Пример #1
0
  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;
  }
Пример #2
0
  private static double[] computeXaXs(
      final IScope scope,
      final IAgentFilter filter,
      final Map<Object, Integer> categoriesId,
      final Map<IAgent, Integer> agsId,
      final int valObsId,
      final int valSimId,
      final int valInitId,
      final GamaMatrix<Double> fuzzytransitions,
      final Double distance,
      final IAgent agent,
      final IList<Object> valsInit,
      final IList<Object> valsObs,
      final IList<Object> valsSim,
      final IContainer.Addressable<Integer, IAgent> agents,
      final int nbCat) {
    double xa = 0.0;
    double xs = 0.0;
    double[] XaXs = new double[2];
    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 (IAgent ag : distancesCoeff.keySet()) {
      int id = agsId.get(ag);
      Object valI = valsInit.get(id);
      Object valO = valsObs.get(id);
      Object valS = valsSim.get(id);
      int valOId = categoriesId.get(valO);
      int valSId = categoriesId.get(valS);
      int valIId = categoriesId.get(valI);
      double dist = distancesCoeff.get(ag);
      double valxatmp =
          fuzzyTransition(scope, fuzzytransitions, nbCat, valInitId, valSimId, valIId, valOId)
              * dist;
      double valxstmp =
          fuzzyTransition(scope, fuzzytransitions, nbCat, valInitId, valObsId, valIId, valSId)
              * dist;

      if (valxatmp > xa) {
        xa = valxatmp;
      }
      if (valxstmp > xs) {
        xs = valxstmp;
      }
    }

    XaXs[0] = xa;
    XaXs[1] = xs;
    return XaXs;
  }
Пример #3
0
 private static double p(
     final double dist,
     final int a,
     final int b,
     final double[] X,
     final double[] Y,
     final Map<Double, Integer> ringsPn) {
   int n = 0;
   if (dist > 0.0) {
     n = ringsPn.get(dist);
   }
   return (1 - FastMath.pow(1 - X[a], n)) * (1 - FastMath.pow(1 - Y[b], n));
 }
Пример #4
0
 private static void computeXYCrispVector(
     final IScope scope,
     final Map<Object, Integer> categoriesId,
     final List<Object> categories,
     final IList<Object> vals1,
     final IList<Object> vals2,
     final GamaMatrix<Double> fuzzycategories,
     final int nbCat,
     final int nb,
     final double[][] crispVector1,
     final double[][] crispVector2,
     final double[] X,
     final double[] Y,
     final boolean[] sim,
     final IList<Object> weights) {
   for (int j = 0; j < nbCat; j++) {
     X[j] = 0;
     Y[j] = 0;
   }
   double total = 0;
   for (int i = 0; i < nb; i++) {
     double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
     total += weight;
     Object val1 = vals1.get(i);
     Object val2 = vals2.get(i);
     int indexVal1 = categoriesId.get(val1);
     int indexVal2 = categoriesId.get(val2);
     X[indexVal1] += weight;
     Y[indexVal2] += weight;
     for (int j = 0; j < nbCat; j++) {
       crispVector1[i][j] = Cast.asFloat(scope, fuzzycategories.get(scope, indexVal1, j));
       crispVector2[i][j] = Cast.asFloat(scope, fuzzycategories.get(scope, indexVal2, j));
     }
     if (val1.equals(val2)) {
       sim[i] = true;
     } else {
       sim[i] = false;
     }
   }
   for (int j = 0; j < nbCat; j++) {
     X[j] /= total;
     Y[j] /= total;
   }
 }
Пример #5
0
  private static int buildRings(
      final IScope scope,
      final IAgentFilter filter,
      final Double distance,
      final List<Double> rings,
      final Map<Double, Integer> ringsPn,
      final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents) {

    IList<ILocation> locs = GamaListFactory.create(Types.POINT);
    for (IAgent ag : agents.iterable(scope)) {
      locs.add(ag.getLocation());
    }
    ILocation centralLoc = (ILocation) Stats.getMean(scope, locs);
    IAgent centralAg = scope.getTopology().getAgentClosestTo(scope, centralLoc, filter);
    List<IAgent> neighbors =
        distance == 0 || filter == null
            ? new ArrayList<IAgent>()
            : new ArrayList<IAgent>(
                scope.getTopology().getNeighborsOf(scope, centralAg, distance, filter));

    for (IAgent ag : neighbors) {
      double dist = centralLoc.euclidianDistanceTo(ag.getLocation());
      if (dist == 0) {
        continue;
      }
      if (!rings.contains(dist)) {
        rings.add(dist);
        ringsPn.put(dist, 1);
      } else {
        ringsPn.put(dist, 1 + ringsPn.get(dist));
      }
    }
    Collections.sort(rings);

