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Commit 03feb56c authored by Andrey Filippov's avatar Andrey Filippov
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more debugging of the factorization, experimenting

parent 19b8b05a
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src/main/java/EyesisCorrectionParameters.java
View file @ 03feb56c
......@@ -1656,7 +1656,9 @@ public class EyesisCorrectionParameters {
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 = 5; // "compactness" does not apply to pixels with |x|<=asym_tax_free and |y| <= asym_tax_free
public int asym_tax_free = 5; // "compactness" does not apply to pixels with |x|<=asym_tax_free and |y| <= asym_tax_free
public int seed_size = 8; // number of initial cells in asym_kernel - should be 4*b + 1 (X around center cell) or 4*n + 0 (X around between cells)
public double asym_random; // initialize asym_kernel with random numbers
public double dbg_x =0;
public double dbg_y =0;
public double dbg_x1 =0;
......@@ -1665,7 +1667,15 @@ public class EyesisCorrectionParameters {
public String dbg_mask = ".........:::::::::.........:::::::::......*..:::::*:::.........:::::::::.........";
public int dbg_mode = 1; // 0 - old LMA, 1 - new LMA
public DCTParameters(int dct_size, int asym_size, int asym_pixels, int asym_distance, int dct_window, double compactness, int asym_tax_free) {
public DCTParameters(
int dct_size,
int asym_size,
int asym_pixels,
int asym_distance,
int dct_window,
double compactness,
int asym_tax_free,
int seed_size) {
this.dct_size = dct_size;
this.asym_size = asym_size;
this.asym_pixels = asym_pixels;
......@@ -1673,6 +1683,7 @@ public class EyesisCorrectionParameters {
this.dct_window = dct_window;
this.compactness = compactness;
this.asym_tax_free = asym_tax_free;
this.seed_size = seed_size;
}
public void setProperties(String prefix,Properties properties){
properties.setProperty(prefix+"dct_size",this.dct_size+"");
......@@ -1683,6 +1694,9 @@ public class EyesisCorrectionParameters {
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+"seed_size", this.seed_size+"");
properties.setProperty(prefix+"asym_random", this.asym_random+"");
properties.setProperty(prefix+"LMA_steps", this.LMA_steps+"");
properties.setProperty(prefix+"dbg_x", this.dbg_x+"");
properties.setProperty(prefix+"dbg_y", this.dbg_y+"");
......@@ -1702,6 +1716,8 @@ public class EyesisCorrectionParameters {
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+"seed_size")!=null) this.seed_size=Integer.parseInt(properties.getProperty(prefix+"seed_size"));
if (properties.getProperty(prefix+"asym_random")!=null) this.asym_random=Double.parseDouble(properties.getProperty(prefix+"asym_random"));
if (properties.getProperty(prefix+"LMA_steps")!=null) this.LMA_steps=Integer.parseInt(properties.getProperty(prefix+"LMA_steps"));
if (properties.getProperty(prefix+"dbg_x")!=null) this.dbg_x=Double.parseDouble(properties.getProperty(prefix+"dbg_x"));
if (properties.getProperty(prefix+"dbg_y")!=null) this.dbg_y=Double.parseDouble(properties.getProperty(prefix+"dbg_y"));
......@@ -1722,7 +1738,8 @@ public class EyesisCorrectionParameters {
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("Start asym_kernel with this number of pixels (0 - single, 4n+0 (X between cells), 4*n+1 - x around center cell", this.seed_size, 0); //0..2
gd.addNumericField("Initialize asym_kernel with random numbers (amplitude)", this.asym_random, 2); //0..2
gd.addNumericField("dbg_x", this.dbg_x, 2); //0..2
gd.addNumericField("dbg_y", this.dbg_y, 2); //0..2
gd.addNumericField("dbg_x1", this.dbg_x1, 2); //0..2
......@@ -1743,6 +1760,8 @@ public class EyesisCorrectionParameters {
this.compactness = gd.getNextNumber();
this.fact_precision = gd.getNextNumber();
this.asym_tax_free = (int) gd.getNextNumber();
this.seed_size = (int) gd.getNextNumber();
this.asym_random= gd.getNextNumber();
this.dbg_x= gd.getNextNumber();
this.dbg_y= gd.getNextNumber();
this.dbg_x1= gd.getNextNumber();
......
src/main/java/Eyesis_Correction.java
View file @ 03feb56c
......@@ -90,8 +90,9 @@ private Panel panel1,panel2,panel3,panel4,panel5,panel5a, panel6,panel7,panelPos
10, // asym_pixels = 10; // maximal number of non-zero pixels in direct convolution kernel
2, // asym_distance = 2; // how far to try a new asym kernel pixel from existing ones
1, // dct_window
1.0,// double compactness
5 // asym_tax_free);
1.0,// compactness
5, // asym_tax_free,
8 // seed_size
);
public static EyesisDCT EYESIS_DCT = null;
......@@ -2865,6 +2866,20 @@ private Panel panel1,panel2,panel3,panel4,panel5,panel5a, panel6,panel7,panelPos
if (blurSigma>0) gb=new DoubleGaussianBlur();
if (blurSigma>0) gb.blurDouble(target_kernel, target_kernel_size, target_kernel_size, blurSigma, blurSigma, 0.01);
// SDFA_INSTANCE.showArrays(target_kernel, target_kernel_size, target_kernel_size, "target_kernel");
if (
(DCT_PARAMETERS.dbg_x == 0.0) &&
(DCT_PARAMETERS.dbg_x1 == 0.0) &&
(DCT_PARAMETERS.dbg_y == 0.0) &&
(DCT_PARAMETERS.dbg_y1 == 0.0)) {
System.out.println ("Making sure target kernel is center symmetrical (copying top-left quater)");
for (int ii = 0; ii<target_kernel_size; ii++) for (int jj = 0; jj<target_kernel_size; jj++){
int ii1 = (ii >= DCT_PARAMETERS.dct_size)? (2 * DCT_PARAMETERS.dct_size -ii -2):ii;
int jj1 = (jj >= DCT_PARAMETERS.dct_size)? (2 * DCT_PARAMETERS.dct_size -jj -2):jj;
target_kernel[target_kernel_size*ii+jj] = target_kernel[target_kernel_size*ii1+jj1];
}
}
int target_expanded_size = 2*DCT_PARAMETERS.dct_size + DCT_PARAMETERS.asym_size -2;
double [] target_expanded = new double [target_expanded_size * target_expanded_size];
......@@ -2901,12 +2916,18 @@ private Panel panel1,panel2,panel3,panel4,panel5,panel5a, panel6,panel7,panelPos
DCT_PARAMETERS.asym_size,
DCT_PARAMETERS.dct_size,
DCT_PARAMETERS.fact_precision,
mask);
mask,
DCT_PARAMETERS.seed_size,
DCT_PARAMETERS.asym_random);
System.out.println("factorConvKernel.calcKernels() returned: >>> "+result+ " <<<");
double [] sym_kernel = factorConvKernel.getSymKernel();
double [] asym_kernel = factorConvKernel.getAsymKernel();
double [] convolved = factorConvKernel.getConvolved();
double [][] compare_kernels = {target_expanded, convolved};
double [] diff100 = new double [convolved.length];
for (int ii=0;ii<convolved.length;ii++) diff100[ii]=100.0*(target_expanded[ii]-convolved[ii]);
double [][] compare_kernels = {target_expanded, convolved, diff100};
System.out.println("DCT_PARAMETERS.dct_size="+DCT_PARAMETERS.dct_size+" DCT_PARAMETERS.asym_size="+DCT_PARAMETERS.asym_size);
System.out.println("sym_kernel.length="+ sym_kernel.length);
System.out.println("asym_kernel.length="+asym_kernel.length);
......@@ -2919,7 +2940,7 @@ private Panel panel1,panel2,panel3,panel4,panel5,panel5a, panel6,panel7,panelPos
} else if (label.equals("Min Kernel Factorization")){
DEBUG_LEVEL=MASTER_DEBUG_LEVEL;
if (!DCT_PARAMETERS.showDialog()) return;
FactorConvKernel factorConvKernel = new FactorConvKernel();
FactorConvKernel factorConvKernel = new FactorConvKernel(DCT_PARAMETERS.dbg_mode == 1);
factorConvKernel.setDebugLevel(DEBUG_LEVEL);
factorConvKernel.numIterations = DCT_PARAMETERS.LMA_steps;
factorConvKernel.setAsymCompactness(
......@@ -2967,7 +2988,8 @@ private Panel panel1,panel2,panel3,panel4,panel5,panel5a, panel6,panel7,panelPos
DCT_PARAMETERS.dct_size,
DCT_PARAMETERS.fact_precision,
DCT_PARAMETERS.asym_pixels,
DCT_PARAMETERS.asym_distance);
DCT_PARAMETERS.asym_distance,
DCT_PARAMETERS.seed_size);
/*
public int calcKernels(
double []target_kernel,
......
