use of edu.cmu.tetrad.search.Lofs2 in project tetrad by cmu-phil.
the class Skew method search.
@Override
public Graph search(DataModel dataSet, Parameters parameters) {
if (parameters.getInt("bootstrapSampleSize") < 1) {
Graph graph = algorithm.search(dataSet, parameters);
if (graph != null) {
initialGraph = graph;
} else {
throw new IllegalArgumentException("This Skew algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
}
List<DataSet> dataSets = new ArrayList<>();
dataSets.add(DataUtils.getContinuousDataSet(dataSet));
Lofs2 lofs = new Lofs2(initialGraph, dataSets);
lofs.setRule(Lofs2.Rule.Skew);
return lofs.orient();
} else {
Skew skew = new Skew(algorithm);
if (initialGraph != null) {
skew.setInitialGraph(initialGraph);
}
DataSet data = (DataSet) dataSet;
GeneralBootstrapTest search = new GeneralBootstrapTest(data, skew, parameters.getInt("bootstrapSampleSize"));
BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
switch(parameters.getInt("bootstrapEnsemble", 1)) {
case 0:
edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
break;
case 1:
edgeEnsemble = BootstrapEdgeEnsemble.Highest;
break;
case 2:
edgeEnsemble = BootstrapEdgeEnsemble.Majority;
}
search.setEdgeEnsemble(edgeEnsemble);
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean("verbose"));
return search.search();
}
}
use of edu.cmu.tetrad.search.Lofs2 in project tetrad by cmu-phil.
the class Tanh method search.
@Override
public Graph search(DataModel dataSet, Parameters parameters) {
if (parameters.getInt("bootstrapSampleSize") < 1) {
Graph graph = algorithm.search(dataSet, parameters);
if (graph != null) {
initialGraph = graph;
} else {
throw new IllegalArgumentException("This Tanh algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
}
List<DataSet> dataSets = new ArrayList<>();
dataSets.add(DataUtils.getContinuousDataSet(dataSet));
Lofs2 lofs = new Lofs2(initialGraph, dataSets);
lofs.setRule(Lofs2.Rule.Tanh);
return lofs.orient();
} else {
Tanh tanh = new Tanh(algorithm);
if (initialGraph != null) {
tanh.setInitialGraph(initialGraph);
}
DataSet data = (DataSet) dataSet;
GeneralBootstrapTest search = new GeneralBootstrapTest(data, tanh, parameters.getInt("bootstrapSampleSize"));
BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
switch(parameters.getInt("bootstrapEnsemble", 1)) {
case 0:
edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
break;
case 1:
edgeEnsemble = BootstrapEdgeEnsemble.Highest;
break;
case 2:
edgeEnsemble = BootstrapEdgeEnsemble.Majority;
}
search.setEdgeEnsemble(edgeEnsemble);
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean("verbose"));
return search.search();
}
}
use of edu.cmu.tetrad.search.Lofs2 in project tetrad by cmu-phil.
the class R2 method search.
@Override
public Graph search(DataModel dataSet, Parameters parameters) {
if (parameters.getInt("bootstrapSampleSize") < 1) {
Graph graph = algorithm.search(dataSet, parameters);
if (graph != null) {
initialGraph = graph;
} else {
throw new IllegalArgumentException("This R2 algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
}
List<DataSet> dataSets = new ArrayList<>();
dataSets.add(DataUtils.getContinuousDataSet(dataSet));
Lofs2 lofs = new Lofs2(initialGraph, dataSets);
lofs.setRule(Lofs2.Rule.R2);
return lofs.orient();
} else {
R2 r2 = new R2(algorithm);
if (initialGraph != null) {
r2.setInitialGraph(initialGraph);
}
DataSet data = (DataSet) dataSet;
GeneralBootstrapTest search = new GeneralBootstrapTest(data, r2, parameters.getInt("bootstrapSampleSize"));
BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
switch(parameters.getInt("bootstrapEnsemble", 1)) {
case 0:
edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
break;
case 1:
edgeEnsemble = BootstrapEdgeEnsemble.Highest;
break;
case 2:
edgeEnsemble = BootstrapEdgeEnsemble.Majority;
}
search.setEdgeEnsemble(edgeEnsemble);
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean("verbose"));
return search.search();
}
}
use of edu.cmu.tetrad.search.Lofs2 in project tetrad by cmu-phil.
the class SkewE method search.
@Override
public Graph search(DataModel dataSet, Parameters parameters) {
if (parameters.getInt("bootstrapSampleSize") < 1) {
Graph graph = algorithm.search(dataSet, parameters);
if (graph != null) {
initialGraph = graph;
} else {
throw new IllegalArgumentException("This SkewE algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
}
List<DataSet> dataSets = new ArrayList<>();
dataSets.add(DataUtils.getContinuousDataSet(dataSet));
Lofs2 lofs = new Lofs2(initialGraph, dataSets);
lofs.setRule(Lofs2.Rule.SkewE);
return lofs.orient();
} else {
SkewE skewE = new SkewE(algorithm);
if (initialGraph != null) {
skewE.setInitialGraph(initialGraph);
}
DataSet data = (DataSet) dataSet;
GeneralBootstrapTest search = new GeneralBootstrapTest(data, skewE, parameters.getInt("bootstrapSampleSize"));
BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
switch(parameters.getInt("bootstrapEnsemble", 1)) {
case 0:
edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
break;
case 1:
edgeEnsemble = BootstrapEdgeEnsemble.Highest;
break;
case 2:
edgeEnsemble = BootstrapEdgeEnsemble.Majority;
}
search.setEdgeEnsemble(edgeEnsemble);
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean("verbose"));
return search.search();
}
}
use of edu.cmu.tetrad.search.Lofs2 in project tetrad by cmu-phil.
the class R3 method search.
@Override
public Graph search(DataModel dataSet, Parameters parameters) {
if (parameters.getInt("bootstrapSampleSize") < 1) {
Graph graph = algorithm.search(dataSet, parameters);
if (graph != null) {
initialGraph = graph;
} else {
throw new IllegalArgumentException("This R3 algorithm needs both data and a graph source as inputs; it \n" + "will orient the edges in the input graph using the data");
}
List<DataSet> dataSets = new ArrayList<>();
dataSets.add(DataUtils.getContinuousDataSet(dataSet));
Lofs2 lofs = new Lofs2(initialGraph, dataSets);
lofs.setRule(Lofs2.Rule.R3);
return lofs.orient();
} else {
R3 r3 = new R3(algorithm);
if (initialGraph != null) {
r3.setInitialGraph(initialGraph);
}
DataSet data = (DataSet) dataSet;
GeneralBootstrapTest search = new GeneralBootstrapTest(data, r3, parameters.getInt("bootstrapSampleSize"));
BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
switch(parameters.getInt("bootstrapEnsemble", 1)) {
case 0:
edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
break;
case 1:
edgeEnsemble = BootstrapEdgeEnsemble.Highest;
break;
case 2:
edgeEnsemble = BootstrapEdgeEnsemble.Majority;
}
search.setEdgeEnsemble(edgeEnsemble);
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean("verbose"));
return search.search();
}
}
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