Java AMatrix.columnCount方法代码示例

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
/**
    * <p>
    * To decompose the matrix 'A' it must have full rank.  'A' is a 'm' by 'n' matrix.
    * It requires about 2n*m<sup>2</sup>-2m<sup>2</sup>/3 flops.
    * </p>
    *
    * <p>
    * The matrix provided here can be of different
    * dimension than the one specified in the constructor.  It just has to be smaller than or equal
    * to it.
    * </p>
    */
   @Override
public QRResult decompose( AMatrix A ) {
       this.numCols = A.columnCount();
       this.numRows = A.rowCount();
       minLength = Math.min(numCols,numRows);
       int maxLength = Math.max(numCols,numRows);

       dataQR = new double[ numCols ][  numRows ];
       v = new double[ maxLength ];
       gammas = new double[ minLength ];
       
       convertToColumnMajor(A);

       error = false;

       for( int j = 0; j < minLength; j++ ) {
           householder(j);
           updateA(j);
       }

   //  if (error)
   //      return null;
   //  else
       return new QRResult(getQ(), getR());
   }
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
/**
 * <p>
 * Performs Choleksy decomposition on the provided matrix.
 * </p>
 *
 * <p>
 * If the matrix is not positive definite then this function will return
 * null since it can't complete its computations.  Not all errors will be
 * found.  This is an efficient way to check for positive definiteness.
 * </p>
 * @param mat A symmetric positive definite matrix
 * @return CholeskyResult if decomposition is successful, null otherwise
 */
@Override
protected ICholeskyResult _decompose( AMatrix mat ) {
	int rc=mat.rowCount();
	int cc=mat.columnCount();
	if( rc != cc ) {
        throw new IllegalArgumentException("Must be a square matrix.");
    }

    n = mat.rowCount();
    this.vv = new double[n];
    t=mat.toDoubleArray();
    T = Matrix.wrap(rc, cc, t);
    
    if(mat.rowCount() < blockWidth) {
		B = Matrix.create(0,0);
	}
	else {
		B = Matrix.create(blockWidth,n);
	}
	chol = new CholeskyHelper(blockWidth);

    return decomposeLower();
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
/**
 * Performs QR decomposition on A
 *
 * @param A not modified.
 */
public boolean setA(AMatrix A) {
    if( A.rowCount() > maxRows || A.columnCount() > maxCols )
        setMaxSize(A.rowCount(),A.columnCount());

    a = Matrix.create(A.rowCount(),1);
    temp = Matrix.create(A.rowCount(),1);

    _setA(A);
    result = decomposer.decompose(A);
    if( result == null )
        return false;

    gammas = decomposer.getGammas();
    QR = decomposer.getQR();
    R = result.getR().toMatrix();
    return true;
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
/**
 * <p>
 * Using the decomposition, finds the value of 'X' in the linear equation below:<br>
 *
 * A*x = b<br>
 *
 * where A has dimension of n by n, x and b are n by m dimension.
 * </p>
 * <p>
 * *Note* that 'b' and 'x' can be the same matrix instance.
 * </p>
 *
 * @param B A matrix that is n by m.  Not modified.
 * @param X An n by m matrix where the solution is writen to.  Modified.
 */
public AMatrix solve(AMatrix B) {
	Matrix X = Matrix.create(B.rowCount(), B.columnCount());
    if( B.columnCount() != X.columnCount() && B.rowCount() != n && X.rowCount() != n) {
        throw new IllegalArgumentException("Unexpected matrix size");
    }

    int numCols = B.columnCount();

    double dataB[] = B.toMatrix().data;
    double dataX[] = X.data;