src/main/java/FactorConvKernel.java
View file @ 03feb56c
......@@ -24,6 +24,7 @@
**
*/
import java.util.ArrayList;
import java.util.Random;
import Jama.LUDecomposition;
import Jama.Matrix;
......@@ -60,7 +61,8 @@ public class FactorConvKernel {
public double lambdaStepUp= 8.0; // multiply lambda by this if result is worse
public double lambdaStepDown= 0.5; // multiply lambda by this if result is better
public double thresholdFinish=0.001; // (copied from series) stop iterations if 2 last steps had less improvement (but not worsening )
// public double thresholdFinish=0.001; // (copied from series) stop iterations if 2 last steps had less improvement (but not worsening )
public double thresholdFinish=0.00001; // (copied from series) stop iterations if 2 last steps had less improvement (but not worsening )
public int numIterations= 100; // maximal number of iterations
public double maxLambda= 1000.0; // max lambda to fail
public boolean stopOnFailure = true;
......@@ -69,6 +71,8 @@ public class FactorConvKernel {
public double sym_kernel_scale =1.0;// to scale sym kernel to match original one
public int center_i0;
public int center_j0;
public double center_y;
public double center_x;
public double lambda = init_lambda;
public double currentRMS ;
......@@ -93,6 +97,7 @@ public class FactorConvKernel {
public double [] lastImprovements= {-1.0,-1.0}; // {last improvement, previous improvement}. If both >0 and < thresholdFinish - done
public double goal_rms_pure;
public LMAData lMAData = null;
public int numLMARuns = 0;
public class LMAArrays {
public double [][] jTByJ= null; // jacobian multiplied by Jacobian transposed
......@@ -108,8 +113,6 @@ public class FactorConvKernel {
public class LMAData{
public int sym_radius= 0;
public int asym_size= 0;
// public double [] par_vector; generqate on demand
// public boolean [] par_mask;
private double [][] kernels = {null,null}; // 0 - sym kernel (including 0 [(sym_radius*2-1)*(sym_radius*2-1)]), 1 - asym kernel
private double [][] savedKernels = null;
private boolean [][] kernel_masks = {null,null};
......@@ -145,17 +148,44 @@ public class FactorConvKernel {
public void initLMAData( int debugLevel){
this.debugLevel = debugLevel;
}
public void invalidate_maps_pars(){
map_from_pars = null; // will need to be re-generated
map_to_pars = null;
// invalidate_maps_fx(); // asym_kernel parameters appear in map_*_fx too
}
public void invalidate_maps_fx(){
map_from_fx = null; // will need to be re-generated
map_to_fx = null; // will need to be re-generated
}
public void setSymKernel (double [] sym_kernel){ // does not set mask! Use ( , null) to set default one
kernels[0] = sym_kernel.clone();
sym_radius = (int) Math.round(Math.sqrt(sym_kernel.length));
if ((kernel_masks[0]==null) || (kernel_masks[0].length != kernels[0].length)){
boolean hasNaN = false;
for (int i = 0; i < sym_kernel.length; i++) {
hasNaN = Double.isNaN(kernels[0][i]);
if (hasNaN) break;
}
if ((kernel_masks[0]==null) || (kernel_masks[0].length != kernels[0].length) || hasNaN){
kernel_masks[0] = new boolean [kernels[0].length];
kernel_masks[0][0] = false; // do not adjust center element
for (int i=1;i< kernel_masks[0].length;i++) kernel_masks[0][i] = true;
map_from_pars = null; // will need to be re-generated
map_to_pars = null;
for (int i=1;i< kernel_masks[0].length;i++) kernel_masks[0][i] = hasNaN? (!Double.isNaN(kernels[0][i])): true;
invalidate_maps_pars();
invalidate_maps_fx();
}
if (hasNaN){
for (int i=0; i< kernels[0].length; i++) if (Double.isNaN(kernels[0][i])) kernels[0][i] = 0.0;
}
}
public void updateSymKernel (double [] sym_kernel){ // just change values (and reset fX)
kernels[0] = sym_kernel.clone();
fX=null;
}
public void updateAsymKernel (double [] asym_kernel){ // just change values (and reset fX)
kernels[1] = asym_kernel.clone();
fX=null;
}
public void setSymKernel (double [] sym_kernel, // if null - set mask only
......@@ -163,10 +193,11 @@ public class FactorConvKernel {
{
if (mask == null) kernel_masks[0] = null; // setSymKernel() will generate default
if (sym_kernel !=null) setSymKernel(sym_kernel);
if (mask != null) kernel_masks[0] = mask.clone();
map_from_pars = null; // will need to be re-generated
map_to_pars = null;
if (mask != null) {
kernel_masks[0] = mask.clone();
invalidate_maps_pars();
invalidate_maps_fx();
}
}
public double[] getSymKernel(){
......@@ -174,13 +205,12 @@ public class FactorConvKernel {
}
public double[] getSymKernel(double scale){
if (scale == 1.0) return kernels[0];
else return getScaledKernel(0,scale);
return getScaledKernel(0,scale);
}
public double[] getScaledKernel(int kernType, double scale){
double [] kernel = new double [kernels[kernType].length];
for (int i=0; i< kernel.length; i++) kernel[i] = scale*kernels[kernType][i];
for (int i=0; i< kernel.length; i++) kernel[i] = Double.isNaN(kernels[kernType][i])? 0.0: scale*kernels[kernType][i];
return kernel;
}
......@@ -195,15 +225,21 @@ public class FactorConvKernel {
System.out.println("setAsymKernel(): kernel_masks[1] is "+((kernel_masks[1]==null)?"":"not ")+"null");
if (kernel_masks[1]!=null) System.out.println("kernel_masks[1].length= "+kernel_masks[1].length);
}
if ((kernel_masks[1]==null) || (kernel_masks[1].length != kernels[1].length)){
boolean hasNaN = false;
for (int i = 0; i < asym_kernel.length; i++) {
hasNaN = Double.isNaN(kernels[1][i]);
if (hasNaN) break;
}
if ((kernel_masks[1]==null) || (kernel_masks[1].length != kernels[1].length) || hasNaN){
kernel_masks[1] = new boolean [kernels[1].length];
for (int i=0;i< kernel_masks[1].length;i++) kernel_masks[1][i] = true;
