@Deprecated
@operator(
value = "neighbors_of",
content_type = IType.AGENT,
category = {IOperatorCategory.SPATIAL, IOperatorCategory.SP_QUERIES})
/* TODO, expected_content_type = { IType.FLOAT, IType.INT } */
@doc(
deprecated = "Use 'neighbours_of(topology, agent, distance)' instead",
usages =
@usage(
value =
"a list, containing all the agents of the same species than the key of the pair argument (if it is an agent) located at a distance inferior or equal to the right member (float) of the pair (right-hand operand) to the left member (agent, geometry or point) considering the left-hand operand topology.",
examples = {
@example(
value = "topology(self) neighbors_of self::10",
equals =
"all the agents located at a distance lower or equal to 10 to the agent applying the operator considering its topology.",
test = false)
}))
public static IList neighbours_of(final IScope scope, final ITopology t, final GamaPair pair) {
if (pair == null) {
return GamaListFactory.EMPTY_LIST;
}
Object agent = pair.key;
return Spatial.Queries._neighbours(
scope,
agent instanceof IAgent
? In.list(scope, ((IAgent) agent).getPopulation())
: Different.with(),
agent,
pair.value,
t);
// TODO We could compute a filter based on the population if it is an agent
}
@operator(
value = {"fuzzy_kappa_sim"},
content_type = IType.FLOAT,
category = {IOperatorCategory.MAP_COMPARAISON},
concept = {IConcept.MAP})
@doc(
value =
"fuzzy kappa simulation indicator for 2 map comparisons: fuzzy_kappa_sim(agents_list,list_vals1,list_vals2, output_similarity_per_agents,fuzzy_transitions_matrix, fuzzy_distance, weights). Reference: Jasper van Vliet, Alex Hagen-Zanker, Jelle Hurkens, Hedwig van Delden, A fuzzy set approach to assess the predictive accuracy of land use simulations, Ecological Modelling, 24 July 2013, Pages 32-42, ISSN 0304-3800, ",
examples = {
@example(
value =
"fuzzy_kappa_sim([ag1, ag2, ag3, ag4, ag5], [cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2], similarity_per_agents,[cat1,cat2,cat3],[[1,0,0,0,0,0,0,0,0],[0,1,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0],[0,0,0,0,1,0,0,0,0],[0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,0],[0,0,0,0,0,0,0,0,1]], 2,[1.0,3.0,2.0,2.0,4.0])",
isExecutable = false)
})
public static double fuzzyKappaSimulation(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<Object> valsInit,
final IList<Object> valsObs,
final IList<Object> valsSim,
final IList<Double> similarities,
final List<Object> categories,
final GamaMatrix<Double> fuzzytransitions,
final Double distance,
final IList<Object> weights) {
if (agents == null) {
return 1;
}
int nb = agents.length(scope);
if (nb < 1) {
return 1;
}
similarities.clear();
int nbCat = categories.size();
double[] nbObs = new double[nbCat];
double[] nbSim = new double[nbCat];
double[] nbInit = new double[nbCat];
double[][] nbInitObs = new double[nbCat][nbCat];
double[][] nbInitSim = new double[nbCat][nbCat];
Map<Object, Integer> categoriesId = new TOrderedHashMap<Object, Integer>();
Map<List<Integer>, Map<Double, Double>> XaPerTransition =
new TOrderedHashMap<List<Integer>, Map<Double, Double>>();
Map<List<Integer>, Map<Double, Double>> XsPerTransition =
new TOrderedHashMap<List<Integer>, Map<Double, Double>>();
Set<Double> Xvals = new HashSet<Double>();
for (int i = 0; i < nbCat; i++) {
categoriesId.put(categories.get(i), i);
}
for (int i = 0; i < nbCat; i++) {
nbInit[i] = 0;
nbObs[i] = 0;
nbSim[i] = 0;
for (int j = 0; j < nbCat; j++) {
nbInitObs[i][j] = 0;
nbInitSim[i][j] = 0;
}
}
IAgentFilter filter = In.list(scope, agents);
double total = 0;
for (int i = 0; i < nb; i++) {
double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
