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Commit 2034f62d authored by Andrey Filippov's avatar Andrey Filippov
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got stable convergence of LMA when doing kernel factorization

parent 7d130db1
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Showing with 218 additions and 132 deletions
src/main/java/EyesisCorrectionParameters.java
View file @ 2034f62d
......@@ -1652,6 +1652,7 @@ public class EyesisCorrectionParameters {
public int asym_size = 6; //
public int dct_window = 1; // currently only 3 types of windows - 0 (none), 1 and 2
public int LMA_steps = 100;
public double fact_precision=0.003; // stop iterations if error rms less than this part of target kernel rms
public double compactness = 1.0;
public int asym_tax_free = 1; // "compactness" does not apply to pixels with |x|<=asym_tax_free and |y| <= asym_tax_free
public double dbg_x =0;
......@@ -1672,6 +1673,7 @@ public class EyesisCorrectionParameters {
properties.setProperty(prefix+"asym_size",this.asym_size+"");
properties.setProperty(prefix+"dct_window", this.dct_window+"");
properties.setProperty(prefix+"compactness", this.compactness+"");
properties.setProperty(prefix+"fact_precision", this.fact_precision+"");
properties.setProperty(prefix+"asym_tax_free", this.asym_tax_free+"");
properties.setProperty(prefix+"dbg_x", this.dbg_x+"");
......@@ -1686,6 +1688,7 @@ public class EyesisCorrectionParameters {
if (properties.getProperty(prefix+"asym_size")!=null) this.asym_size=Integer.parseInt(properties.getProperty(prefix+"asym_size"));
if (properties.getProperty(prefix+"dct_window")!=null) this.dct_window=Integer.parseInt(properties.getProperty(prefix+"dct_window"));
if (properties.getProperty(prefix+"compactness")!=null) this.compactness=Double.parseDouble(properties.getProperty(prefix+"compactness"));
if (properties.getProperty(prefix+"fact_precision")!=null) this.fact_precision=Double.parseDouble(properties.getProperty(prefix+"fact_precision"));
if (properties.getProperty(prefix+"asym_tax_free")!=null) this.asym_tax_free=Integer.parseInt(properties.getProperty(prefix+"asym_tax_free"));
if (properties.getProperty(prefix+"dbg_x")!=null) this.dbg_x=Double.parseDouble(properties.getProperty(prefix+"dbg_x"));
......@@ -1701,6 +1704,7 @@ public class EyesisCorrectionParameters {
gd.addNumericField("MDCT window type (0,1,2)", this.dct_window, 0); //0..2
gd.addNumericField("LMA_steps", this.LMA_steps, 0); //0..2
gd.addNumericField("Compactness (punish off-center asym_kernel pixels (proportional to r^2)", this.compactness, 2); //0..2
gd.addNumericField("Factorization target precision (stop if achieved)", this.fact_precision, 4); //0..2
gd.addNumericField("Do not punish pixels in the square around center", this.asym_tax_free, 0); //0..2
gd.addNumericField("dbg_x", this.dbg_x, 2); //0..2
......@@ -1716,6 +1720,7 @@ public class EyesisCorrectionParameters {
this.dct_window= (int) gd.getNextNumber();
this.LMA_steps = (int) gd.getNextNumber();
this.compactness = gd.getNextNumber();
this.fact_precision = gd.getNextNumber();
this.asym_tax_free = (int) gd.getNextNumber();
this.dbg_x= gd.getNextNumber();
this.dbg_y= gd.getNextNumber();
......
src/main/java/Eyesis_Correction.java
View file @ 2034f62d
......@@ -2877,8 +2877,9 @@ private Panel panel1,panel2,panel3,panel4,panel5,panel5a, panel6,panel7,panelPos
boolean result = factorConvKernel.calcKernels(
target_expanded,
DCT_PARAMETERS.asym_size,
DCT_PARAMETERS.dct_size);
System.out.println("factorConvKernel.calcKernels() returned"+result);
DCT_PARAMETERS.dct_size,
DCT_PARAMETERS.fact_precision);
System.out.println("factorConvKernel.calcKernels() returned: >>> "+result+ " <<<");
double [] sym_kernel = factorConvKernel.getSymKernel();
double [] asym_kernel = factorConvKernel.getAsymKernel();
double [] convolved = factorConvKernel.getConvolved();
......
src/main/java/FactorConvKernel.java
View file @ 2034f62d
......@@ -27,12 +27,24 @@ import Jama.LUDecomposition;
import Jama.Matrix;
import ij.IJ;
/*
* TODO: Add options for rotation-symmetrical (2-fold) kernels (with the same center as the symmetrical one for DCT) and a
* sub-pixel shift (4 (2x2) pixel kernel. As the target kernel is rather smooth, half-pixel shift widening can be absorbed
* by corresponding "sharpening" of the symmetrical kernel.