    for (int i = 1; i < rings.size(); i++) {
      double dist = rings.get(i);
      double dist1 = rings.get(i - 1);
      ringsPn.put(dist, ringsPn.get(dist) + ringsPn.get(dist1));
    }

    return rings.size();
  }
Пример #6
0
  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;
  }
Пример #7
0
  private static void computeXaXsTransitions(
      final IScope scope,
      final IAgentFilter filter,
      final GamaMatrix<Double> fuzzytransitions,
      final Double distance,
      final IContainer<Integer, IAgent> agents,
      final int nbCat,
      final Map<List<Integer>, Map<Double, Double>> XaPerTransition,
      final Map<List<Integer>, Map<Double, Double>> XsPerTransition,
      final Set<Double> Xvals) {

    IList<ILocation> locs = GamaListFactory.create(Types.POINT);
    for (IAgent ag : agents.iterable(scope)) {
      locs.add(ag.getLocation());
    }
    ILocation centralLoc = (ILocation) Stats.getMean(scope, locs);
    if (filter != null) {
      IAgent centralAg = scope.getTopology().getAgentClosestTo(scope, centralLoc, filter);
      List<IAgent> neighbors =
          distance == 0
              ? new ArrayList<IAgent>()
              : new ArrayList<IAgent>(
                  scope.getTopology().getNeighborsOf(scope, centralAg, distance, filter));
      double sizeNorm = FastMath.sqrt(centralAg.getEnvelope().getArea());

      Map<IAgent, Double> distancesCoeff = new TOrderedHashMap<IAgent, Double>();
      distancesCoeff.put(centralAg, 1.0);
      for (IAgent ag : neighbors) {
        double euclidDist = centralAg.getLocation().euclidianDistanceTo(ag.getLocation());
        double dist = 1 / (1.0 + euclidDist / sizeNorm);
        distancesCoeff.put(ag, dist);
      }

      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();
            ca.add(i);
            ca.add(j);
            ca.add(k);
            double xa = 0;
            double xs = 0;
            for (IAgent ag : distancesCoeff.keySet()) {
              double dist = distancesCoeff.get(ag);
              double xatmp = fuzzyTransition(scope, fuzzytransitions, nbCat, i, k, i, j) * dist;
              double xstmp = fuzzyTransition(scope, fuzzytransitions, nbCat, i, j, i, k) * dist;
              if (xatmp > xa) {
                xa = xatmp;
              }
              if (xstmp > xs) {
                xs = xstmp;
              }
            }
            if (xa > 0) {

              Map<Double, Double> mapxa = XaPerTransition.get(ca);
              if (mapxa == null) {
                mapxa = new TOrderedHashMap<Double, Double>();
                mapxa.put(xa, 1.0);
                XaPerTransition.put(ca, mapxa);
              } else {
                if (mapxa.containsKey(xa)) {
                  mapxa.put(xa, mapxa.get(xa) + 1.0);
                } else {
                  mapxa.put(xa, 1.0);
                }
              }
              Xvals.add(xa);
            }
            if (xs > 0) {
              Map<Double, Double> mapxs = XsPerTransition.get(ca);
              if (mapxs == null) {
                mapxs = new TOrderedHashMap<Double, Double>();
                mapxs.put(xs, 1.0);
                XsPerTransition.put(ca, mapxs);
              } else {
                if (mapxs.containsKey(xa)) {
                  mapxs.put(xs, mapxs.get(xs) + 1.0);
                } else {
                  mapxs.put(xs, 1.0);
                }
              }
              Xvals.add(xs);
            }
          }
        }
      }
    }
  }
Пример #8
0
  @operator(
      value = {"kappa"},
      content_type = IType.FLOAT,
      category = {IOperatorCategory.MAP_COMPARAISON},
      concept = {})
  @doc(
      value =
          "kappa indicator for 2 map comparisons: kappa(list_vals1,list_vals2,categories, weights). Reference: Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20. ",
      examples = {
        @example(
            value =
                "kappa([cat1,cat1,cat2,cat3,cat2],[cat2,cat1,cat2,cat1,cat2],[cat1,cat2,cat3], [1.0, 2.0, 3.0, 1.0, 5.0])",
            isExecutable = false)
      })
  public static double kappa(
      final IScope scope,
      final IList<Object> vals1,
      final IList<Object> vals2,
      final List<Object> categories,
      final IList<Object> weights) {
    if (vals1 == null || vals2 == null) {
      return 1;
    }
    int nb = vals1.size();
    if (nb != vals2.size()) {
      return 0;
    }
    int nbCat = categories.size();
    double[] X = new double[nbCat];
    double[] Y = new double[nbCat];
    double[][] contigency = new double[nbCat][nbCat];
    for (int j = 0; j < nbCat; j++) {
      X[j] = 0;
      Y[j] = 0;
      for (int k = 0; k < nbCat; k++) {
        contigency[j][k] = 0;
      }
    }