map_from_pars = null; // will need to be re-generated
map_to_pars = null;
map_from_fx = null; // will need to be re-generated
map_to_fx = null; // will need to be re-generated
for (int i=0;i< kernel_masks[1].length;i++) kernel_masks[1][i] = hasNaN? (!Double.isNaN(kernels[1][i])): true;
invalidate_maps_pars();
invalidate_maps_fx();
}
if (hasNaN){
for (int i=0; i< kernels[1].length; i++) if (Double.isNaN(kernels[1][i])) kernels[1][i] = 0.0;
}
if ((asym_weights == null) || (asym_weights.length != kernels[1].length)){
asym_weights = new double[kernels[1].length];
for (int i=0; i<asym_weights.length; i++){
......@@ -218,10 +254,11 @@ public class FactorConvKernel {
{
if (mask == null) kernel_masks[1] = null; // setSymKernel() will generate default
if (asym_kernel !=null) setAsymKernel(asym_kernel);
if (mask != null) kernel_masks[1] = mask.clone();
map_from_pars = null; // will need to be re-generated
map_to_pars = null;
if (mask != null){
kernel_masks[1] = mask.clone();
invalidate_maps_fx();
}
invalidate_maps_pars();
}
public void updateAsymKernelMask (boolean [] mask) // new mask, should not be null
......@@ -230,14 +267,13 @@ public class FactorConvKernel {
if (!mask[i] || !kernel_masks[1][i]) kernels[1][i] = 0.0;
}
kernel_masks[1] = mask.clone();
map_from_pars = null; // will need to be re-generated
map_to_pars = null;
invalidate_maps_pars();
invalidate_maps_fx();
if (debugLevel>1) System.out.println("updateAsymKernelMask(): invalidated map_from_pars and map_to_pars");
}
public double[] getAsymKernel(double scale){
if (scale == 1.0) return kernels[1];
else return getScaledKernel(1,scale);
return getScaledKernel(1,scale);
}
public double[] getAsymKernel(){
......@@ -252,8 +288,7 @@ public class FactorConvKernel {
this.target_kernel = target_kernel.clone();
fx_mask = new boolean[this.target_kernel.length];
for (int i= 0;i< fx_mask.length;i++) fx_mask[i] = true;
map_from_fx = null; // will need to be re-generated
map_to_fx = null; // will need to be re-generated
invalidate_maps_fx();
}
public double [] getTarget(){
......@@ -262,8 +297,7 @@ public class FactorConvKernel {
public void setTargetMask(boolean [] mask){
fx_mask=mask.clone();
map_from_fx = null; // will need to be re-generated
map_to_fx = null; // will need to be re-generated
invalidate_maps_fx();
}
public boolean[] getTargetMask(){
return fx_mask;
......@@ -275,6 +309,7 @@ public class FactorConvKernel {
public void rebuildMapsPars(boolean force)
{
if (force || (map_from_pars == null)){
par_vector = null; // invalidate
int numPars=0;
for (int n = 0; n < kernel_masks.length; n++){
......@@ -762,34 +797,89 @@ public class FactorConvKernel {
this.sym_radius = sym_radius;
}
public double [] generateAsymWeights(
int asym_size,
double scale,
double xc,
double yc,
int taxfree){
double [] weight = new double [asym_size*asym_size];
int ixc = (int) Math.round(xc);
int iyc = (int) Math.round(yc);
for (int i=0; i<asym_size; i++){
for (int j=0; j<asym_size; j++) {
double r2 = (i-yc)*(i-yc)+(j-xc)*(j-xc);
if ( (i - iyc <= taxfree) &&
(j - ixc <= taxfree) &&
(iyc - i <= taxfree) &&
(ixc - j <= taxfree)) r2 = 0.0;
weight [i*asym_size + j] = r2 * scale;
}
}
return weight;
}
public boolean calcKernels(
double []target_kernel,
int asym_size,
int sym_radius,
double fact_precision,
boolean [] mask){
boolean [] mask,
int seed_size,
double asym_random){
this.asym_size = asym_size;
this.sym_radius = sym_radius;
this.target_kernel = target_kernel;
this.startTime=System.nanoTime();
if (new_mode) {
initLevenbergMarquardt(fact_precision);
lMAData.setAsymKernel (
null, // double [] asym_kernel, // if null - set mask only
mask);
if (this.debugLevel>2) {
double [][] kernels = {lMAData.getSymKernel(),lMAData.getAsymKernel()};
System.out.println("calcKernels(): kernels data:");
for (int n=0;n<kernels.length;n++) for (int i=0;i<kernels[n].length;i++){
System.out.println(n+"/"+i+": "+ kernels[n][i]);
}
}
return levenbergMarquardt();
if (new_mode) {
initLevenbergMarquardt(fact_precision, seed_size, asym_random);
if (mask != null) {
// lMAData.setAsymKernel (
// null, // double [] asym_kernel, // if null - set mask only
// mask);
lMAData.updateAsymKernelMask(mask); // this will zero out asym_kernel elements that are masked out
}
if (debugLevel>0){
if (mask == null){
System.out.println("mask is null, retrieving from the kernels");
mask = lMAData.getAsymKernelMask();
}
System.out.println("mask.length="+mask.length);
System.out.println("asym mask: ");
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((mask[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
double [] asym_weights = generateAsymWeights(
asym_size,
this.compactness_weight * this.sym_kernel_scale, // double scale,
this.center_j0, //double xc,
this.center_i0, // double yc,
this.asym_tax_free); // int taxfree);
lMAData.setAsymWeights (asym_weights);
if (this.debugLevel>2) {
double [][] kernels = {lMAData.getSymKernel(),lMAData.getAsymKernel()};
System.out.println("calcKernels(): kernels data:");
for (int n=0;n<kernels.length;n++) for (int i=0;i<kernels[n].length;i++){
System.out.println(n+"/"+i+": "+ kernels[n][i]);
}
}
// first - freeze sym_kernel, then unfreeze
boolean [] sym_mask = lMAData.getSymKernelMask();
boolean [] sym_mask_frozen =new boolean[sym_mask.length];
for (int i = 0; i<sym_mask_frozen.length; i++) sym_mask_frozen[i] = false;
lMAData.setSymKernel(null, sym_mask_frozen);
levenbergMarquardt();
lMAData.setSymKernel(null, sym_mask);
return levenbergMarquardt();
} else{
initLevenbergMarquardt_old(fact_precision);
initLevenbergMarquardt_old(fact_precision, seed_size, asym_random);
