);

	var causeEventMargProb = causeEventCount
		.ToDictionary(x => x.Key, x => Math.Log((double)x.Value) - Math.Log((double)causes.Count));

	var effectEventMargProb = effectEventCount
		.ToDictionary(x => x.Key, x => Math.Log((double)x.Value) - Math.Log((double)effects.Count));

	var joinEvents = Enumerable.Zip(causes, effects)
		.SelectMany(
			x => (
				from a in x.First
				from b in x.Second
				select (a, b))
			);

	var joinProb = (
		from c in causeEvents
		from e in effectEvents
		select new
		{
			Events = new { Cause = c, Effect = e },
			Probability = Math.Log((double)joinEvents.Where(x => x == (c, e)).Count()) - Math.Log((double)joinEvents.Count())
		});
	
	var likelyEffects = causeEvents.
		ToDictionary(
			x => x, 
			x => joinProb
				.Where(je => je.Events.Cause == x)
				.Select(je => 
					(je.Events.Effect, 
					 je.Probability - causeEventMargProb[je.Events.Cause]))
				.ToList()
				.OrderByDescending(x => x.Item2)
			);
	
	likelyEffects.Dump();
}

public class JsonFlattener
{
	public List<string> FlattenText(string jsonStr)
	{
		var token = JToken.Parse(jsonStr);
		var result = new List<string>();
		FlattenRecursive(token, "_", result);
		return result;
	}
	
	private void FlattenRecursive(JToken token, string namePrefix, List<string> resultList, bool includeValue=true, bool includeHasValue=true)
	{
		if(token.Type == JTokenType.Array)
		{
			int i = 0;
			foreach(var child in token.Children())
			{
				var newPrefix = namePrefix + "[" + i.ToString() + "]";
				FlattenRecursive(child, newPrefix, resultList, includeValue, includeHasValue);
				i++;
			}
		}
		else if (token.Type == JTokenType.Object)
		{
			JObject obj = (JObject)token;
			foreach (var item in obj)
			{
				var newPrefix = namePrefix + "." + item.Key;
				FlattenRecursive(item.Value, newPrefix, resultList, includeValue, includeHasValue);
			}
		}
		else
		{
			if(includeHasValue) resultList.Add(namePrefix + "_HasValue");
			if(includeValue) resultList.Add(namePrefix + "=" + token.ToString());
		}
	}
	
}


public interface IEventListor<T>
	where T : IEquatable<T>
{
	IList<T> GetElements();
}