    for( int j = 0; j < numCols; j++ ) {
        for( int i = 0; i < n; i++ ) vv[i] = dataB[i*numCols+j];
        solveInternal();
        for( int i = 0; i < n; i++ ) dataX[i*numCols+j] = vv[i];
    }
    return X;
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
/**
 * Wraps data wrom source matrix in a BlasMatrix if possible, otherwise creates a nwew BlasMatrix
 * @param m
 * @return
 */
public static BlasMatrix wrapOrCreate(AMatrix m) {
	if (m instanceof AStridedMatrix) {
		AStridedMatrix sm=(AStridedMatrix)m;
		if (sm.getArrayOffset()==0) {
			if (sm.columnStride()==1) {
				return new BlasMatrix(sm.getArray(),m.rowCount(),m.columnCount(),0,sm.rowStride(),1);
			}
			if (sm.rowStride()==1) {
				return new BlasMatrix(sm.getArray(),m.rowCount(),m.columnCount(),0,1,sm.columnStride());
			}
		}
	}
	return create(m);
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
public static SparseColumnMatrix create(AMatrix source) {
	if (source instanceof SparseRowMatrix) return ((SparseRowMatrix)source).toSparseColumnMatrix();
	int cc=source.columnCount();
	int rc=source.rowCount();
	AVector[] data = new AVector[cc];
	for (int i=0; i<cc; i++) {
		AVector col = source.getColumn(i);
		if (!col.isZero())
		    data[i] = Vectorz.createSparse(col);
	}
	return new SparseColumnMatrix(data,rc,cc);
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
public static LowerTriangularMatrix createFrom(AMatrix m) {
	int rc=m.rowCount();
	int cc=m.columnCount();
	LowerTriangularMatrix r = new LowerTriangularMatrix(rc,cc);
	for (int j=0; j<cc; j++) {
		for (int i=j; i<rc; i++) {
			r.unsafeSet(i, j, m.unsafeGet(i, j));
		}
	}
	return r;
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
@Override
public AVector innerProduct(AMatrix m) {
	int rc=m.rowCount();
	int cc=m.columnCount();
	this.checkLength(rc);
	AVector r=Vector.createLength(cc);
	List<AVector> cols=m.getColumns();
	for (int i=0; i<cc; i++) {
		AVector col=cols.get(i);
		r.unsafeSet(i, value*col.elementSum());
	}
	return r;
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
public Affine23(AMatrix m, AVector v) {
	if ((v.length()!=2)||(m.columnCount()!=2)||(m.rowCount()!=2)) {
		throw new IllegalArgumentException("Wrong source sizes for Affine23");
	}
	m00=m.unsafeGet(0,0);
	m01=m.unsafeGet(0,1);
	m10=m.unsafeGet(1,0);
	m11=m.unsafeGet(1,1);
	tr0=v.unsafeGet(0);
	tr1=v.unsafeGet(1);
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
public AffineMN(AMatrix matrix, ATranslation translation) {
	this.matrix=matrix;
	this.translation=translation;
	inputDimensions=matrix.columnCount();
	outputDimensions=matrix.rowCount();
	if (outputDimensions!=translation.inputDimensions()) {
		throw new IllegalArgumentException("matrix and translation have incompatible dimensionality");
	}
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
public static VectorMatrixMN create(AMatrix source) {
	int rc=source.rowCount();
	VectorMatrixMN m=new VectorMatrixMN(source.rowCount(),source.columnCount());
	for (int i=0; i<rc; i++) {
		m.rowData[i].set(source.getRow(i));
	}
	return m;
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
@Override
public AVector innerProduct(AMatrix m) {
	if (m instanceof SparseRowMatrix) {
		return this.innerProduct((SparseRowMatrix) m);
	}
	if (m instanceof SparseColumnMatrix) {
		return this.innerProduct((SparseColumnMatrix) m);
	}
	int cc = m.columnCount();
	int rc = m.rowCount();
	checkLength(rc);
	AVector result = SparseIndexedVector.createLength(cc);
	int nzc = 0;
	for (int i = 0; i < cc; i++) {
		double v=this.dotProduct(m.getColumn(i));
		if (v==0.0) continue;
		
		nzc++;
		if ((i>20)&&(nzc>(i/4))) {
			// switch to dense vector
			result=result.toVector();
		}
		
		result.unsafeSet(i, v);
	}
	return result;
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
/**
     * This attempts to improve upon the solution generated by account
     * for numerical imprecisions.  See numerical recipes for more information.  It
     * is assumed that solve has already been run on 'b' and 'x' at least once.
     *
     * @param b A matrix. Not modified.
     * @param x A matrix. Modified.
     */
    public void improveSol( AMatrix b , AMatrix x )
    {
        if( b.columnCount() != x.columnCount() ) {
            throw new IllegalArgumentException("bad shapes");
        }

        double dataA[] = A.asDoubleArray();
        double dataB[] = b.asDoubleArray();
        double dataX[] = x.asDoubleArray();

        final int nc = b.columnCount();
        final int n = b.columnCount();

        double []vv = decomp._getVV();
//        AMatrix LU = decomp.getLU();

//        BigDecimal sdp = new BigDecimal(0);
        for( int k = 0; k < nc; k++ ) {
            for( int i = 0; i < n; i++ ) {
                // *NOTE* in the book this is a long double.  extra precision might be required
                double sdp = -dataB[ i * nc + k];
//                BigDecimal sdp = new BigDecimal(-dataB[ i * nc + k]);
                for( int j = 0; j < n; j++ ) {
                    sdp += dataA[i* n +j] * dataX[ j * nc + k];
//                    sdp = sdp.add( BigDecimal.valueOf(dataA[i* n +j] * dataX[ j * nc + k]));
                }
                vv[i] = sdp;
//                vv[i] = sdp.doubleValue();
            }
            decomp._solveVectorInternal(vv);
            for( int i = 0; i < n; i++ ) {
                dataX[i*nc + k] -= vv[i];
            }
        }
    }
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
@Override
public AStridedMatrix broadcastLike(AMatrix target) {
	if (length()==target.columnCount()) {
		return StridedMatrix.wrap(getArray(), target.rowCount(), length(), getArrayOffset(), 0, getStride());
	} else {
		throw new IllegalArgumentException(ErrorMessages.incompatibleBroadcast(this, target));
	}
}
 

import mikera.matrixx.AMatrix; //导入方法依赖的package包/类
public static DenseColumnMatrix create(AMatrix m) {
	DenseColumnMatrix dm= new DenseColumnMatrix(m.rowCount(), m.columnCount());
	dm.set(m);
	return dm;
}