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Commit 19b8b05a authored by Andrey Filippov's avatar Andrey Filippov
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cleanup after debugging

parent f04c3e13
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Showing with 111 additions and 238 deletions
src/main/java/FactorConvKernel.java
View file @ 19b8b05a
......@@ -24,7 +24,6 @@
**
*/
import java.util.ArrayList;
import java.util.Random;
import Jama.LUDecomposition;
import Jama.Matrix;
......@@ -117,6 +116,7 @@ public class FactorConvKernel {
private int [][] map_from_pars = null; // first index - number in (compressed) parameter vector, value - a pair of {kernel_type, index}
private int [][] map_to_pars = null; // first index - kernel type, second - index in kernel, value index in parameter vector
public double [] fX = null;
public double [] saved_fX = null;
public double [][] jacobian = null;
private double [] target_kernel = null;
private boolean [] fx_mask = null;
......@@ -127,8 +127,11 @@ public class FactorConvKernel {
private int num_pure_points = 0;
private double [] par_vector = null;
private LMAArrays lMAArrays = null;
private LMAArrays savedLMAArrays = null;
public int debugLevel = 1;
public double [] rmses = {-1.0, -1.0}; // [0] - composite, [1] - "pure" (without extra "punishment")
public double [] saved_rmses = null;
public double [] first_rmses = null;
public LMAData(){
......@@ -142,56 +145,6 @@ public class FactorConvKernel {
public void initLMAData( int debugLevel){
this.debugLevel = debugLevel;
}
public LMAData clone(){
LMAData data = new LMAData();
data.sym_radius = sym_radius;
data.asym_size = asym_size;
for (int i=0;i<kernels.length;i++) if (kernels[i] != null) data.kernels[i] = kernels[i].clone();
for (int i=0;i<kernel_masks.length;i++) if (kernel_masks[i] != null) data.kernel_masks[i] = kernel_masks[i].clone();
if (map_from_pars!=null){
data.map_from_pars = new int[map_from_pars.length][];
for (int i=0;i<map_from_pars.length;i++) if (map_from_pars[i] != null) data.map_from_pars[i] = map_from_pars[i].clone();
}
if (map_to_pars!=null){
data.map_to_pars = new int[map_to_pars.length][];
for (int i=0;i<map_to_pars.length;i++) if (map_to_pars[i] != null) data.map_to_pars[i] = map_to_pars[i].clone();
}
if (fX!=null) data.fX = fX.clone();
if (jacobian!=null){
data.jacobian = new double[jacobian.length][];
for (int i=0;i<jacobian.length;i++) if (jacobian[i] != null) data.jacobian[i] = jacobian[i].clone();
}
if (target_kernel!=null) data.target_kernel = target_kernel.clone();
// for (int i=0;i<fx_masks.length;i++) if (fx_masks[i] != null) data.fx_masks[i] = fx_masks[i].clone();
if (fx_mask != null) data.fx_mask = fx_mask.clone();
if (asym_weights!=null) data.asym_weights = asym_weights.clone();
if (map_from_fx!=null){
data.map_from_fx = new int[map_from_fx.length][];
for (int i=0;i<map_from_fx.length;i++) if (map_from_fx[i] != null) data.map_from_fx[i] = map_from_fx[i].clone();
}
if (map_to_fx!=null){
data.map_to_fx = new int[map_to_fx.length][];
for (int i=0;i<map_to_fx.length;i++) if (map_to_fx[i] != null) data.map_to_fx[i] = map_to_fx[i].clone();
}
if (par_vector != null) data.par_vector = par_vector.clone();
if (lMAArrays!=null) data.lMAArrays =lMAArrays;
if (savedLMAArrays!=null) data.savedLMAArrays =savedLMAArrays;
if (savedKernels != null){
data.savedKernels = new double[savedKernels.length][];
