import it.unimi.dsi.fastutil.objects.AbstractObjectSet; //导入依赖的package包/类
@Override
public ObjectSet<Int2DoubleMap.Entry> sparseEntrySet() {
    return new AbstractObjectSet<Int2DoubleMap.Entry>() {

        @Override
        public ObjectIterator<Int2DoubleMap.Entry> iterator() {
            return new ObjectIterator<Int2DoubleMap.Entry>() {
                int n = 0;

                @Override
                public int skip(int i) {
                    int nold = n;
                    n = Math.max(0, Math.min(size(), n + i));
                    return n - nold;
                }

                @Override
                public boolean hasNext() {
                    return n < size();
                }

                @Override
                public Int2DoubleMap.Entry next() {
                    return new AbstractInt2DoubleMap.BasicEntry(n, getDouble(n++));
                }

                @Override
                public void remove() {
                }
            };
        }

        @Override
        public int size() {
            return DenseVector.this.size();
        }
    };
}
 

import it.unimi.dsi.fastutil.objects.AbstractObjectSet; //导入依赖的package包/类
public ObjectSet<LongArrayBitVector> keySet() {
	return new AbstractObjectSet<LongArrayBitVector>() {

		@Override
		public ObjectIterator<LongArrayBitVector> iterator() {
			return new ObjectIterator<LongArrayBitVector>() {
				private int i = 0;
				private int pos = -1;

				@Override
				public boolean hasNext() {
					return i < size;
				}

				@Override
				public LongArrayBitVector next() {
					if (! hasNext()) throw new NoSuchElementException();
					while(node[++pos] == null);
					i++;
					return LongArrayBitVector.copy(node[pos].handle(transform));
				}
			};
		}

		@Override
		public boolean contains(Object o) {
			final BitVector v = (BitVector)o;
			return get(v, true) != null;
		}

		@Override
		public int size() {
			return size;
		}

	};
}
 

import it.unimi.dsi.fastutil.objects.AbstractObjectSet; //导入依赖的package包/类
public Instances mapTargetInstance(Instances inp){
	// Creates instances with the same format
	Instances result=getOutputFormat();
	Attribute contentAtt=inp.attribute(this.m_textIndex.getIndex());

	for(Instance inst:inp){
		String content=inst.stringValue(contentAtt);

		// tokenises the content 
		List<String> tokens = affective.core.Utils.tokenize(content, this.toLowerCase, this.standarizeUrlsUsers, this.reduceRepeatedLetters, this.m_tokenizer,this.m_stemmer,this.m_stopwordsHandler);

		// Identifies the distinct terms
		AbstractObjectSet<String> terms=new  ObjectOpenHashSet<String>(); 
		terms.addAll(tokens);


		Object2IntMap<String> docVec=this.calculateDocVec(tokens);

		double[] values = new double[result.numAttributes()];


		values[result.classIndex()]= inst.classValue();

		for(String att:docVec.keySet()){

			if(this.m_Dictionary.containsKey(att)){
				int attIndex=this.m_Dictionary.getInt(att);
				// we normalise the value by the number of documents
				values[attIndex]=docVec.getInt(att);					
			}


		}


		Instance outInst=new SparseInstance(1, values);

		inst.setDataset(result);

		result.add(outInst);

	}

	return result;

}
 

import it.unimi.dsi.fastutil.objects.AbstractObjectSet; //导入依赖的package包/类
public Instances mapTargetInstance(Instances inp){

		// Creates instances with the same format
		Instances result=getOutputFormat();


		Attribute contentAtt=inp.attribute(this.m_textIndex.getIndex());


		for(Instance inst:inp){
			String content=inst.stringValue(contentAtt);



			// tokenises the content 
			List<String> tokens = affective.core.Utils.tokenize(content, this.toLowerCase, this.standarizeUrlsUsers, this.reduceRepeatedLetters, this.m_tokenizer,this.m_stemmer,this.m_stopwordsHandler);

			// Identifies the distinct terms
			AbstractObjectSet<String> terms=new  ObjectOpenHashSet<String>(); 
			terms.addAll(tokens);


			Object2IntMap<String> docVec=this.calculateDocVec(tokens);

			double[] values = new double[result.numAttributes()];


			values[result.classIndex()]= inst.classValue();

			for(String att:docVec.keySet()){

				if(this.m_Dictionary.containsKey(att)){
					int attIndex=this.m_Dictionary.getInt(att);
					// we normalise the value by the number of documents
					values[attIndex]=docVec.getInt(att);					
				}


			}


			Instance outInst=new SparseInstance(1, values);

			inst.setDataset(result);

			result.add(outInst);

		}

		return result;

	}