public void doRFileEvaluate(final IScope scope, final IContainer param) {
int size = param.length(scope);
// if ( size == 0 ) { throw GamaRuntimeException.error("Missing Parameter Exception", scope); }
final String RFile = getPath();
try {
// Call R
RCaller caller = new RCaller();
String RPath = GamaPreferences.LIB_R.value(scope).getPath();
caller.setRscriptExecutable(RPath);
double[] vectorParam = new double[param.length(scope)];
int k = 0;
for (Object o : param.iterable(scope)) {
vectorParam[k++] = Cast.asFloat(scope, o);
}
RCode c = new RCode();
// Adding the parameters
c.addDoubleArray("vectorParam", vectorParam);
// Adding the codes in file
List<String> R_statements = new ArrayList<String>();
// tmthai.begin----------------------------------------------------------------------------
String fullPath = FileUtils.constructAbsoluteFilePath(scope, RFile, true);
if (DEBUG) {
GuiUtils.debug("Stats.R_compute_param.RScript:" + RPath);
GuiUtils.debug("Stats.R_compute_param.Param:" + vectorParam.toString());
GuiUtils.debug("Stats.R_compute_param.RFile:" + RFile);
GuiUtils.debug("Stats.R_compute_param.fullPath:" + fullPath);
}
// FileReader fr = new FileReader(RFile);
FileReader fr = new FileReader(fullPath);
// tmthai.end----------------------------------------------------------------------------
BufferedReader br = new BufferedReader(fr);
String statement;
while ((statement = br.readLine()) != null) {
c.addRCode(statement);
R_statements.add(statement);
// java.lang.System.out.println(statement);
}
br.close();
fr.close();
caller.setRCode(c);
GamaMap<String, IList> result = GamaMapFactory.create(Types.STRING, Types.LIST);
String var = computeVariable(R_statements.get(R_statements.size() - 1).toString());
caller.runAndReturnResult(var);
// DEBUG:
// java.lang.System.out.println("Name: '" + R_statements.length(scope) + "'");
if (DEBUG) {
GuiUtils.debug("Stats.R_compute_param.R_statements.length: '" + R_statements.size() + "'");
}
for (String name : caller.getParser().getNames()) {
String[] results = null;
results = caller.getParser().getAsStringArray(name);
// java.lang.System.out.println("Name: '" + name + "'");
if (DEBUG) {
GuiUtils.debug(
"Stats.R_compute_param.caller.Name: '"
+ name
+ "' length: "
+ results.length
+ " - Value: "
+ results.toString());
}
result.put(name, GamaListFactory.create(scope, Types.NO_TYPE, results));
}
if (DEBUG) {
GuiUtils.debug("Stats.R_compute_param.return:" + result.serialize(false));
}
setBuffer(result);
} catch (Exception ex) {
throw GamaRuntimeException.error("RCallerExecutionException " + ex.getMessage(), scope);
}
}
@Override
public GamaMap mapValue(
final IScope scope, final IType keyType, final IType contentsType, final boolean copy)
throws GamaRuntimeException {
final IList<IAgent> agents = listValue(scope, contentsType, false);
// Default behavior : Returns a map containing the names of agents as keys and the agents
// themselves as values
final GamaMap result =
GamaMapFactory.create(Types.STRING, scope.getModelContext().getTypeNamed(getName()));
for (final IAgent agent : agents.iterable(scope)) {
result.put(agent.getName(), agent);
}
return result;
}
@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;
}
public void doRFileEvaluate(final IScope scope) {
final String RFile = getPath();
try {
// Call R
RCaller caller = new RCaller();
String RPath = GamaPreferences.LIB_R.value(scope).getPath();
caller.setRscriptExecutable(RPath);
// caller.setRscriptExecutable("\"" + RPath + "\"");
// if(java.lang.System.getProperty("os.name").startsWith("Mac"))
// {
// caller.setRscriptExecutable(RPath);
// }
RCode c = new RCode();
List<String> R_statements = new ArrayList<String>();
// tmthai.begin----------------------------------------------------------------------------
String fullPath = FileUtils.constructAbsoluteFilePath(scope, RFile, true);
if (DEBUG) {
GuiUtils.debug("Stats.R_compute.RScript:" + RPath);
GuiUtils.debug("Stats.R_compute.RFile:" + RFile);
GuiUtils.debug("Stats.R_compute.fullPath:" + fullPath);
}
// FileReader fr = new FileReader(RFile);
FileReader fr = new FileReader(fullPath);
// tmthai.end----------------------------------------------------------------------------
BufferedReader br = new BufferedReader(fr);
String statement;
while ((statement = br.readLine()) != null) {
c.addRCode(statement);
R_statements.add(statement);
// java.lang.System.out.println(statement);
if (DEBUG) {
GuiUtils.debug("Stats.R_compute.statement:" + statement);
}
}
fr.close();
br.close();
caller.setRCode(c);
GamaMap<String, IList> result = GamaMapFactory.create(Types.STRING, Types.LIST);
String var = computeVariable(R_statements.get(R_statements.size() - 1).toString());
caller.runAndReturnResult(var);
for (String name : caller.getParser().getNames()) {
Object[] results = null;
results = caller.getParser().getAsStringArray(name);
// for (int i = 0; i < results.length; i++) {
// java.lang.System.out.println(results[i]);
// }
if (DEBUG) {
GuiUtils.debug(
"Stats.R_compute_param.caller.Name: '"
+ name
+ "' length: "
+ results.length
+ " - Value: "
+ results.toString());
}
result.put(name, GamaListFactory.createWithoutCasting(Types.NO_TYPE, results));
}
if (DEBUG) {
GuiUtils.debug("Stats.R_compute.return:" + result.serialize(false));
}
// return result;
setBuffer(result);
} catch (Exception ex) {
throw GamaRuntimeException.error("RCallerExecutionException " + ex.getMessage(), scope);
}
}
@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;
}
