From df840e99cf011098b39321bb13d16eeb770b379c Mon Sep 17 00:00:00 2001
From: amirabdelrahman <amira-rahman@aucegypt.edu>
Date: Mon, 11 May 2020 10:55:29 -0400
Subject: [PATCH] documentation and voxel,df,dnn search done
---
README.md | 51 +-
Representation Comparison.xlsx | Bin 13911 -> 14295 bytes
cpp_dft/main.cpp | 287 +++++--
cpp_dnn/cmaes_initials.par | 116 +++
cpp_dnn/cmaes_signals.par | 77 ++
cpp_dnn/dnn.cpp | 25 +-
cpp_dnn/image.cpp | 20 +-
cpp_dnn/main.cpp | 1474 ++++++++++++++++++++++++++------
cpp_voxel/main.cpp | 104 +--
img/df/1.png | Bin 0 -> 717 bytes
img/df/2.png | Bin 0 -> 757 bytes
img/df/3.png | Bin 0 -> 1152 bytes
img/df/4.png | Bin 0 -> 767 bytes
img/df/5.png | Bin 0 -> 817 bytes
img/dft/1.png | Bin 0 -> 490 bytes
img/dft/2.png | Bin 0 -> 454 bytes
img/dnn_cmaes/1.png | Bin 0 -> 709 bytes
img/dnn_cmaes/2.png | Bin 0 -> 582 bytes
img/dnn_cmaes/3.png | Bin 0 -> 1020 bytes
img/dnn_cmaes/4.png | Bin 0 -> 783 bytes
img/dnn_cmaes/5.png | Bin 0 -> 711 bytes
img/dnn_sgd/1.png | Bin 0 -> 663 bytes
img/dnn_sgd/2.png | Bin 0 -> 568 bytes
img/dnn_sgd/3.png | Bin 0 -> 1043 bytes
img/dnn_sgd/4.png | Bin 0 -> 761 bytes
img/dnn_sgd/5.png | Bin 0 -> 761 bytes
img/voxel/1.png | Bin 0 -> 667 bytes
img/voxel/2.png | Bin 0 -> 580 bytes
img/voxel/3.png | Bin 0 -> 1090 bytes
img/voxel/4.png | Bin 0 -> 764 bytes
img/voxel/5.png | Bin 0 -> 752 bytes
31 files changed, 1719 insertions(+), 435 deletions(-)
create mode 100644 cpp_dnn/cmaes_initials.par
create mode 100644 cpp_dnn/cmaes_signals.par
create mode 100644 img/df/1.png
create mode 100644 img/df/2.png
create mode 100644 img/df/3.png
create mode 100644 img/df/4.png
create mode 100644 img/df/5.png
create mode 100644 img/dft/1.png
create mode 100644 img/dft/2.png
create mode 100644 img/dnn_cmaes/1.png
create mode 100644 img/dnn_cmaes/2.png
create mode 100644 img/dnn_cmaes/3.png
create mode 100644 img/dnn_cmaes/4.png
create mode 100644 img/dnn_cmaes/5.png
create mode 100644 img/dnn_sgd/1.png
create mode 100644 img/dnn_sgd/2.png
create mode 100644 img/dnn_sgd/3.png
create mode 100644 img/dnn_sgd/4.png
create mode 100644 img/dnn_sgd/5.png
create mode 100644 img/voxel/1.png
create mode 100644 img/voxel/2.png
create mode 100644 img/voxel/3.png
create mode 100644 img/voxel/4.png
create mode 100644 img/voxel/5.png
diff --git a/README.md b/README.md
index 50ab961..466338e 100644
--- a/README.md
+++ b/README.md
@@ -32,11 +32,52 @@ Search methods used: nelder mead, gradient descent, EM and CMAES.