for (int i=0; i<savedKernels.length;i++) data.savedKernels[i] = savedKernels[i].clone();
}
data.num_pure_points = num_pure_points;
data.debugLevel = debugLevel;
return data;
}
public void setSymKernel (double [] sym_kernel){ // does not set mask! Use ( , null) to set default one
kernels[0] = sym_kernel.clone();
......@@ -548,7 +501,6 @@ public class FactorConvKernel {
for (int aj = 0; aj < asym_size; aj ++){
int aindx = ai*asym_size + aj;
int fx_indx = map_to_fx[1][aindx]; // from index in the asym_kernel to fX vector
// int par_indx = map_to_pars[1][aindx];
if (fx_indx>=0) {
fX[fx_indx] += kernels[1][aindx] * asym_weights[aindx];
}
......@@ -556,8 +508,11 @@ public class FactorConvKernel {
}
}
this.fX = fX;
double [] diffs = getDiffByDiff();
rmses[0] = Math.sqrt(diffs[0] / getNumPoints());
rmses[1] = Math.sqrt(diffs[1] / getNumPurePoints());
if (first_rmses == null) first_rmses = rmses.clone();
}
// System.out.println(":::: fX[167]="+fX[167]);
return fX;
}
......@@ -622,18 +577,6 @@ public class FactorConvKernel {
}
}
}
/*
System.out.println("2:jacobian[63][167]="+jacobian[63][167]);
System.out.println("2:jacobian[29][167]="+jacobian[29][167]);
System.out.println("2:jacobian[64][167]="+jacobian[64][167]);
System.out.println("2:jacobian[38][167]="+jacobian[38][167]);
System.out.println("2:jacobian[65][167]="+jacobian[65][167]);
System.out.println("2:jacobian[45][167]="+jacobian[45][167]);
System.out.println("2:jacobian[66][167]="+jacobian[66][167]);
System.out.println("2:jacobian[52][167]="+jacobian[52][167]);
System.out.println("2:jacobian[67][167]="+jacobian[67][167]);
System.out.println("2:jacobian[61][167]="+jacobian[61][167]);
*/
// calculate asym kernel elements "handicaps"
for (int ai = 0; ai < asym_size; ai ++){
for (int aj = 0; aj < asym_size; aj ++){
......@@ -650,14 +593,6 @@ public class FactorConvKernel {
return jacobian;
}
public void saveLMAArrays()
{
savedLMAArrays = lMAArrays.clone();
}
public void restoreLMAArrays()
{
lMAArrays = savedLMAArrays.clone();
}
public void invalidateLMAArrays(){
lMAArrays = null;
savedLMAArrays = null;
......@@ -666,17 +601,45 @@ public class FactorConvKernel {
public boolean isValidLMAArrays(){
return lMAArrays != null;
}
public void saveKernels(){
public void save(){
savedKernels = new double[kernels.length][];
for (int i=0; i<kernels.length;i++) savedKernels[i] = kernels[i].clone();
saved_fX = fX.clone();
saved_rmses = rmses.clone();
}
public void restoreKernels(){
public void restore(){
kernels = new double[savedKernels.length][];
for (int i=0; i<savedKernels.length;i++) kernels[i] = savedKernels[i].clone();
fX = saved_fX; // no need to clone()
rmses = saved_rmses; // no need to clone()
}
public double [] getRMSes(){
return rmses;
}
public double [] getSavedRMSes(){
if (saved_rmses == null) {
double [] m1 = {-1.0,-1.0};
return m1;
}
return saved_rmses;
}
public double [] getFirstRMSes(){
if (first_rmses == null) {
double [] m1 = {-1.0,-1.0};
return m1;
}
return first_rmses;
}
public void resetRMSes(){
first_rmses = null;
saved_rmses = null;
}
public double [][] getJTByJ(){
return getJTByJ(true);
......@@ -726,7 +689,7 @@ public class FactorConvKernel {
return lMAArrays.jTByDiff;
}
public double[] getDiffByDiff()
private double[] getDiffByDiff()
{
double [] diffByDiff = {0.0,0.0};
for (int k = 0; k<fX.length; k++){
......@@ -1179,7 +1142,6 @@ public class FactorConvKernel {
