/** Add a transcription to the retriever */
public void acceptFeatureProfile(Signal featureSignal) throws noMetadataException {
this.Files = null;
FileLocationToFeaturesMap.put(
featureSignal.getStringMetadata(Signal.PROP_FILE_LOCATION), featureSignal.getDataRow(0));
FileLocationToSignalMap.put(
featureSignal.getStringMetadata(Signal.PROP_FILE_LOCATION), featureSignal);
}
/** Retrieve transcriptions most similar to the query */
public SearchResult[] retrieveMostSimilar(Signal likelihoodSignal) throws noMetadataException {
if (this.Files == null) {
this.finaliseIndex();
}
ArrayList results = new ArrayList();
// get features
double[] features =
likelihoodSignal.getData()[likelihoodSignal.getColumnIndex(Signal.PROP_LIKELIHOODS)];
double queryRootSquareSum = 0.0;
for (int j = 0; j < features.length; j++) {
queryRootSquareSum += Math.pow(features[j] / featureStds[j], 2.0);
}
queryRootSquareSum = Math.sqrt(queryRootSquareSum);
// iterate through all files
for (int i = 0; i < Files.length; i++) {
float score = 0.0f;
// get features
double[] testFeatures = ((double[]) this.FileLocationToFeaturesMap.get(Files[i]));
// Normalised Cosine distance between likelihoods
double prodSum = 0.0;
double testSum = 0.0;
for (int j = 0; j < features.length; j++) {
prodSum += (features[j] / featureStds[j]) * (testFeatures[j] / featureStds[j]);
testSum += Math.pow(testFeatures[j] / featureStds[j], 2.0);
}
score = (float) (prodSum / (queryRootSquareSum * Math.sqrt(testSum)));
Signal outSig = (Signal) FileLocationToSignalMap.get((String) Files[i]);
results.add(new SearchResult(outSig, score));
}
SearchResult[] outputResults =
(SearchResult[]) results.toArray(new SearchResult[results.size()]);
java.util.Arrays.sort((Object[]) outputResults);
return outputResults;
}
/**
* Calculates the probability of class membership of a Signal Object.
*
* @param inputSignal The Signal object to calculate the probabilities of class membership for.
* This probabilities should be ordered such that the indexes match the class names returned
* by <code>getClassNames</code>.
* @return An array of the probabilities of class membership
*/
public double[] probabilities(Signal inputSignal) {
Instances theData = Signal2Instance.convert(inputSignal, this.classAttribute);
double[] probsAccum = new double[this.getNumClasses()];
for (int i = 0; i < theData.numInstances(); i++) {
double[] temp;
try {
temp = theRule.distributionForInstance(theData.instance(i));
// if(this.verbose){
// System.out.print("distribution:" );
// for (int j=0;j<this.getNumClasses();j++) {
// System.out.print(" " + temp[j]);
// }
// System.out.println("");
// }
} catch (Exception ex) {
System.out.println(
"Exception occured when calculating distribution for a vector!\n" + ex.getMessage());
ex.printStackTrace();
return null;
}
for (int j = 0; j < this.getNumClasses(); j++) {
probsAccum[j] += Math.log(temp[j]);
}
}
// if(this.verbose){
// System.out.print("Log Probs:" );
// for (int j=0;j<this.getNumClasses();j++) {
// System.out.print(" " + probsAccum[j]);
// }
// System.out.println("");
// }
// normalise to range 0:1
double min = Double.POSITIVE_INFINITY;
double max = Double.NEGATIVE_INFINITY;
for (int j = 0; j < this.getNumClasses(); j++) {
if (probsAccum[j] > max) {
max = probsAccum[j];
}
if (probsAccum[j] < min) {
min = probsAccum[j];
}
}
for (int j = 0; j < this.getNumClasses(); j++) {
probsAccum[j] -= min;
probsAccum[j] /= (max - min);
}
// normalise to sum to 1
double total = 0.0;
for (int j = 0; j < this.getNumClasses(); j++) {
total += probsAccum[j];
}
if (verbose) {
DecimalFormat dec = new DecimalFormat();
dec.setMaximumFractionDigits(3);
if (this.verbose) {
System.out.print("Probabilities:");
}
for (int j = 0; j < this.getNumClasses(); j++) {
probsAccum[j] /= total;
if (verbose) {
System.out.print(" " + dec.format(probsAccum[j]));
}
}
if (verbose) {
try {
System.out.println("\t" + inputSignal.getStringMetadata(Signal.PROP_FILE_LOCATION));
} catch (noMetadataException nme) {
nme.printStackTrace(System.out);
}
}
}
return probsAccum;
}