All target Images are 100*100
-<img src="img/circle_target.png" alt="drawing" width="200"/>
-<img src="img/rectange_target.png" alt="drawing" width="200"/>
-<img src="img/gear_target.png" alt="drawing" width="200"/>
-<img src="img/blob_target.png" alt="drawing" width="200"/>
-<img src="img/hole_target.png" alt="drawing" width="200"/>
+<img src="img/circle_target.png" width="200">
+<img src="img/rectange_target.png" width="200">
+<img src="img/gear_target.png" width="200">
+<img src="img/blob_target.png" width="200">
+<img src="img/hole_target.png" width="200">
+
+----
+
+### Example Best Results
+
+- parametric frep (~ 5 parameters)
+- frep trees (~ 10 parameters)
+- dft (~ 20 parameters)
+- DNN/DMN (~ 300 parameters)
+ - cmaes
+
+<img src="img/dnn_cmaes/1.png" width="200">
+<img src="img/dnn_cmaes/2.png" width="200">
+<img src="img/dnn_cmaes/3.png" width="200">
+<img src="img/dnn_cmaes/4.png" width="200">
+<img src="img/dnn_cmaes/5.png" width="200">
+
+ - sgd
+
+<img src="img/dnn_sgd/1.png" width="200">
+<img src="img/dnn_sgd/2.png" width="200">
+<img src="img/dnn_sgd/3.png" width="200">
+<img src="img/dnn_sgd/4.png" width="200">
+<img src="img/dnn_sgd/5.png" width="200">
+
+- distance fields (~ 400 parameters)
+
+<img src="img/df/1.png" width="200">
+<img src="img/df/2.png" width="200">
+<img src="img/df/3.png" width="200">
+<img src="img/df/4.png" width="200">
+<img src="img/df/5.png" width="200">
+
+- point cloud/mesh (~ 1,000 parameters)
+- voxel search (~ 10,000 parameters)
+
+<img src="img/voxel/1.png" width="200">
+<img src="img/voxel/2.png" width="200">
+<img src="img/voxel/3.png" width="200">
+<img src="img/voxel/4.png" width="200">
+<img src="img/voxel/5.png" width="200">
---
### dft progress
diff --git a/Representation Comparison.xlsx b/Representation Comparison.xlsx
index 893ab9689f6dc18cab4344f1d6192955040ee9b0..2484358bc85a05b62badb7c6a58cb9da4bd0232a 100644
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A!~g&Q
diff --git a/cpp_dft/main.cpp b/cpp_dft/main.cpp
index 76f8361..d6a7939 100644
--- a/cpp_dft/main.cpp
+++ b/cpp_dft/main.cpp
@@ -81,7 +81,7 @@ cv::Mat drawFrep(Frep* frep,scalar minX,scalar maxX,scalar minY,scalar maxY,scal
// cv::Scalar(0,0,0), // BGR Color
// 1); // Line Thickness (Optional)
- std::string fileName="img/"+name+".png";
+ std::string fileName="img_dft/"+name+".png";
cv::imwrite( fileName, image );
if(preview){
cv::namedWindow("Display Image", cv::WINDOW_AUTOSIZE );
@@ -310,7 +310,7 @@ cv::Mat dft(int w, int h, const int resolution, std::vector<scalar> mag,std::vec
cv::Scalar( 0, 0, 0 ),
lineType );
- std::string fileName="img/"+name+".png";
+ std::string fileName="img_dft/"+name+".png";
cv::cvtColor(img, img, cv::COLOR_BGR2GRAY);
if(save){
cv::imwrite ( fileName, img );
@@ -368,97 +368,84 @@ scalar compareToTarget(int width,int height,const int resolution,Vector v, cv::M
int main(int const, char const**) {
- scalar res=0.01f;
- scalar bound =3.0f;
float precision = 0.001f;
+ scalar res=0.05f;
+ scalar bound =2.5f;
+
+
+
scalar minX=-bound;
scalar maxX=bound;
scalar minY=-bound;
scalar maxY=bound;
scalar dx=res;
scalar dy=res;
-
int height=(int)((maxX-minX)/dx);
int width=(int)((maxY-minY)/dy);