if (mask != null){
if (this.debugLevel>2) {
System.out.println("calcKernels(): this.currentVector 0");
......@@ -799,7 +889,10 @@ public class FactorConvKernel {
}
int asym_start= sym_radius * sym_radius - 1;
for (int i = 0; i<mask.length; i++) if (!mask[i]) this.currentVector[asym_start+i] = Double.NaN;
for (int i = 0; i<mask.length; i++) {
if (Double.isNaN(this.currentVector[asym_start+i])) this.currentVector[asym_start+i] = 0.0; // replace all NaN with 0.0;
if (!mask[i]) this.currentVector[asym_start+i] = Double.NaN;
}
if (debugLevel>0){
System.out.println("mask.length="+mask.length+" asym_start="+asym_start+" this.currentVector.length="+this.currentVector.length);
System.out.println("asym mask: ");
......@@ -823,12 +916,247 @@ public class FactorConvKernel {
}
public int calcKernels(
double []target_kernel,
int asym_size,
int sym_radius,
double fact_precision,
int asym_pixels, // maximal number of non-zero pixels in asymmmetrical kernel
int asym_distance, // how far to seed a new pixel
int seed_size){
if (!new_mode) return calcKernels_old(
target_kernel,
asym_size,
sym_radius,
fact_precision,
asym_pixels, // maximal number of non-zero pixels in asymmmetrical kernel
asym_distance, // how far to seed a new pixel
seed_size);
this.asym_size = asym_size;
this.sym_radius = sym_radius;
this.target_kernel = target_kernel;
this.asym_pixels = asym_pixels;
this.asym_distance = asym_distance;
double s = 0.0;
for (int i = 0; i<target_kernel.length; i++){
s+= target_kernel[i]*target_kernel[i];
}
this.goal_rms_pure = Math.sqrt(s/target_kernel.length)*fact_precision;
this.target_rms = Math.sqrt(s/target_kernel.length);
double [] RMSes = null;
// double [] bestRms = new double [asym_pixels];
// int [] enPixels = new int [asym_pixels];
this.startTime=System.nanoTime(); // need local?
int numWeakest = 0;
int numAny=0;
initLevenbergMarquardt(fact_precision, seed_size,0.0);
if (debugLevel>0){
boolean [] mask = lMAData.getAsymKernelMask();
System.out.println("mask.length="+mask.length);
System.out.println("asym mask: ");
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((mask[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
double [] asym_weights = generateAsymWeights(
asym_size,
this.compactness_weight * this.sym_kernel_scale, // double scale,
this.center_j0, //double xc,
this.center_i0, // double yc,
this.asym_tax_free); // int taxfree);
lMAData.setAsymWeights (asym_weights);
if (!levenbergMarquardt()){
boolean [] asym_mask = lMAData.getAsymKernelMask();
int numAsym = 0;
for (int i = 0; i < asym_mask.length; i++) if (asym_mask[i]) numAsym++;
System.out.println("===== calcKernels(): failed to run first LMA ======");
return numAsym;
}
RMSes = lMAData.getRMSes();
// Add points until reached asym_pixels
int numAsym = 0;
while (true){
boolean [] asym_mask = lMAData.getAsymKernelMask();
numAsym = 0;
for (int i = 0; i < asym_mask.length; i++) if (asym_mask[i]) numAsym++;
if (numAsym >= asym_pixels) break;
if ((RMSes!=null) && (RMSes[1] < this.goal_rms_pure)){
if (debugLevel>0){
System.out.println("calcKernels(): reached goal: "+RMSes[1]+" < "+this.goal_rms_pure);
}
break;
}
RMSes =addCell(
asym_distance, // how far to seed a new pixel (hor/vert
RMSes[0], // if Double.NaN - will not compare against it, return the best one
-1); // no skip points
if (RMSes == null) {
if (debugLevel>0) {
System.out.println("calcKernels(): failed to add cell");
}
return numAsym;
}
}
if (debugLevel>0){
System.out.println(
"Finished adding cells, number of LMA runs = "+getLMARuns()+
", RMS = "+RMSes[0]+
", RMSPure = "+RMSes[1]+
", relRMSPure = "+(RMSes[1]/this.target_rms)+
", spent "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3)+" sec");
}
if (debugLevel>0){
boolean [] am = lMAData.getAsymKernelMask();
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((am[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
if ((RMSes!=null) && (RMSes[1] < this.goal_rms_pure)){
if (debugLevel > 0){
System.out.println("calcKernels(): reached goal: "+RMSes[1]+" < "+this.goal_rms_pure);
}
} else {
// Replace weakest
while (true){
double [] newRMSes = replaceWeakest(
asym_distance, // how far to seed a new pixel (hor/vert
RMSes[0]); // double was_rms
if (newRMSes == null){
break;
}
RMSes = newRMSes;
numWeakest++;
if (debugLevel>0){
System.out.println(
"Replaced weakiest cell ("+numWeakest+"), number of LMA runs = "+getLMARuns()+
", RMS = "+RMSes[0]+
", RMSPure = "+RMSes[1]+
", relRMSPure = "+(RMSes[1]/this.target_rms)+
", spent "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3)+" sec");
}
if (debugLevel>0){
boolean [] am = lMAData.getAsymKernelMask();
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((am[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
if ((RMSes!=null) && (RMSes[1] < this.goal_rms_pure)){
if (debugLevel > 0){
System.out.println("calcKernels(): reached goal: "+RMSes[1]+" < "+this.goal_rms_pure);
}
break;
}
}
if (debugLevel>0){
System.out.println(
"Finished replacing weakiest cells, number of LMA runs = "+getLMARuns()+ ", number of weakest replaced = "+numWeakest+
", RMS = "+RMSes[0]+
", RMSPure = "+RMSes[1]+
", relRMSPure = "+(RMSes[1]/this.target_rms)+
", spent "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3)+" sec");
}
if (debugLevel>0){
boolean [] am = lMAData.getAsymKernelMask();
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((am[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
/*
if ((RMSes!=null) && (RMSes[1] < this.goal_rms_pure)){
if (debugLevel>0){
System.out.println("calcKernels(): reached goal: "+RMSes[1]+" < "+this.goal_rms_pure);
}
} else {
// replace any cell