total += weight;
int idCatInit = categoriesId.get(valsInit.get(i));
int idCatObs = categoriesId.get(valsObs.get(i));
int idCatSim = categoriesId.get(valsSim.get(i));
nbInit[idCatInit] += weight;
nbSim[idCatSim] += weight;
nbObs[idCatObs] += weight;
nbInitObs[idCatInit][idCatObs] += weight;
nbInitSim[idCatInit][idCatSim] += weight;
}
double po =
computePo(
scope,
filter,
categoriesId,
fuzzytransitions,
distance,
valsInit,
valsObs,
valsSim,
agents,
nbCat,
nb,
similarities,
weights);
double pe = 0;
computeXaXsTransitions(
scope,
filter,
fuzzytransitions,
distance,
agents,
nbCat,
XaPerTransition,
XsPerTransition,
Xvals);
for (int i = 0; i < nbCat; i++) {
for (int j = 0; j < nbCat; j++) {
for (int k = 0; k < nbCat; k++) {
List<Integer> ca = new ArrayList<Integer>();
ca.add(i);
ca.add(j);
ca.add(k);
Map<Double, Double> pmuXa = XaPerTransition.get(ca);
Map<Double, Double> pmuXs = XsPerTransition.get(ca);
double emu = 0;
for (Double xval : Xvals) {
double XaVal = pmuXa == null || !pmuXa.containsKey(xval) ? 0 : pmuXa.get(xval);
double XsVal = pmuXs == null || !pmuXs.containsKey(xval) ? 0 : pmuXs.get(xval);
double proba = xval * XaVal * XsVal;
emu += proba;
}
double poas = nbInit[i] == 0 ? 0 : nbInitObs[i][j] / nbInit[i] * nbInitSim[i][k] / total;
pe += emu * poas;
}
}
}
if (pe == 1) {
return 1;
}
return (po - pe) / (1 - pe);
}
@operator(
value = {"fuzzy_kappa"},
content_type = IType.FLOAT,
category = {IOperatorCategory.MAP_COMPARAISON},
concept = {})
@doc(
value =
"fuzzy kappa indicator for 2 map comparisons: fuzzy_kappa(agents_list,list_vals1,list_vals2, output_similarity_per_agents,categories,fuzzy_categories_matrix, fuzzy_distance, weights). Reference: Visser, H., and T. de Nijs, 2006. The map comparison kit, Environmental Modelling & Software, 21",
examples = {
@example(
value =
"fuzzy_kappa([ag1, ag2, ag3, ag4, ag5],[cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2], similarity_per_agents,[cat1,cat2,cat3],[[1,0,0],[0,1,0],[0,0,1]], 2, [1.0,3.0,2.0,2.0,4.0])",
isExecutable = false)
})
public static double fuzzyKappa(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<Object> vals1,
final IList<Object> vals2,
final IList<Double> similarities,
final List<Object> categories,
final GamaMatrix<Double> fuzzycategories,
final Double distance,
final IList<Object> weights) {
if (agents == null) {
return 1;
}
int nb = agents.length(scope);
if (nb < 1) {
return 1;
}
int nbCat = categories.size();
similarities.clear();
boolean[] sim = new boolean[nb];
double[][] crispVector1 = new double[nb][nbCat];
double[][] crispVector2 = new double[nb][nbCat];
double[][] fuzzyVector1 = new double[nb][nbCat];
double[][] fuzzyVector2 = new double[nb][nbCat];
double[] X = new double[nbCat];
double[] Y = new double[nbCat];
Map<Object, Integer> categoriesId = new TOrderedHashMap<Object, Integer>();
for (int i = 0; i < nbCat; i++) {
categoriesId.put(categories.get(i), i);
}
IAgentFilter filter = In.list(scope, agents);
computeXYCrispVector(
scope,
categoriesId,
categories,
vals1,
vals2,
fuzzycategories,
nbCat,
nb,
crispVector1,
crispVector2,
X,
Y,
sim,
weights);
double meanSimilarity =
computeSimilarity(
scope,
filter,
distance,
vals1,
vals2,
agents,
nbCat,
nb,
crispVector1,
crispVector2,
sim,
fuzzyVector1,
fuzzyVector2,
similarities,
weights);
List<Double> rings = new ArrayList<Double>();
Map<Double, Integer> ringsPn = new TOrderedHashMap<Double, Integer>();
int nbRings = buildRings(scope, filter, distance, rings, ringsPn, agents);
double similarityExpected = computeExpectedSim(nbCat, X, Y, nbRings, rings, ringsPn);
if (similarityExpected == 1) {
return 1;
}
return (meanSimilarity - similarityExpected) / (1 - similarityExpected);
}