/// <summary>
/// Handles learning for all of the categories, according to category alpha, category beta and
/// the value of the document being read.
/// </summary>
/// <param name="consumed"></param>
public void BatchLearn(Document consumed)
{
for (int i = 0; i < Simulation.instance.categories; ++i)
{
// handle learning for category i
// first, get the alpha and beta values for this specific category
ABPair catAB = state.categoriesAB[i];
// get the document 0-1 value for this value
float DocumentCategoryValue = consumed.values[i];
float nextA = sampleGaussian(meanLearningAlpha(DocumentCategoryValue), 0.2f) + catAB.alpha;
if (nextA <= 0)
{
catAB.alpha = 0.001f;
}
else
{
catAB.alpha = nextA;
}
float nextB = sampleGaussian(meanLearningBeta(DocumentCategoryValue), 0.2f) + catAB.beta;
if (nextB <= 0)
{
catAB.beta = 0.001f;
}
else
{
catAB.beta = nextB;
}
}
}
/// <summary>
/// Reimplementation of ChooseByPolicy to work with multidimensional documents
/// </summary>
/// <returns></returns>
public Document BatchChooseByPolicy()
{
List <float> samples = new List <float>();
var VecBuilder = Vector <float> .Build;
// calculate target vector of all categories
for (int i = 0; i < Settings.Categories; ++i)
{
// get alpha and beta values for each category
ABPair catAB = state.categoriesAB[i];
var betaDist = new Beta(catAB.alpha, catAB.beta);
float sample = (float)betaDist.Sample();
samples.Add(sample);
}
// vector representing the ideal document according to this agents alpha / beta values
var target = VecBuilder.DenseOfArray(samples.ToArray());
//samples.Clear();
Document chosen = state.documents[0];
float minDist = 2f;
for (int i = 0; i < state.documents.Count; ++i)
{
Document doc = state.documents[i];
var docVec = VecBuilder.DenseOfArray(doc.values.ToArray());
var distance = (float)Distance.Euclidean(target, docVec);
// take euclidean distance between
if (distance < minDist)
{
// update the minimum distance
minDist = distance;
// update the reference to closest document found so far
chosen = doc;
}
}
return(chosen);
}