while (true){
double [] newRMSes = replaceAny(
asym_distance, // how far to seed a new pixel (hor/vert
RMSes[0]); // double was_rms
if (newRMSes == null){
break;
}
RMSes = newRMSes;
numAny++;
if (debugLevel>0){
System.out.println(
"Replaced a cell (not the weakest), total number "+numAny+", number of LMA runs = "+getLMARuns()+
", RMS = "+RMSes[0]+
", RMSPure = "+RMSes[1]+
", spent "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3)+" sec");
}
if (debugLevel>0){
boolean [] am = lMAData.getAsymKernelMask();
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((am[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
if ((RMSes!=null) && (RMSes[1] < this.goal_rms_pure)){
if (debugLevel > 0){
System.out.println("calcKernels(): reached goal: "+RMSes[1]+" < "+this.goal_rms_pure);
}
break;
}
}
}
*/
}
if (debugLevel>0){
System.out.println(
"Finished replacing (any) cells, number of LMA runs = "+getLMARuns()+", number of cells replaced - "+numAny+
", RMS = "+RMSes[0]+
", RMSPure = "+RMSes[1]+
", relRMSPure = "+(RMSes[1]/this.target_rms)+
", spent "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3)+" sec");
}
if (debugLevel>0){
boolean [] am = lMAData.getAsymKernelMask();
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((am[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
return numAsym;
}
public int calcKernels_old(
double []target_kernel,
int asym_size,
int sym_radius,
double fact_precision,
int asym_pixels, // maximal number of non-zero pixels in asymmmetrical kernel
int asym_distance){ // how far to seed a new pixel
int asym_distance, // how far to seed a new pixel
int seed_size){
this.asym_size = asym_size;
this.sym_radius = sym_radius;
this.target_kernel = target_kernel;
......@@ -848,7 +1176,7 @@ public class FactorConvKernel {
this.startTime=System.nanoTime();
/// double[] initialVector = setInitialVector(target_kernel, null); // should be (asym_size + 2*sym_radius-1)**2
double [][]kernels = setInitialVector(target_kernel, null); // should be (asym_size + 2*sym_radius-1)**2
double [][]kernels = setInitialVector(target_kernel, seed_size, 0.0); // should be (asym_size + 2*sym_radius-1)**2
double[] initialVector = setVectorFromKernels(kernels[0], kernels[1]);
enPixels[0] = center_i0*asym_size+center_j0;
......@@ -878,11 +1206,15 @@ public class FactorConvKernel {
for (int i = 0; i < mask.length; i++) mask[i] = false;
for (int i = 0; i < numPixels; i++) mask[enPixels[i]] = true;
boolean diagonal = false;
if (asym_distance < 0){
asym_distance = -asym_distance;
diagonal = true;
}
boolean[] mask0 = mask.clone();
for (int n = 0; n < asym_distance; n++){
boolean[] mask1 = mask0.clone();
if (asym_size < 0){
asym_size = -asym_size;
if (diagonal){
for (int i = 0; i <asym_size; i++){
for (int j = 1; j<asym_size; j++){
mask1[asym_size*i + j] |= mask0[asym_size*i + j-1];
......@@ -1031,17 +1363,219 @@ public class FactorConvKernel {
}
// LMAData contains current kernels/masks (but not RMSes!)
// if improved - will leave with the new kernels/masks and return RMSes, if failed - return null and restore
public double [] addCell(
int asym_distance, // how far to seed a new pixel (hor/vert
double was_rms, // if Double.NaN - will not compare against it, return the best one
int skip_index // to prevent just removed to appear again
){
// save state
double [][] saved_kernels = {lMAData.getSymKernel().clone(),lMAData.getAsymKernel().clone()};
boolean [][] saved_kernel_masks = {lMAData.getSymKernelMask().clone(),lMAData.getAsymKernelMask().clone()};
boolean [] sym_mask_frozen =new boolean[saved_kernel_masks[0].length];
for (int i = 0; i<sym_mask_frozen.length; i++) sym_mask_frozen[i] = false;
// double [] saved_RMSes = lMAData.getRMSes();
// create list of neighbors
int [] candidates = expandMask(
saved_kernel_masks[1],
asym_distance);
double [][] results = new double[candidates.length][];
double [][][] result_kernels = new double[candidates.length][][];
for (int n = 0; n < candidates.length; n++) if (candidates[n] != skip_index){
lMAData.setSymKernel (saved_kernels[0], saved_kernel_masks[0]);
lMAData.setAsymKernel (saved_kernels[1], saved_kernel_masks[1]);
boolean [] asym_mask = saved_kernel_masks[1].clone();
asym_mask[candidates[n]] = true;
lMAData.updateAsymKernelMask(asym_mask); // will set new asym_kernel values to 0;
// First - just adjust asym
if (debugLevel>1) System.out.println("Running asym-only LMA");
lMAData.setSymKernel(null, sym_mask_frozen);
levenbergMarquardt();
if (debugLevel>1) System.out.println("Running full LMA");
lMAData.setSymKernel(null, saved_kernel_masks[0]);
if ( levenbergMarquardt()) {
results[n] = lMAData.getRMSes().clone();
result_kernels[n] = new double[2][];
result_kernels[n][0] = lMAData.getSymKernel().clone();
result_kernels[n][1] = lMAData.getAsymKernel().clone();
} else {
results[n]= null;
result_kernels[n] = null;
}
}
int best_index=-1;
for (int n = 0; n<results.length; n++){
if ((results[n] !=null) && (Double.isNaN(was_rms) || (results[n][0] < was_rms)) && ((best_index < 0) || (results[n][0] < results[best_index][0]))){
best_index = n;
}
}
if (best_index <0) { // failed
lMAData.setSymKernel (saved_kernels[0], saved_kernel_masks[0]);
lMAData.setAsymKernel (saved_kernels[1], saved_kernel_masks[1]);
return null;
} else {
boolean [] asym_mask = saved_kernel_masks[1].clone();
asym_mask[candidates[best_index]] = true;
lMAData.setSymKernel (result_kernels[best_index][0], saved_kernel_masks[0]);
lMAData.setAsymKernel (result_kernels[best_index][1], asym_mask);
return results[best_index];
}
}
// LMAData contains current kernels/masks (but not RMSes!)
