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Fast double-precision vector and matrix maths library for Java, supporting N-dimensional numeric arrays.
There is a newer version: 0.67.0
package mikera.vectorz.impl;

import java.util.Arrays;
import mikera.indexz.Index;
import mikera.matrixx.AMatrix;
import mikera.vectorz.AVector;
import mikera.vectorz.util.DoubleArrays;
import mikera.vectorz.util.IntArrays;
import mikera.vectorz.util.VectorzException;


/**
 * Base class containing common implementations for sparse indexed vectors
 * @author Mike
 *
 */
public abstract class ASparseIndexedVector extends ASparseVector {
	private static final long serialVersionUID = -8106136233328863653L;
	
	public ASparseIndexedVector(int length) {
		super(length);
	}
	
	abstract double[] internalData();
	
	abstract Index internalIndex();
	
	int[] internalIndexArray() {
		return internalIndex().data;
	}
	
	@Override
	public boolean includesIndex(int i) {
		return internalIndex().indexPosition(i)>=0;
	}
	
	@Override
	public boolean isZero() {
		return DoubleArrays.isZero(internalData());
	}
	
	@Override
	public boolean isRangeZero(int start, int length) {
		int end=start+length;
		Index index=internalIndex();
		double[] data=internalData();
		int si=index.seekPosition(start);
		int di=index.seekPosition(end);
		for (int i=si; i=0)&&(offset+length<=this.length));
		double[] data=internalData();
		Index index=internalIndex();
		
		int start=index.seekPosition(offset);
		int[] ixs=index.data;
		int dataLength=data.length;
		for (int j=start; j=length) return;
			array[arrayOffset+di]+=data[j];
		}
	}
	
	@Override
	public void addToArray(double[] dest, int offset) {
		double[] data=internalData();
		int[] ixs=internalIndexArray();
		int dataLength=data.length;
		for (int i=0; i=(offset+length)) return;
			array[di+aOffset]+=factor*data[i];
		}
	}
	
	@Override
	public void addProductToArray(double factor, int offset, AVector other,int otherOffset, double[] array, int arrayOffset, int length) {
		if (other instanceof ADenseArrayVector) {
			addProductToArray(factor,offset,(ADenseArrayVector)other,otherOffset,array,arrayOffset,length);
			return;
		}
		assert(offset>=0);
		assert(offset+length<=length());
		
		double[] data=internalData();
		Index index=internalIndex();
		int[] ixs=index.data;
		int dataLength=data.length;
		for (int j=index.seekPosition(offset); j=length) return;
			array[i+arrayOffset]+=factor*data[j]*other.get(i+otherOffset);
		}		
	}
	
	@Override
	public void addProductToArray(double factor, int offset, ADenseArrayVector other,int otherOffset, double[] array, int arrayOffset, int length) {
		assert(offset>=0);
		assert(offset+length<=length());
		double[] otherArray=other.getArray();
		otherOffset+=other.getArrayOffset();
		
		double[] data=internalData();
		Index index=internalIndex();
		int[] ixs=index.data;
		int dataLength=data.length;
		for (int j=index.seekPosition(offset); j=length) return;
			array[i+arrayOffset]+=factor*data[j]*otherArray[i+otherOffset];
		}		
	}
	
	@Override
	public boolean equalsArray(double[] ds, int offset) {
		double[] data=internalData();
		int[] ixs=internalIndexArray();
		int n=data.length;
		if (n==0) return DoubleArrays.isZero(ds, offset, length);
		int di=0;
		int i=0;
		while (di=data.length) break;
			}
		}
		return SparseIndexedVector.wrap(length, nixs, ndata);
	}
		
	@Override 
	public final void getElements(double[] array, int offset) {
		Arrays.fill(array,offset,offset+length,0.0);
		copySparseValuesTo(array,offset);
	}
	
	@Override public final void copyTo(AVector v, int offset) {
		if (v instanceof ADenseArrayVector) {
			ADenseArrayVector av=(ADenseArrayVector)v;
			getElements(av.getArray(),av.getArrayOffset()+offset);
		}
		v.fillRange(offset,length,0.0);
		double[] data=internalData();
		int[] ixs=internalIndexArray();
		for (int i=0; i