@operator(
value = {"clustering_cobweb"},
content_type = IType.LIST,
category = {IOperatorCategory.STATISTICAL},
concept = {IConcept.STATISTIC})
@doc(
value =
"A list of agent groups clusteredby CobWeb Algorithm based on the given attributes. Some paremeters can be defined: "
+ "acuity: minimum standard deviation for numeric attributes; "
+ "cutoff: category utility threshold by which to prune nodes seed",
examples = {
@example(
value =
"clustering_cobweb([ag1, ag2, ag3, ag4, ag5],[\"size\",\"age\", \"weight\"],[\"acuity\"::3.0, \"cutoff\"::0.5)",
equals = "for example, can return [[ag1, ag3], [ag2], [ag4, ag5]]",
isExecutable = false)
},
see = {
"clustering_xmeans",
"clustering_em",
"clustering_farthestFirst",
"clustering_simple_kmeans",
"clustering_cobweb"
})
public static IList<IList<IAgent>> primClusteringCobweb(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<String> attributes,
final GamaMap<String, Object> parameters) {
Cobweb cobweb = new Cobweb();
cobweb.setSeed(Cast.asInt(scope, scope.getRandom().getSeed()));
if (parameters != null) {
try {
if (parameters.containsKey("acuity")) {
cobweb.setAcuity(Cast.asFloat(scope, parameters.get("acuity")));
}
if (parameters.containsKey("cutoff")) {
cobweb.setCutoff(Cast.asFloat(scope, parameters.get("cutoff")));
}
} catch (Exception e) {
}
}
IList<IList<IAgent>> groupes = clusteringUsingWeka(scope, cobweb, attributes, agents);
return groupes;
}
@operator(
value = {"clustering_farthestFirst"},
content_type = IType.LIST,
category = {IOperatorCategory.STATISTICAL},
concept = {IConcept.STATISTIC})
@doc(
value =
"A list of agent groups clustered by Farthest First Algorithm based on the given attributes. Some paremeters can be defined: "
+ "num_clusters: the number of clusters",
examples = {
@example(
value =
"clustering_farthestFirst([ag1, ag2, ag3, ag4, ag5],[\"size\",\"age\", \"weight\"],[\"num_clusters\"::3])",
equals = "for example, can return [[ag1, ag3], [ag2], [ag4, ag5]]",
isExecutable = false)
},
see = {
"clustering_xmeans",
"clustering_simple_kmeans",
"clustering_em",
"clustering_DBScan",
"clustering_cobweb"
})
public static IList<IList<IAgent>> primClusteringFarthestFirst(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<String> attributes,
final GamaMap<String, Object> parameters) {
FarthestFirst ff = new FarthestFirst();
ff.setSeed(Cast.asInt(scope, scope.getRandom().getSeed()));
if (parameters != null) {
try {
if (parameters.containsKey("num_clusters")) {
ff.setNumClusters(Cast.asInt(scope, parameters.get("num_clusters")));
}
} catch (Exception e) {
}
}
IList<IList<IAgent>> groupes = clusteringUsingWeka(scope, ff, attributes, agents);
return groupes;
}
@operator(
value = {"clustering_DBScan"},
content_type = IType.LIST,
category = {IOperatorCategory.STATISTICAL},
concept = {IConcept.STATISTIC})
@doc(
value =
"A list of agent groups clustered by DBScan Algorithm based on the given attributes. Some paremeters can be defined: "
+ "distance_f: The distance function to use for instances comparison (euclidean or manhattan); "
+ "min_points: minimun number of DataObjects required in an epsilon-range-query"
+ "epsilon: epsilon -- radius of the epsilon-range-queries",
examples = {
@example(
value =
"clustering_DBScan([ag1, ag2, ag3, ag4, ag5],[\"size\",\"age\", \"weight\"],[\"distance_f\"::\"manhattan\"])",
equals = "for example, can return [[ag1, ag3], [ag2], [ag4, ag5]]",
isExecutable = false)
},
see = {
"clustering_xmeans",
"clustering_em",
"clustering_farthestFirst",
"clustering_simple_kmeans",
"clustering_cobweb"
})
public static IList<IList<IAgent>> primClusteringDBScan(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<String> attributes,
final GamaMap<String, Object> parameters) {
DBSCAN dbScan = new DBSCAN();
if (parameters != null) {
try {
if (parameters.containsKey("distance_f")) {
String distanceFct = Cast.asString(scope, parameters.get("distance_f"));
if (distanceFct.equals("manhattan")) {
dbScan.setDatabase_distanceType(ManhattanDataObject.class.getName());
} else {
dbScan.setDatabase_distanceType(EuclideanDistance.class.getName());
}
}
if (parameters.containsKey("min_points")) {
dbScan.setMinPoints(Cast.asInt(scope, parameters.get("min_points")));
}
if (parameters.containsKey("epsilon")) {
dbScan.setEpsilon(Cast.asInt(scope, parameters.get("epsilon")));
}
} catch (Exception e) {
}
}
IList<IList<IAgent>> groupes = clusteringUsingWeka(scope, dbScan, attributes, agents);
return groupes;
}
@operator(
value = {"clustering_xmeans"},
content_type = IType.LIST,
category = {IOperatorCategory.STATISTICAL},
concept = {IConcept.STATISTIC})
@doc(
value =
"A list of agent groups clustered by X-Means Algorithm based on the given attributes. Some paremeters can be defined: bin_value: value given for true value of boolean attributes; cut_off_factor: the cut-off factor to use;"
+ "distance_f: The distance function to use. 4 possible distance functions: euclidean (by default) ; 'chebyshev', 'manhattan' or 'levenshtein'; "