if (i0 >= asym_size) i0=asym_size-1;
if (j0 >= asym_size) j0=asym_size-1;
}
Random rand = new Random();
double [] asym_kernel = new double [asym_size * asym_size];
for (int i = 0; i < asym_kernel.length; i++) asym_kernel[i] = 0; // 0.001*(rand.nextDouble()-0.5)/(asym_size*asym_size);
asym_kernel[asym_size*i0+j0] = 1.0;
......@@ -1240,33 +1202,7 @@ public class FactorConvKernel {
}
return kvect;
}
/*
// Remove masked out elements of the parameters vector
private double [] compressVector(
double [] kvect){
int comp_len = kvect.length;
for (int i = 0; i<kvect.length; i++) if (Double.isNaN(kvect[i])) comp_len--;
double [] cvect = new double[comp_len];
this.vector_mask = new boolean[kvect.length];
int indx = 0;
for (int i = 0; i<kvect.length; i++){
this.vector_mask[i] = !Double.isNaN(kvect[i]);
if (this.vector_mask[i]) cvect[indx++] = kvect[i];
}
return cvect;
}
private double [] expandVector(
double [] cvect,
double [] mvect){ // 'old' parameter vectors where Double.NaN means disabled
double [] kvect = new double [mvect.length];
int indx = 0;
for (int i = 0; i < mvect.length; i++){
if (!Double.isNaN(mvect[i])) kvect[i] = cvect[indx++];
else kvect[i] = Double.NaN;
}
return kvect;
}
*/
private void maskAsymPoint(
double [] kvect,
int indx) {
......@@ -1652,25 +1588,18 @@ public class FactorConvKernel {
lMAData.invalidateLMAArrays();
// this.currentVector = setVectorFromKernels(kernels[0], kernels[1]);
}
//lMAData
private boolean levenbergMarquardt(){
long startTime=System.nanoTime();
this.firstRMS=-1; //undefined
this.iterationStepNumber = 0;
this.lambda = this.init_lambda;
// New
this.currentfX=null;
this.lMAArrays=null;
this.currentRMS=-1;
this.currentRMSPure=-1;
this.jacobian=null; // invalidate
//System.out.println("Setting both lastImprovements(); to -1");
lMAData.resetRMSes(); // boith first and saved
lastImprovements[0]=-1.0;
lastImprovements[1]=-1.0;
if (this.numIterations < 0){
// this.currentfX=
lMAData.getFX(true); // try false too
return true;
}
......@@ -1684,25 +1613,25 @@ public class FactorConvKernel {
}
while (true) { // loop for the same series
boolean [] state=stepLevenbergMarquardtFirst(goal_rms_pure);
boolean [] state=stepLevenbergMarquardt(goal_rms_pure);
// state[0] - better, state[1] - finished
if (this.debugLevel>1) System.out.println(this.iterationStepNumber+": stepLevenbergMarquardtFirst()==>"+state[1]+":"+state[0]);
if (this.debugLevel>1) System.out.println(this.iterationStepNumber+": stepLevenbergMarquardt()==>"+state[1]+":"+state[0]);
// boolean cont=true;
// Make it success if this.currentRMS<this.firstRMS even if LMA failed to converge
if (state[1] && !state[0] && (this.firstRMS>this.currentRMS)) {
if (this.debugLevel>1) System.out.println("LMA failed to converge, but RMS improved from the initial value ("+this.currentRMS+" < "+this.firstRMS+")");
if (state[1] && !state[0] && (lMAData.getFirstRMSes()[0] > lMAData.getRMSes()[0])) {
if (this.debugLevel>1) System.out.println("LMA failed to converge, but RMS improved from the initial value ("+lMAData.getRMSes()[0]+" < "+lMAData.getFirstRMSes()[0]+")");
state[0]=true;
}
if (this.debugLevel>0){
if (state[1] && !state[0]){ // failure, show at debugLevel >0
System.out.println("LevenbergMarquardt(): failed step ="+this.iterationStepNumber+
", RMS="+IJ.d2s(this.currentRMS,8)+
