@Override public DataSet next(int num) { DataSet ret = sampleFrom.sample(num); numTimesSampled++; return ret; }
/** * Returns a single dataset (all fields are null) * * @return an empty dataset (all fields are null) */ public static DataSet empty() { return new DataSet(null, null); }
/** * Get the feature matrix (inputs for the data) * * @return the feature matrix for the dataset */ @Override public INDArray getFeatureMatrix() { return getFeatures(); }
@Override public int totalExamples() { return data.numExamples(); }
@Override public DataSet next() { int last = Math.min(numExamples(), cursor() + batch()); DataSet next = (DataSet) data.getRange(cursor, last); if (preProcessor != null) preProcessor.preProcess(next); cursor += batch(); return next; } }
@Override public List<DataSet> batchByNumLabels() { return batchBy(numOutcomes()); }
@Override public int totalOutcomes() { return data.numOutcomes(); }
@Override public int inputColumns() { return sampleFrom.numInputs(); }
/** * Shuffles the dataset and resets to the first fold * * @return void */ @Override public void reset() { //shuffle and return new k folds singleFold.shuffle(); kCursor = 0; }
public TestDataSetIterator(DataSet dataset, int batch) { this(dataset.asList(), batch); }
/** * Same as calling binarize(0) */ @Override public void binarize() { binarize(0); }
@Override public boolean hasNext() { return cursor < numExamples(); }
@Override public boolean isEmpty() { return nullOrEmpty(features) && nullOrEmpty(labels) && nullOrEmpty(featuresMaskArrays) && nullOrEmpty(labelsMaskArrays); }
@Override public List<String> getLabels() { return singleFold.getLabelNamesList(); }
@Override public boolean equals(Object o) { if (this == o) return true; if (!(o instanceof DataSet)) return false; DataSet d = (DataSet) o; if (!equalOrBothNull(features, d.features)) return false; if (!equalOrBothNull(labels, d.labels)) return false; if (!equalOrBothNull(featuresMask, d.featuresMask)) return false; return equalOrBothNull(labelsMask, d.labelsMask); }
@Override public int numExamples() { return sampleFrom.numExamples(); }
@Override public int totalOutcomes() { return sampleFrom.numOutcomes(); }
@Override public int inputColumns() { return data.numInputs(); }
/** * Sample without replacement * * @param numSamples the number of samples to getFromOrigin * @param rng the rng to use * @return the sampled dataset without replacement */ @Override public DataSet sample(int numSamples, org.nd4j.linalg.api.rng.Random rng) { return sample(numSamples, rng, false); }
@Override public int numExamples() { return data.numExamples(); }