// if improved - will leave with the new kernels/masks and return RMSes, if failed - return null and restore
// Try to replace the cell that has the lowest absolute value with the different one
public double [] replaceWeakest(
int asym_distance, // how far to seed a new pixel (hor/vert
double was_rms
){
// save state
double [][] saved_kernels = {lMAData.getSymKernel().clone(),lMAData.getAsymKernel().clone()};
boolean [][] saved_kernel_masks = {lMAData.getSymKernelMask().clone(),lMAData.getAsymKernelMask().clone()};
int weakestIndex =-1;
for (int i = 0; i < saved_kernel_masks[1].length; i++) if (saved_kernel_masks[1][i]){
if ((weakestIndex < 0) || (Math.abs(saved_kernels[1][i]) < Math.abs(saved_kernels[1][weakestIndex]))) {
weakestIndex = i;
}
}
if (weakestIndex < 0) return null ; //should not happen
boolean [] asym_mask = saved_kernel_masks[1].clone();
asym_mask[weakestIndex] = false;
lMAData.setAsymKernel (null, asym_mask); // set mask only
double [] RMSes = addCell( asym_distance, Double.NaN, weakestIndex); // if Double.NaN - will not compare against it, return the best one
// double [] RMSes = addCell( asym_distance, Double.NaN, -1); // if Double.NaN - will not compare against it, return the best one
if (RMSes == null){
System.out.println("replaceWeakest(): Failed to find any replacements at all");
} else if (RMSes[0] > was_rms){
if (this.debugLevel>1){
System.out.println("replaceWeakest(): Failed to find a better replacemnet ("+ RMSes[0]+">"+was_rms+")");
}
RMSes = null;
}
if (RMSes == null) { // failed in any way - restore state
lMAData.setSymKernel (saved_kernels[0], saved_kernel_masks[0]);
lMAData.setAsymKernel (saved_kernels[1], saved_kernel_masks[1]);
return null;
}
return RMSes; // keep new kernels, return RMSes
}
/*
properties.setProperty(prefix+"asym_pixels",this.asym_pixels+"");
properties.setProperty(prefix+"asym_distance",this.asym_distance+"");
*/
// LMAData contains current kernels/masks (but not RMSes!)
// if improved - will leave with the new kernels/masks and return RMSes, if failed - return null and restore
// Try to replace each of the already enabled cells the different ones
public double [] replaceAny(
int asym_distance, // how far to seed a new pixel (hor/vert
double was_rms
){
// save state
double [][] saved_kernels = {lMAData.getSymKernel().clone(),lMAData.getAsymKernel().clone()};
boolean [][] saved_kernel_masks = {lMAData.getSymKernelMask().clone(),lMAData.getAsymKernelMask().clone()};
int numCells = 0;
for (int i = 0; i < saved_kernel_masks[1].length; i++) if (saved_kernel_masks[1][i]) numCells++;
int [] cells = new int [numCells];
int indx = 0;
for (int i = 0; i < saved_kernel_masks[1].length; i++) if (saved_kernel_masks[1][i]) cells[indx++] = i;
double [][] results = new double[cells.length][];
double [][][] result_kernels = new double[cells.length][][];
boolean [][] result_asym_mask = new boolean[cells.length][];
for (int removedCell = 0; removedCell < cells.length; removedCell++){
boolean [] asym_mask = saved_kernel_masks[1].clone();
asym_mask[cells[removedCell]] = false;
lMAData.setAsymKernel (null, asym_mask); // set mask only
results[removedCell] = addCell( asym_distance, Double.NaN,cells[removedCell]); // if Double.NaN - will not compare against it, return the best one
if (results[removedCell] == null){
System.out.println("replaceAny(): Failed to find any replacements at all");
} else if (results[removedCell][0] > was_rms){
if (this.debugLevel>2){
System.out.println("replaceAny(): Failed to find a better replacemnet for cell "+removedCell+" ("+ results[removedCell][0]+">"+was_rms+")");
}
results[removedCell] = null;
}
if (results[removedCell] != null) {
result_kernels[removedCell] = new double[2][];
result_kernels[removedCell][0] = lMAData.getSymKernel().clone();
result_kernels[removedCell][1] = lMAData.getAsymKernel().clone();
result_asym_mask[removedCell] = lMAData.getAsymKernelMask().clone();
}
}
// See if any of the replacements improved result
int bestToRemove = -1;
for (int n = 0; n < results.length; n++){
if ((results[n] != null) && (results[n][0] < was_rms) && ((bestToRemove < 0) || (results[n][0] < results[bestToRemove][0]))) {
bestToRemove = n;
}
}
if (bestToRemove < 0){
lMAData.setSymKernel (saved_kernels[0], saved_kernel_masks[0]);
lMAData.setAsymKernel (saved_kernels[1], saved_kernel_masks[1]);
return null;
}
lMAData.setSymKernel (result_kernels[bestToRemove][0], saved_kernel_masks[0]);
lMAData.setAsymKernel (result_kernels[bestToRemove][1], result_asym_mask[bestToRemove]);
return results[bestToRemove];
}
private int [] expandMask(
boolean [] mask,
int asym_distance){
boolean diagonal = false;
if (asym_distance < 0){
asym_distance = -asym_distance;
diagonal = true;
}
boolean[] mask0 = mask.clone();
for (int n = 0; n < asym_distance; n++){
boolean[] mask1 = mask0.clone();
if (diagonal){
for (int i = 0; i <asym_size; i++){
for (int j = 1; j<asym_size; j++){
mask1[asym_size*i + j] |= mask0[asym_size*i + j-1];
mask1[asym_size*i + (asym_size-j-1)] |= mask0[asym_size*i + (asym_size-j)];
mask1[asym_size*j + i] |= mask0[asym_size*(j-1) + i];
mask1[asym_size*(asym_size-j-1) + i] |= mask0[asym_size*(asym_size-j) + i];
}
}
} else {
// hor
for (int i = 0; i <asym_size; i++){
for (int j = 1; j<asym_size; j++){
mask1[asym_size*i + j] |= mask0[asym_size*i + j-1];
mask1[asym_size*i + (asym_size-j-1)] |= mask0[asym_size*i + (asym_size-j)];
}
}
// vert
mask0 = mask1.clone();
for (int i = 0; i <asym_size; i++){
for (int j = 1; j<asym_size; j++){
mask1[asym_size*j + i] |= mask0[asym_size*(j-1) + i];
mask1[asym_size*(asym_size-j-1) + i] |= mask0[asym_size*(asym_size-j) + i];
}
}
}
mask0 = mask1.clone();
if (debugLevel > 1) {
System.out.println("expandMask(): (n="+n+"), asym_size="+asym_size+" mask0.length="+mask0.length);
for (int i=0;i<asym_size;i++){
System.out.print(i+": ");
for (int j=0;j<asym_size;j++){
System.out.print((mask0[i*asym_size+j]?(mask[i*asym_size+j]?" +":" X"):" .")+" ");
}
System.out.println();
}
}
}