" ("+IJ.d2s(this.firstRMS,8)+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime),3));
", RMS="+IJ.d2s(lMAData.getRMSes()[0], 8)+
" ("+IJ.d2s(lMAData.getFirstRMSes()[0], 8)+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime), 3));
} else if (this.debugLevel>1){ // success: show only if debugLevel > 1
System.out.println("==> LevenbergMarquardt(): before action step ="+this.iterationStepNumber+
", RMS="+IJ.d2s(this.currentRMS,8)+
" ("+IJ.d2s(this.firstRMS,8)+") "+
", RMS="+IJ.d2s(lMAData.getRMSes()[0], 8)+
" ("+IJ.d2s(lMAData.getFirstRMSes()[0], 8)+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3));
}
}
......@@ -1711,13 +1640,14 @@ public class FactorConvKernel {
if (this.debugLevel>1) {
System.out.println(
"stepLevenbergMarquardtAction() step="+this.iterationStepNumber+
", this.currentRMS="+this.currentRMS+
", this.currentRMSPure="+this.currentRMSPure+
", this.nextRMS="+this.nextRMS+
", this.nextRMSPure="+this.nextRMSPure+
", this.currentRMS="+lMAData.getSavedRMSes()[0]+
", this.currentRMSPure="+lMAData.getSavedRMSes()[1]+
", this.nextRMS="+lMAData.getRMSes()[0]+
", this.nextRMSPure="+lMAData.getRMSes()[1]+
" lambda="+this.lambda+" at "+IJ.d2s(0.000000001*(System.nanoTime()- startTime),3)+" sec");
}
if (this.nextRMS < this.currentRMS) { //improved
// if (this.nextRMS < this.currentRMS) { //improved
if (state[0]) { // improved
this.lambda *= this.lambdaStepDown;
this.currentRMS = this.nextRMS;
} else {
......@@ -1727,8 +1657,8 @@ public class FactorConvKernel {
if ((this.debugLevel>0) && ((this.debugLevel>2) || ((System.nanoTime()-this.startTime)>10000000000.0))){ // > 10 sec
// if ((this.debugLevel>0) && ((this.debugLevel>0) || ((System.nanoTime()-this.startTime)>10000000000.0))){ // > 10 sec
System.out.println("--> long wait: LevenbergMarquardt(): step = "+this.iterationStepNumber+
", RMS="+IJ.d2s(this.currentRMS,8)+
" ("+IJ.d2s(this.firstRMS,8)+") "+
", RMS="+IJ.d2s(lMAData.getRMSes()[0],8)+
" ("+IJ.d2s(lMAData.getFirstRMSes()[0],8)+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3));
}
if (state[1]) { // finished
......@@ -1737,53 +1667,22 @@ public class FactorConvKernel {
}
}
if (this.debugLevel>0) System.out.println("LevenbergMarquardt() finished in "+this.iterationStepNumber+
" steps, RMS="+this.currentRMS+
" ("+this.firstRMS+") "+
" steps, RMS="+lMAData.getRMSes()[0]+
" ("+lMAData.getFirstRMSes()[0]+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime),3));
return true; // all series done
}
private boolean [] stepLevenbergMarquardtFirst(double goal_rms_pure){
private boolean [] stepLevenbergMarquardt(double goal_rms_pure){
double [] deltas=null;
double [] rmses; // [0]: full rms, [1]:pure rms
// calculate this.currentfX, this.jacobian if needed
if (this.debugLevel>2) {
System.out.println("this.currentVector 2");
double [] dbg_vector = lMAData.getVector();
int [][] map_from_pars = lMAData.getMapFromPars();
for (int i= 0; i<dbg_vector.length;i++) if ((debugLevel > 3) || (map_from_pars[i][0]==1)){
System.out.println(i+": "+ dbg_vector[i]);
}
}
if (!lMAData.isValidLMAArrays()) {
lMAData.getFX(true); // try false too
System.out.println("1-stepLevenbergMarquardtFirst("+goal_rms_pure+")");
double [][] jac1=lMAData.getJacobian(
if (!lMAData.isValidLMAArrays()) { // First time and after improvement
lMAData.getFX(true); // Will calculate fX and rms (both composite and pure) if null (only when firs called) (try with false too?)
