Java CommonOps.sub方法代码示例

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from sample space into eigen space.
 *
 * @param sampleData Sample space data.
 * @return Eigen space projection.
 */
public double[] sampleToEigenSpace(double[] sampleData) {
    if (sampleData.length != getDataMatrix().getNumCols()) {
        throw new IllegalArgumentException("Unexpected sample length");
    }
    DenseMatrix64F mean = DenseMatrix64F.wrap(getDataMatrix().getNumCols(), 1, this.getMean());

    DenseMatrix64F s = new DenseMatrix64F(getDataMatrix().getNumCols(), 1, true, sampleData);
    DenseMatrix64F r = new DenseMatrix64F(getNumPrincipalComponents(), 1);

    CommonOps.sub(s, mean, s);

    CommonOps.mult(getPrincipalComponentSubspace(), s, r);

    return r.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from sample space into eigen space. If {@link #doWhitening} is true, then the vector
 * is also L2 normalized after projection.
 * 
 * @param sampleData
 *            Sample space vector
 * @return Eigen space projected vector
 * @throws Exception
 */
public double[] sampleToEigenSpace(double[] sampleData) throws Exception {
	if (!isPcaInitialized) {
		// initiallize PCA correctly!
		throw new Exception("PCA is not correctly initiallized!");
	}
	if (sampleData.length != sampleSize) {
		throw new IllegalArgumentException("Unexpected vector length!");
	}
	DenseMatrix64F sample = new DenseMatrix64F(sampleSize, 1, true, sampleData);
	DenseMatrix64F projectedSample = new DenseMatrix64F(numComponents, 1);
	// mean subtraction
	CommonOps.sub(sample, means, sample);
	// projection
	CommonOps.mult(V_t, sample, projectedSample);
	// whitening
	if (doWhitening) { // always perform this normalization step when whitening is used
		return Normalization.normalizeL2(projectedSample.data);
	} else {
		return projectedSample.data;
	}
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from sample space into eigen space.
 *
 * @param sampleData Sample space data.
 * @return Eigen space projection.
 */
public double[] sampleToEigenSpace( double[] sampleData ) {
   if( sampleData.length != A.getNumCols() )
      throw new IllegalArgumentException("Unexpected sample length");
   DenseMatrix64F mean = DenseMatrix64F.wrap(A.getNumCols(),1,this.mean);

   DenseMatrix64F s = new DenseMatrix64F(A.getNumCols(),1,true,sampleData);
   DenseMatrix64F r = new DenseMatrix64F(numComponents,1);

   CommonOps.sub(s,mean,s);

   CommonOps.mult(V_t,s,r);

   return r.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Sets up the system of equations which are to be solved.  This equation is derived from
 * constraints (3) and (4) in the paper.   See section 3.1.
 *
 * @param homographies set of observed homographies.
 */
private void setupA( List<DenseMatrix64F> homographies ) {
	A.reshape(2*homographies.size(),6, false);

	DenseMatrix64F h1 = new DenseMatrix64F(3,1);
	DenseMatrix64F h2 = new DenseMatrix64F(3,1);

	DenseMatrix64F v12 = new DenseMatrix64F(1,6);
	DenseMatrix64F v11 = new DenseMatrix64F(1,6);
	DenseMatrix64F v22 = new DenseMatrix64F(1,6);

	DenseMatrix64F v11m22 = new DenseMatrix64F(1,6);

	for( int i = 0; i < homographies.size(); i++ ) {
		DenseMatrix64F H = homographies.get(i);

		CommonOps.extract(H,0,3,0,1,h1,0,0);
		CommonOps.extract(H,0,3,1,2,h2,0,0);

		// normalize H by the max value to reduce numerical error when computing A
		// several numbers are multiplied against each other and could become quite large/small
		double max1 = CommonOps.elementMaxAbs(h1);
		double max2 = CommonOps.elementMaxAbs(h2);
		double max = Math.max(max1,max2);

		CommonOps.divide(max,h1);
		CommonOps.divide(max,h2);

		// compute elements of A
		computeV(h1, h2, v12);
		computeV(h1, h1, v11);
		computeV(h2, h2, v22);

		CommonOps.sub(v11,v22,v11m22);