- // defines the complex number: (10 + 2i)
- // std::complex<scalar> mycomplex(10.0f, 2.0f);
- // std::complex<scalar> mycomplex =polar (1.0f, 0.927295f) ;
- // std::complex<scalar> j;
- // j=-1.0f;
- // j=sqrt(j);
+ cout<<"width: "<<width<<", height: "<<height <<std::endl;
- // std::complex<scalar> trial = mycomplex * exp (-1.0f*PI_F*j*1.0f);
- // std::complex<scalar> trial = exp (-1.0f*PI_F*j*1.0f);
-
- // // prints the real part using the real function
- // cout << "Real part: " << real(trial) << endl;
- // cout << "Imaginary part: " << imag(trial) << endl;
- // cout << "The complex whose magnitude is " << 5.0;
- // cout << " and phase angle is " << 0.927295;
-
- // // use of polar()
- // cout << " is " << polar (5.0, 0.927295) << endl;
+ const int resolution=19;
- // // defines the complex number: (3.0+4.0i)
- // std::complex<double> mycomplex (3.0, 4.0);
-
- // // prints the absolute value of the complex number
- // cout << "The absolute value of " << mycomplex << " is: ";
- // cout << abs(mycomplex) << endl; //magnitude
-
- // // prints the argument of the complex number
- // cout << "The argument of " << mycomplex << " is: ";
- // cout << arg(mycomplex) << endl; //phase
+ cpp_frep::Circle c(1.5f,0.0f,0.0f);
+ // std::vector<unsigned char> targ1 =drawFrep(&c,minX, maxX, minY, maxY, dx, dy,"circle_target",false);
+ cv::Mat targ1 =drawFrep(&c,minX, maxX, minY, maxY, dx, dy,"dft_circle",false);
+ cpp_frep::Rectangle r(-1.5f,1.5f,-1.0f,1.0f);
+ cpp_frep::Rotate rot1 (&r,0.0f,0.0f,90.0f);
+ // std::vector<unsigned char> targ2 =drawFrep(&rot1,minX, maxX, minY, maxY, dx, dy,"rectange_target",false);
+ cv::Mat targ2 =drawFrep(&rot1,minX, maxX, minY, maxY, dx, dy,"dft_reactangle",false);
-
+ cpp_frep::InvoluteGear gear( 1.0f, 1.0f, 1.1f, 20.0f, 8.0f) ;
+ cpp_frep::Rotate rot (&gear,0.0f,0.0f,15.0f);
+ cpp_frep::Scale sc1(&rot,0.0f,0.0f,0.5f,0.5f);
+ cpp_frep::Add target(&sc1,&c);
+ // std::vector<unsigned char> targ3 =drawFrep(&target,minX, maxX, minY, maxY, dx, dy,"gear_target",false);
+ cv::Mat targ3 =drawFrep(&target,minX, maxX, minY, maxY, dx, dy,"dft_gear",false);
- // std::vector<scalar> mag ={0.0f,0.5f, 0.25f,0.1f,0.05f};
- // std::vector<scalar> ampl={0.0f,0.0f,PI_F/2.0f,0.0f,0.0f};
- const int resolution=19;
+ cpp_frep::Circle c1(0.9f,-0.5f,0.8f);
+ cpp_frep::Circle c2(0.8f,0.2f,-0.2f);
+ cpp_frep::Circle c3(0.9f,1.5f,0.0f);
+ cpp_frep::Circle c4(0.9f,1.7f,-1.1f);
+ // Subtract ss(&rot1,&c1);
+
+ // Subtract sss(&ss,&c2);
+ cpp_frep::Add aa(&c1,&c3);
+ cpp_frep::Add aaa(&aa,&c2);
+ cpp_frep::Add a(&aaa,&c4);
+ cpp_frep::Morph m (&a,&target,0.6f);
+ cpp_frep::Rotate rot2 (&m,0.3f,0.0f,-90.0f);
+ // Scale scc (&m,0.0f,0.0f,1.0,1.0);
+ // std::vector<unsigned char> targ4 =drawFrep(&rot2,minX, maxX, minY, maxY, dx, dy,"blob_target",false);
+ cv::Mat targ4 =drawFrep(&rot2,minX, maxX, minY, maxY, dx, dy,"dft_blob",false);
- // std::vector<scalar> mag ={0.0f, 1.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f, 0.05f};
- // std::vector<scalar> ampl={0.0f, 0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f, 0.0f};
- // cv::Mat d = dft(width, height, resolution, mag , ampl,"dft",true,true);