lMAData.getJacobian(
true, // boolean recalculate,
true); //boolean skip_disabled_asym)
if (debugLevel > 2){
double [] fX = lMAData.getFX(true); // try false too
double [][] jacobian = lMAData.getJacobian(false, true);
int [][] map_from_fx = lMAData.getMapFromFx();
int [][] map_from_pars = lMAData.getMapFromPars();
for (int i=0; i<fX.length; i++) if ((debugLevel > 3) || (map_from_fx[i][0] == 1)) {
System.out.println("fX["+i+"]="+fX[i]);
}/*
for (int n= 0; n<jacobian.length;n++) if ((debugLevel > 3) || (map_from_pars[n][0]==1)){ // only for asym_kernel
for (int i=0; i<fX.length; i++) if ((debugLevel > 2) || (map_from_fx[i][0] == 1)) {
System.out.println("1-jacobian["+n+"]["+i+"]="+jacobian[n][i]+" fX["+i+"]="+fX[i]);
}
}
*/
for (int n= 0; n<jacobian.length;n++) if ((debugLevel > 3) || (map_from_pars[n][0]==1)){ // only for asym_kernel
for (int i=0; i<fX.length; i++) if ((debugLevel > 2) || (map_from_fx[i][0] == 1)) {
System.out.println("1-jacobian["+n+"]["+i+"]="+jacobian[n][i]+" fX["+i+"]="+fX[i]+" jac="+jac1[n][i]);
}
}
}
lMAData.getJTByJ(true); // recalculate
lMAData.getJTByDiff(true); // recalculate
lMAData.getJTByJ(true); // force recalculate (it is null anyway)
lMAData.getJTByDiff(true); // force recalculate
if (debugLevel >2) {
double [][] jTByJ = lMAData.getJTByJ(false); // do not recalculate
double [] jTByDiff = lMAData.getJTByDiff(false); // do not recalculate
......@@ -1796,51 +1695,35 @@ public class FactorConvKernel {
System.out.println("jTByDiff["+n+"]="+jTByDiff[n]);
}
}
rmses = lMAData.getDiffByDiff();
this.currentRMSPure= Math.sqrt(rmses[1] / lMAData.getNumPurePoints());
this.currentRMS = Math.sqrt(rmses[0] / lMAData.getNumPoints());
if (this.debugLevel>1) {
System.out.println("initial RMS="+IJ.d2s(this.currentRMS,8)+
" ("+IJ.d2s(this.currentRMSPure,8)+")"+
". Calculating next Jacobian. Points:"+lMAData.getNumPoints()+"("+lMAData.getNumPurePoints()+")"+" Parameters:"+lMAData.getNumPars());
System.out.println("initial RMS="+IJ.d2s(lMAData.getRMSes()[0],8)+
" ("+IJ.d2s(lMAData.getRMSes()[1],8)+")"+
". Calculating next Jacobian. Points:"+lMAData.getNumPoints()+"("+lMAData.getNumPurePoints()+")"+
" Parameters:"+lMAData.getNumPars());
}
lMAData.save(); // save kernels (vector), fX and RMS values
} else { // LMA arrays already calculated
rmses = lMAData.getDiffByDiff();
this.currentRMSPure= Math.sqrt(rmses[1] / lMAData.getNumPurePoints());
this.currentRMS = Math.sqrt(rmses[0] / lMAData.getNumPoints());
} else { // LMA arrays already calculated after previous failure
if (debugLevel > 2){
System.out.println("existing data: this.currentRMS="+IJ.d2s(this.currentRMS,8)+
" ("+IJ.d2s(this.currentRMSPure,8)+")");
System.out.println("existing data: this.currentRMS="+IJ.d2s(lMAData.getRMSes()[0], 8)+
" ("+IJ.d2s(lMAData.getRMSes()[1], 8)+")");
}
}
if (this.firstRMS<0) {
this.firstRMS=this.currentRMS;
this.firstRMSPure=this.currentRMSPure;
}
lMAData.saveKernels(); // save to be able to roll back if failed