+ "max_iterations: the maximum number of iterations to perform; max_kmeans: the maximum number of iterations to perform in KMeans; max_kmeans_for_children: the maximum number of iterations KMeans that is performed on the child centers;"
+ "max_num_clusters: the maximum number of clusters; min_num_clusters: the minimal number of clusters",
examples = {
@example(
value =
"clustering_xmeans([ag1, ag2, ag3, ag4, ag5],[\"size\",\"age\", \"weight\", \"is_male\"],[\"bin_value\"::1.0, \"distance_f\"::\"manhattan\", \"max_num_clusters\"::10, \"min_num_clusters\"::2])",
equals = "for example, can return [[ag1, ag3], [ag2], [ag4, ag5]]",
isExecutable = false)
},
see = {
"clustering_simple_kmeans",
"clustering_em",
"clustering_farthestFirst",
"clustering_DBScan",
"clustering_cobweb"
})
public static IList<IList<IAgent>> primClusteringXMeans(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<String> attributes,
final GamaMap<String, Object> parameters)
throws GamaRuntimeException {
XMeans xmeans = new XMeans();
xmeans.setSeed(Cast.asInt(scope, scope.getRandom().getSeed()));
if (parameters != null) {
if (parameters.containsKey("bin_value")) {
xmeans.setBinValue(Cast.asFloat(scope, parameters.get("bin_value")));
}
if (parameters.containsKey("cut_off_factor")) {
xmeans.setCutOffFactor(Cast.asFloat(scope, parameters.get("cut_off_factor")));
}
if (parameters.containsKey("distance_f")) {
String distanceFct = Cast.asString(scope, parameters.get("distance_f"));
if (distanceFct.equals("chebyshev")) {
xmeans.setDistanceF(new ChebyshevDistance());
} else if (distanceFct.equals("manhattan")) {
xmeans.setDistanceF(new ManhattanDistance());
} else if (distanceFct.equals("levenshtein")) {
xmeans.setDistanceF(new EditDistance());
}
}
if (parameters.containsKey("max_iterations")) {
try {
xmeans.setMaxIterations(Cast.asInt(scope, parameters.get("max_iterations")));
} catch (Exception e) {
}
}
if (parameters.containsKey("max_kmeans")) {
xmeans.setMaxKMeans(Cast.asInt(scope, parameters.get("max_kmeans")));
}
if (parameters.containsKey("max_kmeans_for_children")) {
xmeans.setMaxKMeansForChildren(
Cast.asInt(scope, parameters.get("max_kmeans_for_children")));
}
if (parameters.containsKey("max_num_clusters")) {
xmeans.setMaxNumClusters(Cast.asInt(scope, parameters.get("max_num_clusters")));
}
if (parameters.containsKey("min_num_clusters")) {
xmeans.setMinNumClusters(Cast.asInt(scope, parameters.get("min_num_clusters")));
}
}
IList<IList<IAgent>> groupes = clusteringUsingWeka(scope, xmeans, attributes, agents);
return groupes;
}
@operator(
value = {"clustering_simple_kmeans"},
content_type = IType.LIST,
category = {IOperatorCategory.STATISTICAL},
concept = {IConcept.STATISTIC})
@doc(
value =
"A list of agent groups clustered by K-Means Algorithm based on the given attributes. Some paremeters can be defined: "
+ "distance_f: The distance function to use. 4 possible distance functions: euclidean (by default) ; 'chebyshev', 'manhattan' or 'levenshtein'; "
+ "dont_replace_missing_values: if false, replace missing values globally with mean/mode; max_iterations: the maximum number of iterations to perform;"
+ "num_clusters: the number of clusters",
examples = {
@example(
value =
"clustering_simple_kmeans([ag1, ag2, ag3, ag4, ag5],[\"size\",\"age\", \"weight\"],[\"distance_f\"::\"manhattan\", \"num_clusters\"::3])",
equals = "for example, can return [[ag1, ag3], [ag2], [ag4, ag5]]",
isExecutable = false)
},
see = {
"clustering_xmeans",
"clustering_em",
"clustering_farthestFirst",
"clustering_DBScan",
"clustering_cobweb"
})
public static IList<IList<IAgent>> primClusteringSimpleKMeans(
final IScope scope,
final IAddressableContainer<Integer, IAgent, Integer, IAgent> agents,
final IList<String> attributes,
final GamaMap<String, Object> parameters) {
SimpleKMeans kmeans = new SimpleKMeans();
kmeans.setSeed(Cast.asInt(scope, scope.getRandom().getSeed()));
if (parameters != null) {
try {
if (parameters.containsKey("distance_f")) {
String distanceFct = Cast.asString(scope, parameters.get("distance_f"));
if (distanceFct.equals("chebyshev")) {
kmeans.setDistanceFunction(new ChebyshevDistance());
} else if (distanceFct.equals("manhattan")) {
kmeans.setDistanceFunction(new ManhattanDistance());
} else if (distanceFct.equals("levenshtein")) {
kmeans.setDistanceFunction(new EditDistance());
}
}
if (parameters.containsKey("dont_replace_missing_values")) {
kmeans.setDontReplaceMissingValues(
Cast.asBool(scope, parameters.get("dont_replace_missing_values")));
}
if (parameters.containsKey("max_iterations")) {
kmeans.setMaxIterations(Cast.asInt(scope, parameters.get("max_iterations")));
}
if (parameters.containsKey("num_clusters")) {
kmeans.setNumClusters(Cast.asInt(scope, parameters.get("num_clusters")));
}
} catch (Exception e) {
}
}
IList<IList<IAgent>> groupes = clusteringUsingWeka(scope, kmeans, attributes, agents);
return groupes;
}