*
* Try to minimize (or limit) the number of non-zero elements of asymmetrical kernels - they are convolved directly with the
* Bayer mosaic data.
* 1. Do as now, then select N of the highest absolute value asymmetrical elements, mask out (and zero) all others
* 2. Optionally try to improve: Remove some from step 1, then add one-by-one: select from neighbors (or neighbors of neighbors?)
* add the one that gets the best improvement
*/
public class FactorConvKernel {
public int asym_size = 6;
public int sym_radius = 8; // 2*2^n - for DCT
public double[] target_kernel = null; // should be expanded to 2*(sym_radius)+asym_size- 1 in each direction
// public double[] asym_kernel = null;
// public double[] sym_kernel = null;
public double target_rms; // Target kernel rma (to compare with residual error)
public int debugLevel = 3;
public double init_lambda = 0.001;
......@@ -98,11 +110,12 @@ public class FactorConvKernel {
public boolean calcKernels(
double []target_kernel,
int asym_size,
int sym_radius){
int sym_radius,
double fact_precision){
this.asym_size = asym_size;
this.sym_radius = sym_radius;
this.target_kernel = target_kernel;
return levenbergMarquardt();
return levenbergMarquardt(fact_precision);
}
public double [] getSymKernel(){
......@@ -114,7 +127,9 @@ public class FactorConvKernel {
}
public double [] getConvolved(){ // check that it matches original
return this.currentfX;
double [] convolved = new double [target_kernel.length];
System.arraycopy(currentfX, 0, convolved, 0, convolved.length);
return convolved;
}
public void setDebugLevel(int debugLevel){
......@@ -174,8 +189,6 @@ public class FactorConvKernel {
scy += d*(i-sym_rad_m1-asym_size/2);
}
}
// int async_center = asym_size / 2; // will be 2 for 5x5 and 3 for 6x6 - more room in negative
int j0= (int) Math.round(sx/s0 - sym_rad_m1); // should be ~ async_center
int i0= (int) Math.round(sy/s0 - sym_rad_m1); // should be ~ async_center
......@@ -188,8 +201,6 @@ public class FactorConvKernel {
System.out.println("setInitialVector(): fi0 = "+(sy/s0 - sym_rad_m1)+" i0 = "+i0 );
}
// fit i0,j0 to asym_kernel (it should be larger)
// i0 += asym_size/2;
// j0 += asym_size/2;
if ((i0<0) || (i0>=asym_size) || (i0<0) || (i0>=asym_size)){
System.out.println("Warning: kernel center too far : i="+(i0 - asym_size/2)+", j= "+(j0 - asym_size/2));
if (i0 < 0) i0=0;
......@@ -210,9 +221,6 @@ public class FactorConvKernel {
}
}
// i0 -= asym_size/2;
// j0 -= asym_size/2;
double [] sym_kernel = new double [sym_radius * sym_radius];
int [] sym_kernel_count = new int [sym_radius * sym_radius];
for (int i = 0; i < sym_kernel.length; i++){
......@@ -256,23 +264,63 @@ public class FactorConvKernel {
return kvect;
}
private double [] convKernels(
private double [] getDerivDelta( // calcualte approximate partial derivative as delta
double [] kvect, // parameters vector
int indx, // index of parameter to calculate approximate derivative
double delta
){
double [] kvect_inc = kvect.clone();
double [] fx = getFX( kvect);
kvect_inc[indx] += delta;
double [] fx1 = getFX( kvect_inc);
for (int i = 0; i < fx.length; i++){
fx[i] = (fx1[i]-fx[i])/delta;
}
return fx;
}
public double [] compareDerivative(
int indx,
double delta, // value to increment parameter by for derivative calculation
boolean verbose){
double [] rslt = {0.0,0.0};
double [][] jacob = getJacobian(this.currentVector);
double [] deriv = getDerivDelta(this.currentVector, indx, delta);
double [] fx = getFX(this.currentVector);
for (int i = 0; i< fx.length; i++) {
if (verbose) {