lMAData.saveLMAArrays(); // save to be able to roll back if failed
// calculate deltas
// deltas=solveLMA(this.lMAArrays, this.lambda, this.debugLevel);
// calculate deltas
deltas=lMAData.solveLMA(this.lambda, this.debugLevel);
// boolean matrixNonSingular=true;
if (deltas==null) {
if (this.debugLevel>2) {
if (this.debugLevel > 0) {
System.out.println("--- Singular matrix - failed to compute deltas ---");
}
deltas=new double[lMAData.getNumPars()];
for (int i=0;i<deltas.length;i++) deltas[i]=0.0;
boolean [] status={false, true}; // done / bad
return status; // done, bad
return status; // done, bad . No need to restore - nothing was changed
}
if (this.debugLevel > 2 ){
System.out.println("--- deltas ---"+" this.currentRMS="+this.currentRMS);
System.out.println("--- deltas ---"+" this.currentRMS="+lMAData.getRMSes()[0]);
int [][] map_from_pars = lMAData.getMapFromPars();
for (int i=0; i < deltas.length; i++) if ((debugLevel > 3) || (map_from_pars[i][0]==1)) {
System.out.println("deltas["+i+"]="+ deltas[i]);
......@@ -1849,7 +1732,6 @@ public class FactorConvKernel {
// apply deltas
lMAData.applyDeltas(deltas);
// this.nextVector=this.currentVector.clone();
if (this.debugLevel > 2) {
System.out.println("modified Vector");
double [] dbg_vector = lMAData.getVector();
......@@ -1861,75 +1743,66 @@ public class FactorConvKernel {
}
// calculate for new (modified vector)
lMAData.getFX(true); // try false too
lMAData.getJacobian(
true, // boolean recalculate,
true); //boolean skip_disabled_asym)
lMAData.getJTByJ(true); // recalculate
lMAData.getJTByDiff(true); // recalculate
rmses = lMAData.getDiffByDiff();
this.nextRMSPure= Math.sqrt(rmses[1] / lMAData.getNumPurePoints());
this.nextRMS = Math.sqrt(rmses[0] / lMAData.getNumPoints());
lMAData.getFX(true); // also calculates RMSes (try false too)
this.lastImprovements[1]=this.lastImprovements[0];
this.lastImprovements[0]=this.currentRMS-this.nextRMS;
this.lastImprovements[0]=lMAData.getSavedRMSes()[0] - lMAData.getRMSes()[0]; // this.currentRMS-this.nextRMS;
if (this.debugLevel>2) {
System.out.println("stepLMA this.currentRMS="+this.currentRMS+
", this.nextRMS="+this.nextRMS+
", delta="+(this.currentRMS-this.nextRMS));
System.out.println("stepLMA currentRMS="+lMAData.getSavedRMSes()[0]+
", nextRMS="+lMAData.getRMSes()[0]+
", delta="+(lMAData.getSavedRMSes()[0]-lMAData.getRMSes()[0]));
}
boolean [] status={this.nextRMS<=this.currentRMS, false};
boolean [] status={lMAData.getSavedRMSes()[0] > lMAData.getRMSes()[0], false};
// additional test if "worse" but the difference is too small, it was be caused by computation error, like here:
//stepLevenbergMarquardtAction() step=27, this.currentRMS=0.17068403807026408, this.nextRMS=0.1706840380702647
if (!status[0]) { // worse
if (this.nextRMS<(this.currentRMS+this.currentRMS*this.thresholdFinish*0.01)) {
this.nextRMS=this.currentRMS;
if (lMAData.getRMSes()[0] < (lMAData.getSavedRMSes()[0] * (1.0+this.thresholdFinish*0.01))) {
// this.nextRMS=this.currentRMS; // no need to modify ?