    Map<Object, Integer> categoriesId = new TOrderedHashMap<Object, Integer>();
    for (int i = 0; i < nbCat; i++) {
      categoriesId.put(categories.get(i), i);
    }
    double total = 0;
    for (int i = 0; i < nb; i++) {
      double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
      total += weight;
      Object val1 = vals1.get(i);
      Object val2 = vals2.get(i);
      int indexVal1 = categoriesId.get(val1);
      int indexVal2 = categoriesId.get(val2);
      X[indexVal1] += weight;
      Y[indexVal2] += weight;
      contigency[indexVal1][indexVal2] += weight;
    }
    for (int j = 0; j < nbCat; j++) {
      X[j] /= total;
      Y[j] /= total;
      for (int k = 0; k < nbCat; k++) {
        contigency[j][k] /= total;
      }
    }
    double po = 0;
    double pe = 0;
    for (int i = 0; i < nbCat; i++) {
      po += contigency[i][i];
      pe += X[i] * Y[i];
    }
    if (pe == 1) {
      return 1;
    }
    return (po - pe) / (1 - pe);
  }
Пример #9
0
  @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);
  }
Пример #10
0
  @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);
  }
Пример #11
0
 @operator(
     value = {"kappa_sim"},
     content_type = IType.FLOAT,
     category = {IOperatorCategory.MAP_COMPARAISON},
     concept = {})
 @doc(
     value =
         "kappa simulation indicator for 2 map comparisons: kappa(list_valsInits,list_valsObs,list_valsSim, categories, weights). Reference: van Vliet, J., Bregt, A.K. & Hagen-Zanker, A. (2011). Revisiting Kappa to account for change in the accuracy assessment of land-use change models, Ecological Modelling 222(8)",
     examples = {
       @example(
           value =
               "kappa([cat1,cat1,cat2,cat2,cat2],[cat2,cat1,cat2,cat1,cat3],[cat2,cat1,cat2,cat3,cat3], [cat1,cat2,cat3],[1.0, 2.0, 3.0, 1.0, 5.0])",
           isExecutable = false)
     })
 public static double kappaSimulation(
     final IScope scope,
     final IList<Object> valsInit,
     final IList<Object> valsObs,
     final IList<Object> valsSim,
     final List<Object> categories,
     final IList<Object> weights) {
   if (valsInit == null || valsObs == null || valsSim == null) {
     return 1;
   }
   int nb = valsInit.size();
   if (nb != valsObs.size() || nb != valsSim.size()) {
     return 0;
   }
   int nbCat = categories.size();
   double[] O = new double[nbCat];
   double[][] contigency = new double[nbCat][nbCat];
   double[][] contigencyOA = new double[nbCat][nbCat];
   double[][] contigencyOS = new double[nbCat][nbCat];
   for (int j = 0; j < nbCat; j++) {
     O[j] = 0;
     for (int k = 0; k < nbCat; k++) {
       contigency[j][k] = 0;
       contigencyOA[j][k] = 0;
       contigencyOS[j][k] = 0;
     }
   }
   Map<Object, Integer> categoriesId = new TOrderedHashMap<Object, Integer>();
   for (int i = 0; i < nbCat; i++) {
     categoriesId.put(categories.get(i), i);
   }
   double total = 0;
   for (int i = 0; i < nb; i++) {
     double weight = weights == null ? 1.0 : Cast.asFloat(scope, weights.get(i));
     total += weight;
     Object val1 = valsObs.get(i);
     Object val2 = valsSim.get(i);
     Object valO = valsInit.get(i);
     int indexVal1 = categoriesId.get(val1);
     int indexVal2 = categoriesId.get(val2);
     int indexValO = categoriesId.get(valO);
     O[indexValO] += weight;
     contigency[indexVal1][indexVal2] += weight;
     contigencyOA[indexValO][indexVal1] += weight;
     contigencyOS[indexValO][indexVal2] += weight;
   }
   for (int j = 0; j < nbCat; j++) {
     for (int k = 0; k < nbCat; k++) {
       contigency[j][k] /= total;
       if (O[j] > 0) {
         contigencyOA[j][k] /= O[j];
         contigencyOS[j][k] /= O[j];
       }
     }
     O[j] /= total;
   }
   double po = 0;
   double pe = 0;
   for (int j = 0; j < nbCat; j++) {
     po += contigency[j][j];
     double sum = 0;
     for (int i = 0; i < nbCat; i++) {
       sum += contigencyOA[j][i] * contigencyOS[j][i];
     }
     pe += O[j] * sum;
   }
   if (pe == 1) {
     return 1;
   }
   return (po - pe) / (1 - pe);
 }