System.out.println(i+": "+(jacob[indx][i]-deriv[i])+ " jacob["+indx+"]["+i+"] = "+jacob[indx][i]+
" deriv["+i+"]="+deriv[i]+" f["+i+"]="+fx[i]);
}
rslt[0]+=(jacob[indx][i]-deriv[i])*(jacob[indx][i]-deriv[i]);
rslt[1]+=jacob[indx][i]*jacob[indx][i];
}
rslt[0] = Math.sqrt(rslt[0]/fx.length);
rslt[1] = Math.sqrt(rslt[1]/fx.length);
System.out.println("rms(jacob["+indx+"][]) = "+rslt[1]+", rms(diff) = "+rslt[0]);
return rslt;
}
private double [] getFX(
double [] kvect) // first - all elements of sym kernel but [0] (replaced by 1.0), then - asym ones
{
int conv_size = asym_size + 2*sym_radius-2;
int asym_start= sym_radius * sym_radius - 1;
int sym_radius_m1 = sym_radius -1;
double [] conv_data = new double [conv_size*conv_size];
int asym_terms_start= conv_size*conv_size;
double cw = getCompactWeight();
double [] fx = new double [conv_size*conv_size+asym_size*asym_size];
if (this.debugLevel>2){
System.out.println("convKernels(): vector_length= "+kvect.length);
System.out.println("convKernels(): sym_radius= "+sym_radius);
System.out.println("convKernels(): asym_size= "+asym_size);
System.out.println("convKernels(): conv_size= "+conv_size);
System.out.println("convKernels(): conv_data.length= "+conv_data.length);
System.out.println("convKernels(): asym_start= "+asym_start);
}
for (int i = 0; i < conv_data.length; i++) conv_data[i] = 0.0;
System.out.println("fx(): vector_length= "+kvect.length);
System.out.println("fx(): sym_radius= "+sym_radius);
System.out.println("fx(): asym_size= "+asym_size);
System.out.println("fx(): conv_size= "+conv_size);
System.out.println("fx(): fx.length= "+fx.length);
System.out.println("fx(): asym_start= "+asym_start);
}
for (int i = 0; i < fx.length; i++) fx[i] = 0.0;
for (int i = -sym_radius_m1; i <= sym_radius_m1; i++) {
for (int j = -sym_radius_m1; j <= sym_radius_m1; j++) {
int indx = ((i < 0)? -i : i) * sym_radius + ((j < 0)? -j : j);
......@@ -281,56 +329,26 @@ public class FactorConvKernel {
for (int ia=0; ia < asym_size; ia++) {
for (int ja=0; ja < asym_size; ja++) {
int async_index = asym_size*ia + ja + asym_start;
conv_data[base_indx + conv_size * ia + ja] += sd* kvect[async_index];
fx[base_indx + conv_size * ia + ja] += sd* kvect[async_index];
}
}
}
}
return conv_data;
}
private double getRms(
double [] conv_data,
double [] target_kernel){
double rms = 0;
for (int i=0; i < conv_data.length; i++){
double d = target_kernel[i] - conv_data[i];
rms += d * d;
}
return Math.sqrt(rms/conv_data.length);
}
private double getCompactRms(double [] kvect){ // uses global compactness_weight, sym_kernel_scale, center_i0, center_j0 and asym_kernel
double cw = getCompactWeight();
double rms = 0;
int indx = sym_radius * sym_radius -1;
for (int ia=0; ia <asym_size;ia++){
for (int ja=0;ja< asym_size;ja++){
int async_index = asym_size*ia + ja;
int ir2 = (ia-center_i0)*(ia-center_i0)+(ja-center_j0)*(ja-center_j0);
if ((ia - center_i0 <= asym_tax_free) &&
(center_i0 - ia <= asym_tax_free) &&
(ja - center_j0 <= asym_tax_free) &&
(center_j0 - ja <= asym_tax_free)) ir2 = 0;
double d = kvect[indx++]*ir2;
rms += d * d;
}
fx[async_index+asym_terms_start] = ir2 * cw* kvect[asym_start + async_index];
}
// return cw*Math.sqrt(rms)/(asym_size*asym_size); // square for area, another - to normalize by r^2
return cw*Math.sqrt(rms)/asym_size; // square for area, another - to normalize by r^2
}
/*
int sym_len= sym_radius * sym_radius;
double [] asym_kernel = new double [asym_size*asym_size];
int indx = sym_radius * sym_radius -1;
for (int i =0; i< asym_kernel.length; i++) asym_kernel[i] = scale * kvect[indx++];
