private static double computePo(
final IScope scope,
final IAgentFilter filter,
final Map<Object, Integer> categoriesId,
final GamaMatrix<Double> fuzzytransitions,
final Double distance,
final IList<Object> valsInit,
final IList<Object> valsObs,
final IList<Object> valsSim,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final int nbCat,
final int nb,
final IList<Double> similarities,
final IList<Object> weights) {
Map<IAgent, Integer> agsId = new TOrderedHashMap<IAgent, Integer>();
for (int i = 0; i < agents.length(scope); i++) {
agsId.put(agents.get(scope, i), i);
}
for (int i = 0; i < nb; i++) {
Object valObs = valsObs.get(i);
Object valSim = valsSim.get(i);
Object valInit = valsInit.get(i);
int valObsId = categoriesId.get(valObs);
int valSimId = categoriesId.get(valSim);
int valInitId = categoriesId.get(valInit);
IAgent agent = agents.get(scope, i);
double[] XaXs =
computeXaXs(
scope,
filter,
categoriesId,
agsId,
valObsId,
valSimId,
valInitId,
fuzzytransitions,
distance,
agent,
valsInit,
valsObs,
valsSim,
agents,
nbCat);
similarities.add(FastMath.min(XaXs[0], XaXs[1]));
}
double meanSimilarity = 0;
double total = 0;
for (int i = 0; i < nb; i++) {
double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
double val = weight * similarities.get(i);
total += weight;
meanSimilarity += val;
}
meanSimilarity /= total;
return meanSimilarity;
}
private static double computeSimilarity(
final IScope scope,
final IAgentFilter filter,
final Double distance,
final IList<Object> vals1,
final IList<Object> vals2,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final int nbCat,
final int nb,
final double[][] crispVector1,
final double[][] crispVector2,
final boolean[] sim,
final double[][] fuzzyVector1,
final double[][] fuzzyVector2,
final IList<Double> similarities,
final IList<Object> weights) {
Map<IAgent, Integer> agsId = new TOrderedHashMap<IAgent, Integer>();
for (int i = 0; i < agents.length(scope); i++) {
agsId.put(agents.get(scope, i), i);
}
for (int i = 0; i < nb; i++) {
if (sim[i]) {
similarities.add(1.0);
} else {
IAgent agent = agents.get(scope, i);
// double sizeNorm = agent.getPerimeter() / 4.0;
double sizeNorm = FastMath.sqrt(agent.getEnvelope().getArea());
List<IAgent> neighbors =
distance == 0 || filter == null
? new ArrayList<IAgent>()
: new ArrayList<IAgent>(
scope.getTopology().getNeighborsOf(scope, agent, distance, filter));
Map<IAgent, Double> distancesCoeff = new TOrderedHashMap<IAgent, Double>();
distancesCoeff.put(agent, 1.0);
for (IAgent ag : neighbors) {
double euclidDist = agent.getLocation().euclidianDistanceTo(ag.getLocation());
distancesCoeff.put(ag, 1 / (1.0 + euclidDist / sizeNorm));
}
for (int j = 0; j < nbCat; j++) {
double max1 = 0.0;
double max2 = 0.0;
for (IAgent ag : neighbors) {
int id = agsId.get(ag);
double val1 = crispVector1[id][j] * distancesCoeff.get(ag);
double val2 = crispVector2[id][j] * distancesCoeff.get(ag);
if (val1 > max1) {
max1 = val1;
}
if (val2 > max2) {
max2 = val2;
}
}
fuzzyVector1[i][j] = max1;
fuzzyVector2[i][j] = max2;
}
double s1Max = -1 * Double.MAX_VALUE;
double s2Max = -1 * Double.MAX_VALUE;
for (int j = 0; j < nbCat; j++) {
double s1 = FastMath.min(fuzzyVector1[i][j], crispVector2[i][j]);
double s2 = FastMath.min(fuzzyVector2[i][j], crispVector1[i][j]);
if (s1 > s1Max) {
s1Max = s1;
}
if (s2 > s2Max) {
s2Max = s2;
}
}
similarities.add(FastMath.min(s1Max, s2Max));
}
}
double meanSimilarity = 0;
double total = 0;
for (int i = 0; i < nb; i++) {
double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
double val = weight * similarities.get(i);
total += weight;
meanSimilarity += val;
}
meanSimilarity /= total;
return meanSimilarity;
}
