/// <summary>
/// Given a random model and a BeliefLevel
/// return the beliefBits for the agent: an array fill of random binaries
/// representing the detailed belief of an agent
/// </summary>
/// <param name="model"></param>
/// <param name="beliefLevel"></param>
/// <returns></returns>
public float[] InitializeBits(RandomGenerator model, BeliefLevel beliefLevel)
{
float[] beliefBits;
switch (model)
{
case RandomGenerator.RandomUniform:
{
float min;
float max;
if (beliefLevel == BeliefLevel.Random)
{
min = RangeMin;
max = RangeMax;
}
else
{
min = GetMinFromBeliefLevel(beliefLevel);
max = GetMaxFromBeliefLevel(beliefLevel);
}
beliefBits = ContinuousUniform.Samples(Length, min, max);
break;
}
case RandomGenerator.RandomBinary:
{
if (beliefLevel == BeliefLevel.Random)
{
beliefBits = ContinuousUniform.FilteredSamples(Length, RangeMin, RangeMax);
}
else
{
var mean = 1 - GetValueFromBeliefLevel(beliefLevel);
beliefBits = ContinuousUniform.FilteredSamples(Length, mean);
}
break;
}
default:
throw new ArgumentOutOfRangeException(nameof(model), model, null);
}
return(beliefBits);
}
/// <summary>
/// Given a KnowledgeModel and a KnowledgeLevel
/// return the knowledgeBits for the agent: an array fill of random binaries
/// representing the detailed knowledge of an agent
/// </summary>
/// <param name="length"></param>
/// <param name="model"></param>
/// <param name="knowledgeLevel"></param>
/// <param name="step"></param>
/// <returns></returns>
public void InitializeKnowledge(byte length, RandomGenerator model, KnowledgeLevel knowledgeLevel, ushort step)
{
float[] knowledgeBits;
switch (model)
{
case RandomGenerator.RandomUniform:
{
float min;
float max;
switch (knowledgeLevel)
{
case KnowledgeLevel.Random:
min = 0;
max = 1;
break;
default:
min = Knowledge.GetMinFromKnowledgeLevel(knowledgeLevel);
max = Knowledge.GetMaxFromKnowledgeLevel(knowledgeLevel);
break;
}
knowledgeBits = ContinuousUniform.Samples(length, min, max);
if (Math.Abs(min - max) < Constants.Tolerance)
{
SetKnowledgeBits(knowledgeBits, step);
return;
}
for (byte i = 0; i < knowledgeBits.Length; i++)
{
if (knowledgeBits[i] < min * (1 + 0.05))
{
// In randomUniform, there is quasi no bit == 0. But in reality, there are knowledgeBit we ignore.
// We force the lowest (Min +5%) knowledgeBit to 0
knowledgeBits[i] = 0;
}
}
break;
}
case RandomGenerator.RandomBinary:
{
switch (knowledgeLevel)
{
case KnowledgeLevel.Random:
knowledgeBits = ContinuousUniform.FilteredSamples(length, 0, 1);
break;
default:
var mean = 1 - Knowledge.GetValueFromKnowledgeLevel(knowledgeLevel);
knowledgeBits = ContinuousUniform.FilteredSamples(length, mean);
break;
}
break;
}
default:
throw new ArgumentOutOfRangeException(nameof(model), model, null);
}
SetKnowledgeBits(knowledgeBits, step);
}