if (debugLevel>1){
System.out.println("getAsymKernel(): asym_kernel.length="+asym_kernel.length);
return fx;
}
*/
private double getCompactWeight(){
// return compactness_weight*sym_kernel_scale/(asym_size*asym_size); // use
return compactness_weight*sym_kernel_scale; // (asym_size*asym_size*asym_size*asym_size); // use
}
......@@ -394,35 +412,43 @@ public class FactorConvKernel {
private double [] getJTByDiff(
double [][] jacob, // jacobian
double [] target_kernel, // target kernel
double [] conv_data, // current convolution result of async_kernel (*) sync_kernel
double [] fx, // current convolution result of async_kernel (*) sync_kernel, extended by asym_kernel components
double [] kvect // parameter vector - used asym values
){
double [] jTByDiff = new double [jacob.length];
for (int i=0; i < jTByDiff.length; i++){
jTByDiff[i] = 0;
for (int k=0; k< target_kernel.length; k++){
jTByDiff[i] += jacob[i][k]*(target_kernel[k]-conv_data[k]);
}
for (int k = 0; k< target_kernel.length; k++){
jTByDiff[i] += jacob[i][k]*(target_kernel[k]-fx[k]);
}
int conv_size = asym_size + 2*sym_radius-2;
int asym_start= sym_radius * sym_radius - 1;
int asym_terms_start= conv_size*conv_size;
double cw = getCompactWeight();
for (int ia=0; ia <asym_size;ia++){
for (int ja=0;ja< asym_size;ja++){
int async_index = asym_size*ia + ja;
int ir2 = (ia-center_i0)*(ia-center_i0)+(ja-center_j0)*(ja-center_j0);
if ((ia - center_i0 <= asym_tax_free) &&
(center_i0 - ia <= asym_tax_free) &&
(ja - center_j0 <= asym_tax_free) &&
(center_j0 - ja <= asym_tax_free)) ir2 = 0;
jTByDiff[async_index + asym_start] += jacob[async_index + asym_start][async_index+asym_terms_start]*
ir2 * cw* kvect[asym_start + async_index];
for (int k = target_kernel.length; k< fx.length; k++){
jTByDiff[i] += jacob[i][k]*(-fx[k]);
}
}
return jTByDiff;
}
private double[] getDiffByDiff(
double [] target_kernel, // target kernel
double [] fx, // current convolution result of async_kernel (*) sync_kernel, extended async kernel components
double [] kvect // parameter vector - used asym values
){
double [] diffByDiff = {0.0,0.0};
for (int k=0; k< target_kernel.length; k++){
double d = target_kernel[k]-fx[k];
diffByDiff[0] += d*d;
}
diffByDiff[1] = diffByDiff[0]; // actual squared error, without compactness
for (int k=target_kernel.length; k< fx.length; k++){
double d = fx[k];
diffByDiff[0] += d*d;
}
if (this.debugLevel > 2){
System.out.println("getDiffByDiff: diffByDiff[0]="+diffByDiff[0]+" diffByDiff[1]="+diffByDiff[1]);
}
return diffByDiff;
}
private double [] solveLMA(
LMAArrays lMAArrays,
......@@ -463,18 +489,25 @@ public class FactorConvKernel {
return Ma.getColumnPackedCopy();
}
private boolean levenbergMarquardt(){
private boolean levenbergMarquardt(double fact_precision){
double goal_rms_pure; // ext if pure error rms is smaller (or stoped/failed to improve)
double s = 0.0;
for (int i = 0; i<target_kernel.length; i++){
s+= target_kernel[i]*target_kernel[i];
}
goal_rms_pure = Math.sqrt(s/target_kernel.length)*fact_precision;
this.startTime=System.nanoTime();
this.firstRMS=-1; //undefined
this.currentVector = setInitialVector(target_kernel); // should be (asym_size + 2*sym_radius-1)**2
if (this.numIterations < 0){
this.currentfX=convKernels (this.currentVector); // to be able to read back
this.currentfX=getFX (this.currentVector);
return true;
}
while (true) { // loop for the same series
boolean [] state=stepLevenbergMarquardtFirst();
boolean [] state=stepLevenbergMarquardtFirst(goal_rms_pure);