ArrayList<Integer> asym_candidates = new ArrayList<Integer>();
for (int i = 0; i<mask.length; i++) if (mask0[i] && !mask[i]) asym_candidates.add(new Integer(i));
int [] front = new int[asym_candidates.size()];
for (int i = 0; i<front.length;i++) front[i] = asym_candidates.get(i);
return front;
}
public double [] getSymKernel(){
if (new_mode) return lMAData.getSymKernel(sym_kernel_scale);
......@@ -1101,16 +1635,18 @@ public class FactorConvKernel {
// initial estimation
private double [][] setInitialVector(
double [] target_kernel, // should be (asym_size + 2*sym_radius-1)**2
boolean [] asym_mask) // which of the asymmetrical kernel to use
int seed_size, // 4*n +1 - center and 45-degree, 4*n - 4 center and 45-degree cross
double asym_random)
{
int conv_size = asym_size + 2*sym_radius-2;
int sym_rad_m1 = sym_radius - 1; // 7
// find center of the target kernel squared value
double s0=0.0,sx=0.0,sy=0.0;
double scx=0.0,scy=0.0;
boolean [] asym_mask = null;
if (asym_mask == null) {
asym_mask = new boolean[asym_size*asym_size];
for (int i = 0; i<asym_mask.length; i ++ ) asym_mask[i] = true;
for (int i = 0; i<asym_mask.length; i ++ ) asym_mask[i] = seed_size == 0;
}
for (int i = 0; i < conv_size; i++){
for (int j = 0; j < conv_size; j++){
......@@ -1123,10 +1659,11 @@ public class FactorConvKernel {
scy += d*(i-sym_rad_m1-asym_size/2);
}
}
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
if (debugLevel>1){
double xc = sx/s0 - sym_rad_m1;
double yc = sy/s0 - sym_rad_m1;
int j0= (int) Math.round(xc); // should be ~ async_center
int i0= (int) Math.round(yc); // should be ~ async_center
if (debugLevel>0){
System.out.println("setInitialVector(): scx="+(scx/s0) + " scy="+(scy/s0));
System.out.println("setInitialVector(): x="+(sx/s0) + " y="+(sy/s0));
......@@ -1143,9 +1680,56 @@ public class FactorConvKernel {
if (j0 >= asym_size) j0=asym_size-1;
}
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);
Random rand = new Random();
// for (int i = 0; i < asym_kernel.length; i++) asym_kernel[i] = 0; // 0.001*(rand.nextDouble()-0.5)/(asym_size*asym_size);
// for (int i = 0; i < asym_kernel.length; i++) asym_kernel[i] = 0.001*(rand.nextDouble()-0.5)/(asym_size*asym_size);
if (asym_random > 0) {
for (int i = 0; i < asym_kernel.length; i++) asym_kernel[i] = asym_random*(rand.nextDouble()-0.5)/(asym_size*asym_size);
}
asym_kernel[asym_size*i0+j0] = 1.0;
if (asym_random < 0) {
DoubleGaussianBlur gb = new DoubleGaussianBlur();
gb.blurDouble(asym_kernel, asym_size, asym_size, -asym_random, -asym_random, 0.01);
}
asym_mask [asym_size*i0+j0] = true;
if (seed_size > 0 ){ // 0 - just center, for compatibility with the old code
// the following code assumes that the asym_kernel can accommodate all initial cells
if ((seed_size & 1) == 1) { // around single center pixel
for (int n = 1; n <= (seed_size-1)/4; n++){
asym_mask [asym_size * (i0 + n) + (j0 + n)] = true;
asym_mask [asym_size * (i0 + n) + (j0 - n)] = true;
asym_mask [asym_size * (i0 - n) + (j0 + n)] = true;
asym_mask [asym_size * (i0 - n) + (j0 - n)] = true;
}
} else {
int j00 = (xc < j0) ? (j0-1):j0;
int i00 = (yc < i0) ? (i0-1):i0;
for (int n = 0; n < seed_size/4; n++){
asym_mask [asym_size * (i00 + n +1) + (j00 + n +1)] = true;
asym_mask [asym_size * (i00 + n +1) + (j00 - n +0)] = true;
asym_mask [asym_size * (i00 - n +0) + (j00 + n +1)] = true;
asym_mask [asym_size * (i00 - n +0) + (j00 - n +0)] = true;
}
}
}
if (debugLevel>2){
System.out.println("setInitialVector(target_kernel,"+seed_size+"): asym_mask.length="+asym_mask.length);
System.out.println("asym mask: ");
for (int ii=0;ii < asym_size;ii++){
System.out.print(ii+": ");
for (int jj=0;jj < asym_size;jj++){
System.out.print((asym_mask[ii * asym_size + jj]?" X":" .")+" ");
}
System.out.println();
}
}
center_x = xc; // not limited by asym_size
center_y = yc; // not limited by asym_size
center_j0 = j0; // center to calcualte compatess odf asymmetrical kernel
center_i0 = i0; // center to calcualte compatess odf asymmetrical kernel
center_j0 = j0; // center to calcualte compatess odf asymmetrical kernel
......@@ -1170,14 +1754,20 @@ public class FactorConvKernel {
int indx =((i >= sym_rad_m1)? (i-sym_rad_m1): (sym_rad_m1 -i)) * sym_radius +
((j >= sym_rad_m1)? (j-sym_rad_m1): (sym_rad_m1 -j));
sym_kernel[indx] += target_kernel[conv_size*(i+i0) + (j+j0)];
if ((debugLevel > 2) && (indx == 0)) {
if (debugLevel>0)System.out.println("1.sym_kernel[0] = "+sym_kernel[0]+" i="+i+" j="+j+" i0="+i0+" j0="+j0+" conv_size="+conv_size+
" target_kernel["+(conv_size*(i+i0) + (j+j0))+"] = " + target_kernel[(conv_size*(i+i0) + (j+j0))]);
}
sym_kernel_count[indx]++;
}
}
if (debugLevel > 2)System.out.println("sym_kernel[0] = "+sym_kernel[0]+" sym_kernel_count[0] = " +sym_kernel_count[0]);
for (int i = 0; i < sym_kernel.length; i++){
if (sym_kernel_count[i] >0) sym_kernel[i] /= sym_kernel_count[i];
else sym_kernel[i] = 0.0;
}
if (debugLevel > 2)System.out.println("sym_kernel[0] = "+sym_kernel[0]);
double [][] kernels = {sym_kernel, asym_kernel};
return kernels;
// return setVectorFromKernels(sym_kernel, asym_kernel);
......@@ -1202,7 +1792,7 @@ public class FactorConvKernel {
}
return kvect;
}
/*
private void maskAsymPoint(
double [] kvect,
int indx) {
......@@ -1214,6 +1804,7 @@ public class FactorConvKernel {
int indx) {
kvect[indx + sym_radius * sym_radius -1] = 0.0;
}
private int [] getVectorLengths(double [] kvect){ // full lengths, some elements may be Double.NaN (disabled)
int [] lengths = {0,0,0};// [0] - full length; [1] - asym start, [2] - asym length;
for (int i = 0; i<sym_radius*sym_radius - 1; i++){
......@@ -1226,6 +1817,7 @@ public class FactorConvKernel {
return lengths;
}
*/
......@@ -1569,7 +2161,15 @@ public class FactorConvKernel {
return Ma.getColumnPackedCopy();
}
private void initLevenbergMarquardt(double fact_precision){
public void resetLMARuns(){
numLMARuns = 0;
}
public int getLMARuns(){
return numLMARuns;
}
private void initLevenbergMarquardt(double fact_precision, int seed_size, double asym_random){
