public int cluster(float[] tagTestValues, float[] maxNumTermTestValues, int numClusters)
throws SQLException {
List<CDataPoint> allDataPointsList = new ArrayList<CDataPoint>();
List<CDataPoint> oneGramDataPointsList = new ArrayList<CDataPoint>();
List<CDataPoint> nGramDataPointsList = new ArrayList<CDataPoint>();
int numRecords = 0;
NClusteringSets clusteringSets = null;
int dataIndex = 0;
CDbpediaSql query = CDbpediaSql.getInstance();
query.setQuery(new String[] {"label", "lgabstract"}, null);
List<String[]> fieldsList = query.execute(_startIndex, _endIndex - _startIndex);
for (float tagTestValue : tagTestValues) {
for (float maxNumTermTestValue : maxNumTermTestValues) {
CNGramsModel.getInstance().setTagEstimator(tagTestValue);
CNGramsModel.getInstance().setMaxTermFreqEstimator(maxNumTermTestValue);
clusteringSets = new NClusteringSets();
numRecords += executeRun(fieldsList, clusteringSets);
CDataPoint datapoint = null;
dataIndex = allDataPointsList.size();
List<double[]> valuesList = clusteringSets.getAllDatasetsList();
for (double[] values : valuesList) {
datapoint = new CDataPoint(values, dataIndex);
allDataPointsList.add(datapoint);
}
dataIndex = oneGramDataPointsList.size();
valuesList = clusteringSets.getOneGramDatasetsList();
for (double[] values : valuesList) {
datapoint = new CDataPoint(values, dataIndex);
oneGramDataPointsList.add(datapoint);
}
dataIndex = nGramDataPointsList.size();
valuesList = clusteringSets.getNGramDatasetsList();
for (double[] values : valuesList) {
datapoint = new CDataPoint(values, dataIndex++);
nGramDataPointsList.add(datapoint);
}
}
}
/*
* Aggregate the clustering results for all N-Grams, 1-Grams and
* N-Grams with N >1
*/
StringBuilder resultsBuf = new StringBuilder();
CKMeansClustering kmeanClustering = null;
if (allDataPointsList.size() > 0) {
kmeanClustering = new CKMeansClustering(numClusters, 500, allDataPointsList);
kmeanClustering.train();
resultsBuf.append("All NGrams\n");
resultsBuf.append(kmeanClustering.toString());
}
if (oneGramDataPointsList.size() > 0) {
kmeanClustering = new CKMeansClustering(numClusters, 500, oneGramDataPointsList);
kmeanClustering.train();
resultsBuf.append("\n1-Grams only\n");
resultsBuf.append(kmeanClustering.toString());
}
if (nGramDataPointsList.size() > 0) {
kmeanClustering = new CKMeansClustering(numClusters, 500, nGramDataPointsList);
kmeanClustering.train();
resultsBuf.append("\nN-Grams only\n");
resultsBuf.append(kmeanClustering.toString());
}
/*
* Save the clustering results ...
*/
try {
CTfIdfScore.ITfDiscriminant discriminant = CTfIdfScore.getInstance().getTfDiscrimant();
final String fileName =
CLUSTER_FILES + "_" + discriminant.getName() + "_" + String.valueOf(numClusters);
CFileUtil.write(fileName, resultsBuf.toString());
} catch (IOException e) {
CLogger.error(e.toString());
}
return numRecords;
}
/**
* Execute the validation of the N-Gram frequency model against the Wikipedia reference database.
*
* @param startIndex index of the first record in the Wikipedia reference database
* @param endIndex index of the last record in the Wikipedia reference database
* @return Number of training sets created
* @throws SQLException if Wikipedia database is unavailable
*/
public int validate() throws SQLException {
CDbpediaSql query = CDbpediaSql.getInstance();
List<String[]> fieldsList = query.execute(_startIndex, _endIndex - _startIndex);
return executeRun(fieldsList, new NValidationSets());
}