		CommonOps.insert( v12    , A, i*2   , 0);
		CommonOps.insert( v11m22 , A, i*2+1 , 0);
	}
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Similar to {@link #setupA(java.util.List)} but all references to B12 have been removed
 * since it will always be zero when the skew is zero
 *
 * @param homographies set of observed homographies.
 */
private void setupA_NoSkew( List<DenseMatrix64F> homographies ) {
	A.reshape(2*homographies.size(),5, false);

	DenseMatrix64F h1 = new DenseMatrix64F(3,1);
	DenseMatrix64F h2 = new DenseMatrix64F(3,1);

	DenseMatrix64F v12 = new DenseMatrix64F(1,5);
	DenseMatrix64F v11 = new DenseMatrix64F(1,5);
	DenseMatrix64F v22 = new DenseMatrix64F(1,5);

	DenseMatrix64F v11m22 = new DenseMatrix64F(1,5);

	for( int i = 0; i < homographies.size(); i++ ) {
		DenseMatrix64F H = homographies.get(i);

		CommonOps.extract(H,0,3,0,1,h1,0,0);
		CommonOps.extract(H,0,3,1,2,h2,0,0);

		// normalize H by the max value to reduce numerical error when computing A
		// several numbers are multiplied against each other and could become quite large/small
		double max1 = CommonOps.elementMaxAbs(h1);
		double max2 = CommonOps.elementMaxAbs(h2);
		double max = Math.max(max1,max2);

		CommonOps.divide(max,h1);
		CommonOps.divide(max,h2);

		// compute elements of A
		computeV_NoSkew(h1, h2, v12);
		computeV_NoSkew(h1, h1, v11);
		computeV_NoSkew(h2, h2, v22);

		CommonOps.sub(v11,v22,v11m22);

		CommonOps.insert( v12    , A, i*2   , 0);
		CommonOps.insert( v11m22 , A, i*2+1 , 0);
	}
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * R = W*U^T
 * N = v2 cross u
 * (1/d)*T = (H-R)*N
 */
private void createSolution( DenseMatrix64F W , DenseMatrix64F U , Vector3D_F64 u ,
							 DenseMatrix64F H ,
							 Se3_F64 se , Vector3D_F64 N )
{
	CommonOps.multTransB(W,U,se.getR());
	GeometryMath_F64.cross(v2,u,N);

	CommonOps.sub(H, se.getR(),tempM);
	GeometryMath_F64.mult(tempM,N,se.getT());
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Computes the homography based on two unique lines on the plane
 *
 * @param line0 Line on the plane
 * @param line1 Line on the plane
 */
public boolean process(PairLineNorm line0, PairLineNorm line1) {

	// Find plane equations of second lines in the first view
	double a0 = GeometryMath_F64.dot(e2,line0.l2);
	double a1 = GeometryMath_F64.dot(e2,line1.l2);

	GeometryMath_F64.multTran(A,line0.l2,Al0);
	GeometryMath_F64.multTran(A,line1.l2,Al1);

	// find the intersection of the planes created by each view of each line
	// first line
	planeA.set( line0.l1.x , line0.l1.y , line0.l1.z , 0 );
	planeB.set( Al0.x , Al0.y , Al0.z , a0 );

	if( !Intersection3D_F64.intersect(planeA,planeB,intersect0) )
		return false;
	intersect0.slope.normalize(); // maybe this will reduce overflow problems?

	// second line
	planeA.set( line1.l1.x , line1.l1.y , line1.l1.z , 0 );
	planeB.set( Al1.x , Al1.y , Al1.z , a1 );

	if( !Intersection3D_F64.intersect(planeA,planeB,intersect1) )
		return false;

	intersect1.slope.normalize();

	// compute the plane defined by these two lines
	from0to1.x = intersect1.p.x - intersect0.p.x;
	from0to1.y = intersect1.p.y - intersect0.p.y;
	from0to1.z = intersect1.p.z - intersect0.p.z;

	// the plane's normal will be the cross product of one of the slopes and a line connecting the two lines
	GeometryMath_F64.cross(intersect0.slope,from0to1,pi.n);
	pi.p.set(intersect0.p);

	// convert this plane description into general format
	UtilPlane3D_F64.convert(pi,pi_gen);

	v.set(pi_gen.A/pi_gen.D,pi_gen.B/pi_gen.D,pi_gen.C/pi_gen.D);

	// H = A - e2*v^T
	GeometryMath_F64.outerProd(e2,v,av);
	CommonOps.sub(A,av,H);

	// pick a good scale and sign for H
	adjust.adjust(H, line0);

	return true;
}