private static INDArray getData() {
Random r = new Random(1);
int[] result = new int[window];
for (int i = 0; i < window; i++) {
result[i] = r.nextInt(nIn);
}
return FeatureUtil.toOutcomeMatrix(result, nIn);
}
@Override
public DataSet call(Collection<Writable> writables) throws Exception {
List<Writable> list;
if (writables instanceof List) list = (List<Writable>) writables;
else list = new ArrayList<>(writables);
// allow people to specify label index as -1 and infer the last possible label
int labelIndex = this.labelIndex;
if (numPossibleLabels >= 1 && labelIndex < 0) {
labelIndex = list.size() - 1;
}
INDArray label = null;
INDArray featureVector = Nd4j.create(labelIndex >= 0 ? list.size() - 1 : list.size());
int featureCount = 0;
for (int j = 0; j < list.size(); j++) {
Writable current = list.get(j);
if (converter != null) current = converter.convert(current);
if (labelIndex >= 0 && j == labelIndex) {
// Current value is the label
if (converter != null) {
try {
current = converter.convert(current);
} catch (WritableConverterException e) {
e.printStackTrace();
}
}
if (numPossibleLabels < 1)
throw new IllegalStateException("Number of possible labels invalid, must be >= 1");
if (regression) {
label = Nd4j.scalar(current.toDouble());
} else {
// Convert to one-hot vector for
int curr = current.toInt();
if (curr >= numPossibleLabels)
throw new IllegalStateException(
"Invalid input: class label is "
+ curr
+ " with numPossibleLables = "
+ numPossibleLabels
+ " (class label must be 0 <= labelIdx < numPossibleLabels)");
label = FeatureUtil.toOutcomeVector(curr, numPossibleLabels);
}
} else {
// Current value is not the label
featureVector.putScalar(featureCount++, current.toDouble());
}
}
DataSet ds = new DataSet(featureVector, (labelIndex >= 0 ? label : featureVector));
if (preProcessor != null) preProcessor.preProcess(ds);
return ds;
}
/**
* Vectorizes the passed in text treating it as one document
*
* @param text the text to vectorize
* @param label the label of the text
* @return a dataset with a transform of weights(relative to impl; could be word counts or tfidf
* scores)
*/
@Override
public DataSet vectorize(String text, String label) {
Tokenizer tokenizer = tokenizerFactory.create(text);
List<String> tokens = tokenizer.getTokens();
INDArray input = Nd4j.create(1, cache.numWords());
for (String token : tokens) {
int idx = cache.indexOf(token);
if (cache.indexOf(token) >= 0) input.putScalar(idx, cache.wordFrequency(token));
}
INDArray labelMatrix = FeatureUtil.toOutcomeVector(labels.indexOf(label), labels.size());
return new DataSet(input, labelMatrix);
}
private DataSet getDataSet(Collection<Writable> record) {
List<Writable> currList;
if (record instanceof List) currList = (List<Writable>) record;
else currList = new ArrayList<>(record);
// allow people to specify label index as -1 and infer the last possible label
if (numPossibleLabels >= 1 && labelIndex < 0) {
labelIndex = record.size() - 1;
}
INDArray label = null;
INDArray featureVector = Nd4j.create(labelIndex >= 0 ? currList.size() - 1 : currList.size());
for (int j = 0; j < currList.size(); j++) {
if (labelIndex >= 0 && j == labelIndex) {
if (numPossibleLabels < 1)
throw new IllegalStateException("Number of possible labels invalid, must be >= 1");
Writable current = currList.get(j);
if (current.toString().isEmpty()) continue;
if (converter != null)
try {
current = converter.convert(current);
} catch (WritableConverterException e) {
e.printStackTrace();
}
if (regression) {
label = Nd4j.scalar(Double.valueOf(current.toString()));
} else {
int curr = Double.valueOf(current.toString()).intValue();
if (curr >= numPossibleLabels) curr--;
label = FeatureUtil.toOutcomeVector(curr, numPossibleLabels);
}
} else {
Writable current = currList.get(j);
if (current.toString().isEmpty()) continue;
featureVector.putScalar(j, Double.valueOf(current.toString()));
}
}
return new DataSet(featureVector, labelIndex >= 0 ? label : featureVector);
}
/**
* Fit the model
*
* @param examples the examples to classify (one example in each row)
* @param labels the labels for each example (the number of labels must match
*/
@Override
public void fit(INDArray examples, int[] labels) {
INDArray outcomeMatrix = FeatureUtil.toOutcomeMatrix(labels, numLabels());
fit(examples, outcomeMatrix);
}