protected void internalRemove(final int _i) { list_.remove(_i); }
/** * Removes and returns the value at the top of the stack. * * @return an <code>int</code> value */ public int pop() { return _list.remove(_list.size() - 1); }
/** * Removes the value at <tt>offset</tt> from the list. * * @param offset an <code>int</code> value * @return the value previously stored at offset. */ public int remove(int offset) { int old = get(offset); remove(offset, 1); return old; }
/** * @overrride */ public void undo() { list_.remove(idxBefore_, newValue_.length); fireDataRemoved(); }
private void sampleNextInstanceToCluster (Clustering clustering) { if (unclusteredInstances == null) fillUnclusteredInstances(clustering.getNumInstances()); instanceBeingClustered = (unclusteredInstances.size() == 0) ? -1 : unclusteredInstances.remove(0); }
private void sampleNextInstanceToCluster (Clustering clustering) { if (unclusteredInstances == null) fillUnclusteredInstances(clustering.getNumInstances()); instanceBeingClustered = (unclusteredInstances.size() == 0) ? -1 : unclusteredInstances.remove(0); }
private void sampleNextInstanceToCluster (Clustering clustering) { if (unclusteredInstances == null) fillUnclusteredInstances(clustering.getNumInstances()); instanceBeingClustered = (unclusteredInstances.size() == 0) ? -1 : unclusteredInstances.remove(0); }
public void setDocumentFeatureFrequency(int document, int feature, int frequency) { if (document >= 0) { int size = _contentDB._documentsFeatures.size(); if (document >= size) { for (int i = size; i <= document; ++i) { _contentDB._documentsFeatures.add(new TIntArrayList()); _contentDB._documentsFrequencies.add(new TIntArrayList()); } } if (feature >= 0) { TIntArrayList feats = _contentDB._documentsFeatures.get(document); TIntArrayList freqs = _contentDB._documentsFrequencies.get(document); int pos = feats.binarySearch(feature); if (pos < 0 && frequency > 0) { pos = -pos - 1; feats.insert(pos, feature); freqs.insert(pos, frequency); } else { if (frequency > 0) { freqs.setQuick(pos, frequency); } else { feats.remove(pos); freqs.remove(pos); } } } } }
public void setDocumentFeatureFrequency(int document, int feature, int frequency) { if (feature >= 0) { int size = _contentDB._featuresDocuments.size(); if (feature >= size) { for (int i = size; i <= feature; ++i) { _contentDB._featuresDocuments.add(new TIntArrayList()); _contentDB._featuresFrequencies.add(new TIntArrayList()); } } if (document >= 0) { TIntArrayList docs = _contentDB._featuresDocuments.get(feature); TIntArrayList freqs = _contentDB._featuresFrequencies .get(feature); int pos = docs.binarySearch(document); if (pos < 0 && frequency > 0) { pos = -pos - 1; docs.insert(pos, document); freqs.insert(pos, frequency); } else { if (frequency > 0) { freqs.setQuick(pos, frequency); } else { docs.remove(pos); freqs.remove(pos); } } } } }
public boolean remove(final int _i, final CtuluCommandContainer _c) { if ((_i >= 0) && (_i < list_.size())) { final int xRemoved = list_.remove(_i); if (_c != null) { _c.addCmd(new CommandRemove(_i, xRemoved)); } fireDataRemoved(); return true; } return false; }
clustering.getNumClusters()); updateScoreMatrix(clustering, clusterIndex, clusterToMerge); unclusteredInstances.remove(unclusteredInstances.indexOf(instanceToMerge)); clustering = ClusterUtils.mergeClusters(clustering, clusterIndex, clusterToMerge);
clustering.getNumClusters()); updateScoreMatrix(clustering, clusterIndex, clusterToMerge); unclusteredInstances.remove(unclusteredInstances.indexOf(instanceToMerge)); clustering = ClusterUtils.mergeClusters(clustering, clusterIndex, clusterToMerge);
clustering.getNumClusters()); updateScoreMatrix(clustering, clusterIndex, clusterToMerge); unclusteredInstances.remove(unclusteredInstances.indexOf(instanceToMerge)); clustering = ClusterUtils.mergeClusters(clustering, clusterIndex, clusterToMerge);
docs.remove(j); docsPrimary.remove(j); if (j < docs.size() && removedDocuments.hasNext()) {
docs.remove(j); if (j < docs.size() && removedDocuments.hasNext()) { doc = docs.getQuick(j);
centroids[whichCluster].documents.remove(idxToRemove); centroids[whichCluster].distances.remove(idxToRemove); visitedClusters[whichCluster] = true;
feats.remove(j); if (j < feats.size() && removedFeatures.hasNext()) { feat = feats.getQuick(j);