public void LoadFromXml(XDocument xmlReader)
{
float minimalSalienceThreshold;
var propertiesElement = xmlReader.Root.Element("properties");
normativeMatchDistance = float.Parse(propertiesElement.Element("normative_match_distance").Value.ToString(), CultureInfo.InvariantCulture);
distanceConstraint = float.Parse(propertiesElement.Element("distance_constraint").Value.ToString(), CultureInfo.InvariantCulture);
comparativeFitAlfa = float.Parse(propertiesElement.Element("comparative_fit_alfa").Value.ToString(), CultureInfo.InvariantCulture);
comparativeFitBeta = float.Parse(propertiesElement.Element("comparative_fit_beta").Value.ToString(),
CultureInfo.InvariantCulture);
minimalSalienceThreshold = float.Parse(propertiesElement.Element("minimal_salience_threshold").Value.ToString(),
CultureInfo.InvariantCulture);
//
agents = new List <Agent>();
foreach (var element in xmlReader.Root.Element("agents").Elements("agent"))
{
var agent = new Agent();
agent.characteristics = new Dictionary <Characteristic, float>();
agent.knowledgeBase = new List <SocialGroup>();
agent.minimalSalienceThreshold = minimalSalienceThreshold;
agent.name = element.Attribute("name").Value;
foreach (var subElement in element.Elements("characteristic"))
{
var characteristic = new Characteristic();
characteristic.name = subElement.Attribute("name").Value;
agent.characteristics.Add(characteristic, float.Parse(subElement.Value, CultureInfo.InvariantCulture));
}
foreach (var subElement in element.Element("knowledge_base").Elements("social_group"))
{
var socialGroup = new SocialGroup();
socialGroup.characteristics = new Dictionary <Characteristic, float>();
socialGroup.name = subElement.Attribute("name").Value;
foreach (var subEubElement in subElement.Elements("characteristic"))
{
var characteristic = new Characteristic();
characteristic.name = subEubElement.Attribute("name").Value;
socialGroup.characteristics.Add(characteristic, float.Parse(subEubElement.Value, CultureInfo.InvariantCulture));
}
socialGroup.CalculateAccessibility(agent);
agent.knowledgeBase.Add(socialGroup);
}
// agent.NormalizeAccessibility();
agents.Add(agent);
}
}
public void CalculateNormativeFit(Context currentContext, Simulation simulation)
{
int numDimensions = currentContext.relevantCharacteristcs.Count;
for (int c = 0; c < clusterMeans.Count; c++)
{
var clusterMean = clusterMeans[c];
float bestDistance = float.MaxValue;
foreach (var socialGroup in knowledgeBase)
{
bool validGroup = true;
float distance = 0.0f;
for (int i = 0; i < numDimensions; i++)
{
float groupValue = 0.0f;
if (!socialGroup.characteristics.TryGetValue(currentContext.relevantCharacteristcs[i], out groupValue))
{
validGroup = false;
break;
}
float weight = currentContext.relevantCharacteristcs[i].weight;
float difference = (clusterMean.mean[i] - groupValue) * weight;
distance += difference * difference;
}
if (!validGroup)
{
continue;
}
distance = (float)Math.Sqrt((double)distance);
if (distance < bestDistance)
{
bestDistance = distance;
clusterMean.matchedGroup = socialGroup;
}
}
// ad-hoc group
if (bestDistance > simulation.normativeMatchDistance * currentContext.GetMaxDistance())
{
numAdhocGroups++;
SocialGroup adhocGroup = new SocialGroup();
adhocGroup.isAdHoc = true;
adhocGroup.characteristics = new Dictionary <Characteristic, float>();
adhocGroup.name = "Group " + numAdhocGroups;
for (int i = 0; i < numDimensions; i++)
{
adhocGroup.characteristics.Add(currentContext.relevantCharacteristcs[i], clusterMean.mean[i]);
}
adhocGroup.CalculateAccessibility(this);
knowledgeBase.Add(adhocGroup);
clusterMean.matchedGroup = adhocGroup;
}
clusterMeans[c] = clusterMean;
}
}