if (this.debugLevel>1) System.out.println(this.iterationStepNumber+": stepLevenbergMarquardtFirst()==>"+state[1]+":"+state[0]);
// boolean cont=true;
// Make it success if this.currentRMS<this.firstRMS even if LMA failed to converge
......@@ -516,11 +549,30 @@ public class FactorConvKernel {
if (this.debugLevel>0) System.out.println("LevenbergMarquardt(): RMS="+this.currentRMS+
" ("+this.firstRMS+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()-this.startTime),3));
if (this.debugLevel>2) {
double worstRatio = 0;
int worstIndex = -1;
for (int i = 0; i<currentVector.length;i++) {
double [] r=compareDerivative(
i,
0.0000001, // delta, // value to increment parameter by for derivative calculation
false); // verbose)
if (r[1] > 0){
if (r[0]/r[1] > worstRatio){
worstRatio = r[0]/r[1];
worstIndex = i;
}
}
}
System.out.println("rms(relative diff["+worstIndex+"]) = "+worstRatio);
}
return true; // all series done
}
private boolean [] stepLevenbergMarquardtFirst(){
private boolean [] stepLevenbergMarquardtFirst(double goal_rms_pure){
double [] deltas=null;
double [] rmses; // [0]: full rms, [1]:pure rms
if (this.currentVector==null) {
this.currentRMS=-1;
this.currentRMSPure=-1;
......@@ -537,9 +589,9 @@ public class FactorConvKernel {
System.out.println(i+": "+ this.currentVector[i]);
}
}
// if ((this.currentfX==null)|| ((this.jacobian==null) && !this.threadedLMA )) {
if ((this.currentfX==null)|| (this.lMAArrays==null)) {
this.currentfX=convKernels (this.currentVector); // first - all elements of sym kernel but [0] (replaced by 1.0), then - asym ones
this.currentfX=getFX (this.currentVector);
this.jacobian = getJacobian (this.currentVector);
this.lMAArrays= new LMAArrays();
lMAArrays.jTByJ=getJTByJ(
......@@ -550,12 +602,18 @@ public class FactorConvKernel {
this.currentfX, // current convolution result of async_kernel (*) sync_kernel
this.currentVector); // used to control compactness of asym_kernel
this.currentRMSPure= getRms(this.currentfX,this.target_kernel);
/// this.currentRMS= getCompactRms(this.currentVector) + this.currentRMSPure;
double compactRMS = getCompactRms(this.currentVector);
this.currentRMS= Math.sqrt((compactRMS * asym_size*asym_size +
(currentRMSPure)*(currentRMSPure)*target_kernel.length)/(asym_size*asym_size+target_kernel.length));
rmses = getDiffByDiff(
this.target_kernel, // target kernel
this.currentfX, // current convolution result of async_kernel (*) sync_kernel
this.currentVector);
this.currentRMSPure= Math.sqrt(rmses[1] / target_kernel.length);
this.currentRMS = Math.sqrt(rmses[0] / (asym_size*asym_size+target_kernel.length));
if (debugLevel > 1){
System.out.println("currentRMSPure= "+ currentRMSPure + " getDiffByDiff[1] = "+Math.sqrt(getDiffByDiff(
this.target_kernel, // target kernel
this.currentfX, // current convolution result of async_kernel (*) sync_kernel
this.currentVector)[1] / target_kernel.length));
}
if (this.debugLevel>1) {
System.out.println("initial RMS="+IJ.d2s(this.currentRMS,8)+
......@@ -563,14 +621,22 @@ public class FactorConvKernel {
". Calculating next Jacobian. Points:"+this.target_kernel.length+" Parameters:"+this.currentVector.length);
}
} else {
this.currentRMSPure= getRms(this.currentfX,this.target_kernel);
// this.currentRMS= getCompactRms(this.currentVector) + this.currentRMSPure;
double compactRMS = getCompactRms(this.currentVector);
this.currentRMS= Math.sqrt((compactRMS * asym_size*asym_size +
(currentRMSPure)*(currentRMSPure)*target_kernel.length)/(asym_size*asym_size+target_kernel.length));
rmses = getDiffByDiff(