lMAData = new LMAData(debugLevel);
lMAData.setTarget(target_kernel);
......@@ -1579,13 +2179,14 @@ public class FactorConvKernel {
}
this.goal_rms_pure = Math.sqrt(s/target_kernel.length)*fact_precision;
// this.currentVector = setInitialVector(target_kernel, null); // should be (asym_size + 2*sym_radius-1)**2
double [][]kernels = setInitialVector(target_kernel, null); // should be (asym_size + 2*sym_radius-1)**2
double [][]kernels = setInitialVector(target_kernel, seed_size, asym_random); // should be (asym_size + 2*sym_radius-1)**2
sym_kernel_scale = kernels[0][0];
for (int i=0;i<kernels[0].length;i++) kernels[0][i] /=sym_kernel_scale;
for (int i=0;i<kernels[1].length;i++) kernels[1][i] *=sym_kernel_scale;
for (int i=0;i<kernels[0].length;i++) if (!Double.isNaN(kernels[0][i])) kernels[0][i] /=sym_kernel_scale;
for (int i=0;i<kernels[1].length;i++) if (!Double.isNaN(kernels[1][i])) kernels[1][i] *=sym_kernel_scale;
lMAData.setSymKernel (kernels[0]);
lMAData.setAsymKernel(kernels[1]);
lMAData.invalidateLMAArrays();
// lMAData.invalidateLMAArrays(); // should be in each run , not at init
resetLMARuns();
// this.currentVector = setVectorFromKernels(kernels[0], kernels[1]);
}
......@@ -1594,10 +2195,10 @@ public class FactorConvKernel {
long startTime=System.nanoTime();
this.iterationStepNumber = 0;
this.lambda = this.init_lambda;
lMAData.resetRMSes(); // boith first and saved
lMAData.resetRMSes(); // both first and saved
lastImprovements[0]=-1.0;
lastImprovements[1]=-1.0;
lMAData.invalidateLMAArrays(); // should be in each run , not at init
if (this.numIterations < 0){
lMAData.getFX(true); // try false too
......@@ -1611,9 +2212,16 @@ public class FactorConvKernel {
System.out.println(i+": "+ dbg_vector[i]);
}
}
if (this.debugLevel>1){
System.out.println("LMA before loop, RMS = "+lMAData.getRMSes()[0]+", pure="+lMAData.getRMSes()[1]);
}
while (true) { // loop for the same series
boolean [] state=stepLevenbergMarquardt(goal_rms_pure);
if ((this.iterationStepNumber==1) && (this.debugLevel > 1)){
System.out.println("LMA after first stepLevenbergMarquardt, RMS = "+lMAData.getRMSes()[0]+", pure="+lMAData.getRMSes()[1]);
}
// state[0] - better, state[1] - finished
if (this.debugLevel>1) System.out.println(this.iterationStepNumber+": stepLevenbergMarquardt()==>"+state[1]+":"+state[0]);
// boolean cont=true;
......@@ -1654,7 +2262,7 @@ public class FactorConvKernel {
this.lambda*= this.lambdaStepUp;
}
if ((this.debugLevel>0) && ((this.debugLevel>2) || ((System.nanoTime()-this.startTime)>10000000000.0))){ // > 10 sec
if ((this.debugLevel>0) && ((this.debugLevel>2) || ((System.nanoTime()-this.startTime)>30000000000.0))){ // > 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(lMAData.getRMSes()[0],8)+
......@@ -1662,20 +2270,27 @@ public class FactorConvKernel {
") at "+ IJ.d2s(0.000000001*(System.nanoTime()-startTime),3));
}
if (state[1]) { // finished
if (!state[0]) return false; // sequence failed
if (!state[0]) {
numLMARuns++;
return false; // sequence failed
}
break; // while (true), proceed to the next series
}
}
if (this.debugLevel>0) System.out.println("LevenbergMarquardt() finished in "+this.iterationStepNumber+
if (this.debugLevel>1) System.out.println("LevenbergMarquardt() finished in "+this.iterationStepNumber+
" steps, RMS="+lMAData.getRMSes()[0]+
" ("+lMAData.getFirstRMSes()[0]+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime),3));
", RMSpure="+lMAData.getRMSes()[1]+
" ("+lMAData.getFirstRMSes()[1]+") "+
"at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime),3));
numLMARuns++;
return true; // all series done
}
private boolean [] stepLevenbergMarquardt(double goal_rms_pure){
double [] deltas=null;
// System.out.println("lMAData.isValidLMAArrays()="+lMAData.isValidLMAArrays());
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(
......@@ -1814,15 +2429,16 @@ public class FactorConvKernel {
// Old version
private void initLevenbergMarquardt_old(double fact_precision){
private void initLevenbergMarquardt_old(double fact_precision, int seed_size, double asym_random){
double s = 0.0;
for (int i = 0; i<target_kernel.length; i++){
s+= target_kernel[i]*target_kernel[i];
}
this.goal_rms_pure = Math.sqrt(s/target_kernel.length)*fact_precision;
// this.currentVector = setInitialVector(target_kernel, null); // should be (asym_size + 2*sym_radius-1)**2
double [][]kernels = setInitialVector(target_kernel, null); // should be (asym_size + 2*sym_radius-1)**2
// this.currentVector = setInitialVector(target_kernel, 1); // should be (asym_size + 2*sym_radius-1)**2
double [][]kernels = setInitialVector(target_kernel, seed_size, asym_random); // should be (asym_size + 2*sym_radius-1)**2
this.currentVector = setVectorFromKernels(kernels[0], kernels[1]);
resetLMARuns();
}
......@@ -1890,15 +2506,21 @@ public class FactorConvKernel {
}
//stepLevenbergMarquardtAction_old();
if (state[1]) {
if (!state[0]) return false; // sequence failed
if (!state[0]) {
numLMARuns++;
return false; // sequence failed
}
break; // while (true), proceed to the next series
}
}
// if (this.fittingStrategy.isLastSeries(this.seriesNumber)) break;
if (this.debugLevel>0) System.out.println("LevenbergMarquardt_old() finished in "+this.iterationStepNumber+
if (this.debugLevel>0) System.out.println(
"LevenbergMarquardt_old() finished in "+this.iterationStepNumber+
" steps, RMS="+this.currentRMS+
" ("+this.firstRMS+") "+
") at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime),3));
" ("+this.firstRMS+")"+
", RMSPure="+this.currentRMSPure+
" ("+this.firstRMSPure+") "+
"at "+ IJ.d2s(0.000000001*(System.nanoTime()- startTime),3));
if (this.debugLevel>2) {
double worstRatio = 0;
int worstIndex = -1;
......@@ -1920,6 +2542,7 @@ public class FactorConvKernel {
}
System.out.println(" rms(relative diff["+worstIndex+"]) = >>>>> "+worstRatio+" <<<<<");
}
numLMARuns++;
return true; // all series done
}
......
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