/// <summary>
/// Returns certainty associated with all possible input labels
/// </summary>
/// <param name="input"></param>
/// <returns></returns>
public Dictionary <Label, LabelLookupResult> Resolve(T input)
{
HeurisiticArray <T> ha = new HeurisiticArray <T>(input);
//Add a bunch of heuristics:
//ha.AddHeuristics()
return(Resolve(ha));
}
public InputProcessor(List <int> image)
{
heurisiticArray = new HeurisiticArray <List <int> >(new List <int>());
//define the functions which generate these heuristics
var pixelData = new HeuristicSet <List <int> >("Pixel Data", i => new List <int>(), image);
heurisiticArray.AddHeuristics(pixelData);
var colorData = new HeuristicSet <List <int> >("Color data", i => new List <int>(), image);
heurisiticArray.AddHeuristics(colorData);
}
public void Add(HeurisiticArray <T> trainingData)
{
foreach (var a in trainingData.HeuristicMethods())
{
//If contains, add. Otherwise create new
if (allTrainingData.ContainsKey(a))
{
allTrainingData[a].Add(trainingData);
}
else
{
allTrainingData[a] = new List <HeurisiticArray <T> >()
{
trainingData
};
}
}
}
public HeurisiticArray <int[][]> AccessElement(int at)
{
var a = lines.ElementAt(at);
var label = a.Split(',').First();
var data = a.Split(',').Skip(1).Select(v => int.Parse(v));
int[][] inputData = new int[28][];
for (int i = 0; i < 28; i++)
{
inputData[i] = new int[28];
}
for (int i = 0; i < data.Count(); i++)
{
inputData[i / 28][i % 28] = data.ElementAt(i);
}
var set = new HeuristicSet <int[][]>("pixels", pixel => data, inputData);
var b = new HeurisiticArray <int[][]>(inputData);
b.AddLabel(new Label(label));
b.AddHeuristics(set);
return(b);
}
//To resolve probabilistic information:
//Given a particular unlabeled heuristic value:
//For each possible label, how well does this value correlate (and anti-correlate)
//with a particular piece of input data?
//Dictionary<training data element, correlationVal>
public Dictionary <Label, LabelLookupResult> Resolve(HeurisiticArray <T> ha)
{
//Iterate over every possible output label
//For each output label iterate over every piece of training data
//For each piece of training data iterate over every heuristic
//Construct: Dictionary<Label, Dictionary<heurisitcMethod,List<Tuple<match, noMatch>>>
Dictionary <Label, HeuristicIndicationArray> indication
= new Dictionary <Label, HeuristicIndicationArray>();
foreach (var td in allTrainingData)
{
foreach (var heuristicArray in td.Value)
{
foreach (var method in ha.GetHeuristicMethods())
{
var heur1 = heuristicArray.GetHeuristics(method);
var heur2 = heuristicArray.GetHeuristics(method);
//Make sure that heur2 actually exists
indication[td.Key].Add(method, heur1, heur2);
}
}
}
throw new NotImplementedException();
}
public void Add(HeurisiticArray <int[][]> heuristicArray)
{
Library += heuristicArray.ToString() + "\n";
}