this.target_kernel, // target kernel
this.currentfX, // current convolution result of async_kernel (*) sync_kernel
this.currentVector);
this.currentRMSPure= Math.sqrt(rmses[1] / target_kernel.length);
this.currentRMS = Math.sqrt(rmses[0] / (asym_size*asym_size+target_kernel.length));
if (debugLevel > 2){
System.out.println("this.currentRMS=" + this.currentRMS+ " getDiffByDiff[1] = "+Math.sqrt(getDiffByDiff(
this.target_kernel, // target kernel
this.currentfX, // current convolution result of async_kernel (*) sync_kernel
this.currentVector)[1] / target_kernel.length));
}
}
// this.currentRMS= calcError(calcYminusFx(this.currentfX));
if (this.firstRMS<0) {
this.firstRMS=this.currentRMS;
this.firstRMSPure=this.currentRMSPure;
......@@ -603,15 +669,10 @@ public class FactorConvKernel {
}
}
// this.savedJacobian=this.jacobian;
this.savedLMAArrays=lMAArrays.clone();
this.jacobian=null; // not needed, just to catch bugs
// calculate next vector and Jacobian (this.jacobian)
// this.nextfX=calculateFxAndJacobian(this.nextVector,true); //=========== OLD
this.nextfX=getFX (this.nextVector);
// this.nextfX=calculateFxAndJacobian(this.nextVector,true);
// this.lMAArrays=calculateJacobianArrays(this.nextfX);
this.nextfX=convKernels(this.nextVector);
this.jacobian = getJacobian(this.currentVector);
this.lMAArrays= new LMAArrays();
......@@ -623,13 +684,19 @@ public class FactorConvKernel {
this.nextfX, // next convolution result of async_kernel (*) sync_kernel
this.nextVector); // used to control compactness of asym_kernel
rmses = getDiffByDiff(
this.target_kernel, // target kernel
this.nextfX, // current convolution result of async_kernel (*) sync_kernel
this.nextVector);
this.nextRMSPure= Math.sqrt(rmses[1] / target_kernel.length);
this.nextRMS = Math.sqrt(rmses[0] / (asym_size*asym_size+target_kernel.length));
this.nextRMSPure= getRms(this.nextfX,this.target_kernel);
// this.nextRMS= getCompactRms(this.nextVector) + this.nextRMSPure;
double nextCompactRMS = getCompactRms(this.currentVector);
this.nextRMS= Math.sqrt((nextCompactRMS * asym_size*asym_size +
(nextRMSPure)*(nextRMSPure)*target_kernel.length)/(asym_size*asym_size+target_kernel.length));
if (debugLevel > 2){
System.out.println("nextRMSPure= "+ nextRMSPure + " target_kernel.length = "+target_kernel.length+" getDiffByDiff[1] = "+Math.sqrt(getDiffByDiff(
this.target_kernel, // target kernel
this.nextfX, // current convolution result of async_kernel (*) sync_kernel
this.nextVector)[1] / target_kernel.length));
}
this.lastImprovements[1]=this.lastImprovements[0];
this.lastImprovements[0]=this.currentRMS-this.nextRMS;
......@@ -666,6 +733,19 @@ public class FactorConvKernel {
(this.lastImprovements[0]<this.thresholdFinish*this.currentRMS) &&
(this.lastImprovements[1]>=0.0) &&
(this.lastImprovements[1]<this.thresholdFinish*this.currentRMS));
if (!status[1] && (this.currentRMSPure < goal_rms_pure)) {
status[1] = true;
if (this.debugLevel>1) {
System.out.println("Improvent is possible, but the factorization precision reached its goal");
System.out.println(
"stepLMA this.currentRMS="+this.currentRMS+
", this.currentRMSPure="+this.currentRMSPure+
", this.nextRMS="+this.nextRMS+
", this.nextRMSPure="+this.nextRMSPure+
", delta="+(this.currentRMS-this.nextRMS)+
", deltaPure="+(this.currentRMSPure-this.nextRMSPure));
}
}
} else if (matrixNonSingular){
// this.jacobian=this.savedJacobian;// restore saved Jacobian
this.lMAArrays=this.savedLMAArrays; // restore Jt*J and Jt*diff
......
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