status[0]=true;
status[1]=true;
this.lastImprovements[0]=0.0;
if (this.debugLevel>1) {
System.out.println("New RMS error is larger than the old one, but the difference is too small to be trusted ");
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));
"stepLMA this.currentRMS="+lMAData.getSavedRMSes()[0]+
", this.currentRMSPure="+lMAData.getSavedRMSes()[1]+
", this.nextRMS="+lMAData.getRMSes()[0]+
", this.nextRMSPure="+lMAData.getRMSes()[1]+
", delta="+(lMAData.getSavedRMSes()[0] - lMAData.getRMSes()[0])+
", deltaPure="+(lMAData.getSavedRMSes()[1] - lMAData.getRMSes()[1]));
}
}
}
if (status[0]) { // improved
status[1]=(this.iterationStepNumber>this.numIterations) || ( // done
(this.lastImprovements[0]>=0.0) &&
(this.lastImprovements[0]<this.thresholdFinish*this.currentRMS) &&
(this.lastImprovements[0]<this.thresholdFinish*lMAData.getSavedRMSes()[0]) &&
(this.lastImprovements[1]>=0.0) &&
(this.lastImprovements[1]<this.thresholdFinish*this.currentRMS));
if (this.currentRMSPure < goal_rms_pure) {
(this.lastImprovements[1]<this.thresholdFinish*lMAData.getSavedRMSes()[0]));
if ( lMAData.getRMSes()[1] < 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));
"stepLMA this.currentRMS="+lMAData.getSavedRMSes()[0]+
", this.currentRMSPure="+lMAData.getSavedRMSes()[1]+
", this.nextRMS="+lMAData.getRMSes()[0]+
", this.nextRMSPure="+lMAData.getRMSes()[1]+
", delta="+(lMAData.getSavedRMSes()[0]-lMAData.getRMSes()[0])+
", deltaPure="+(lMAData.getSavedRMSes()[1]-lMAData.getRMSes()[1]));
}
}
lMAData.invalidateLMAArrays();
} else { // did not improve - roll back
lMAData.restoreKernels(); // roll back
lMAData.restoreLMAArrays(); // roll back
lMAData.restore(); // roll back: kernels, fX, RMSes
status[1]=(this.iterationStepNumber>this.numIterations) || // failed
((this.lambda*this.lambdaStepUp)>this.maxLambda);
}
///this.currentRMS
//TODO: add other failures leading to result failure?
if (this.debugLevel>2) {
System.out.println("stepLevenbergMarquardtFirst()=>"+status[0]+","+status[1]);
System.out.println("stepLevenbergMarquardt()=>"+status[0]+","+status[1]);
}
return status;
}
......@@ -2277,7 +2150,7 @@ public class FactorConvKernel {
///this.currentRMS
//TODO: add other failures leading to result failure?
if (this.debugLevel>2) {
System.out.println("stepLevenbergMarquardtFirst()=>"+status[0]+","+status[1]);
System.out.println("stepLevenbergMarquardt()=>"+status[0]+","+status[1]);
}
return status;
}
......
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