@Override
public List<Integer> batchRetrieve(
final MlStormWekaState binaryClassifierState, final List<TridentTuple> queryTuples) {
List<Integer> queryResults = new ArrayList<Integer>();
for (TridentTuple queryTuple : queryTuples) {
double[] fv = getFeatureVectorFromArgs(queryTuple);
final Instance instance = MlStormFeatureVectorUtils.buildWekaInstance(fv);
try {
final int classification = (int) binaryClassifierState.predict(instance);
queryResults.add(classification);
} catch (Exception e) {
e.printStackTrace();
}
}
return queryResults;
}
@Override
public List<Map.Entry<Double, double[]>> batchRetrieve(
final MlStormWekaState clustererState, final List<TridentTuple> queryTuples) {
ArrayList<Map.Entry<Double, double[]>> queryResults =
new ArrayList<Map.Entry<Double, double[]>>();
for (TridentTuple query : queryTuples) {
//noinspection unchecked
final Map<Integer, Map.Entry<Integer, double[]>> voteMap =
(Map<Integer, Map.Entry<Integer, double[]>>) query.getValueByField("voteMap");
final double[] fv = new double[numPartitions];
for (Integer key : voteMap.keySet()) {
fv[key] = voteMap.get(key).getKey();
}
try {
Instance testInstance = MlStormFeatureVectorUtils.buildWekaInstance(fv);
double[] distribution = null;
double result = clustererState.predict(testInstance);
queryResults.add(new KeyValuePair<Double, double[]>(result, distribution));
} catch (IOException e) {
e.printStackTrace();
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (Exception e) {
if (e.toString().contains(MlStormWekaState.NOT_READY_TO_PREDICT)) {
System.err.println(
MessageFormat.format(
"Not Ready yet! Continue training with - {0}", Arrays.toString(fv)));
queryResults.add(new KeyValuePair<Double, double[]>(fv[fv.length - 1], null));
} else {
throw new IllegalStateException(e);
}
}
}
return queryResults;
}