+ cpp_frep::Circle c5(0.5f,0.0f,0.0f);
+ cpp_frep::Subtract ss(&c,&c5);
+ // std::vector<unsigned char> targ5 =drawFrep(&ss,minX, maxX, minY, maxY, dx, dy,"hole_target",false);
+ cv::Mat targ5 =drawFrep(&ss,minX, maxX, minY, maxY, dx, dy,"dft_hole",false);
- // mag=getRandomMag(resolution);
- // ampl=getRandomAmpl(resolution);
- // cv::Mat d = dft(width, height, resolution, mag,ampl,"dft",true);
- // cv::cvtColor(d, d, cv::COLOR_BGR2GRAY);
- // cv::cvtColor(targ, targ, cv::COLOR_BGR2GRAY);
- // cv::Mat diff= d-targ;
- // cout<< "nonzero: "<<countNonZero(diff)<<endl;
- // cout<< "size: "<<width*height<<endl;
- // cout<< "norm l2: "<<cv::norm(d, targ, cv::NORM_L2)<<endl;
- // cout<< "norm l1: "<<cv::norm(d, targ, cv::NORM_L1)<<endl;
- // cout<< "norm NORM_HAMMING: "<<cv::norm(d, targ,cv::NORM_HAMMING )<<endl;
- // cout<< "norm NORM_HAMMING2: "<<cv::norm(d, targ,cv::NORM_HAMMING2 )<<endl;
- // cout<< "norm NORM_L2SQR: "<<cv::norm(d, targ,cv::NORM_L2SQR )<<endl;
- // cout<< "norm: "<<cv::norm(d, targ)<<endl;
-
- // cv::namedWindow("Display Image", cv::WINDOW_AUTOSIZE );
- // cv::imshow("Display Image", diff);
- // cv::waitKey(0);
- Circle c(1.2f,0.0f,0.0f);
- InvoluteGear gear( 1.0f, 1.0f, 1.1f, 20.0f, 8.0f) ;
- Rotate rot (&gear,0.0f,0.0f,15.0f);
- Scale sc1(&rot,0.0f,0.0f,0.3f,0.3f);
- Add target(&sc1,&c);
- cv::Mat targ =drawFrep(&target,minX, maxX, minY, maxY, dx, dy,"dft_target",false);
+ // Circle c(1.2f,0.0f,0.0f);
+ // InvoluteGear gear( 1.0f, 1.0f, 1.1f, 20.0f, 8.0f) ;
+ // Rotate rot (&gear,0.0f,0.0f,15.0f);
+ // Scale sc1(&rot,0.0f,0.0f,0.3f,0.3f);
+ // Add target(&sc1,&c);
+ cv::Mat targ;
// Circle c(1.0f,0.0f,0.0f);
// cv::Mat targ =drawFrep(&c,minX, maxX, minY, maxY, dx, dy,"dft_target",false);
- cv::cvtColor(targ, targ, cv::COLOR_BGR2GRAY);
+ cv::cvtColor(targ3, targ, cv::COLOR_BGR2GRAY);
+
+
+
///neldermead
@@ -484,7 +471,7 @@ int main(int const, char const**) {
int count2=0;
int maxSteps=10000;
int maxSteps1=10000;
- int saveEvery=10;
+ int saveEvery=25;
scalar score=0.0f;
std::string name1 = "ii";
@@ -503,6 +490,7 @@ int main(int const, char const**) {
if(count2>=saveEvery){
//save
score=compareToTarget( width, height,resolution,v, targ ,name,true,false);
+ std::cout<<"Step:"<<count<<", total:"<< count1<<", score:"<<-score<<"\n";
count2=0;
}else{
@@ -511,7 +499,7 @@ int main(int const, char const**) {
}
- std::cout<<"Step:"<<count<<", total:"<< count1<<", score:"<<-score<<"\n";
+
v = o.step(v, score);
count++;
count1++;
@@ -538,3 +526,170 @@ int main(int const, char const**) {
}
+///target1
+ // Step:25, total:25, score:44004
+ // Step:51, total:51, score:40950
+ // Step:77, total:77, score:26444
+ // Step:103, total:103, score:23756
+ // Step:129, total:129, score:16948
+ // Step:155, total:155, score:14380
+ // Step:181, total:181, score:13500
+ // Step:207, total:207, score:12524
+ // Step:233, total:233, score:13532
+ // Step:259, total:259, score:12020
+ // Step:285, total:285, score:12356
+ // Step:311, total:311, score:11908
+ // Step:337, total:337, score:11772
+ // Step:363, total:363, score:12788
+ // Step:389, total:389, score:11564
+ // Step:415, total:415, score:12196
+ // Step:441, total:441, score:11636
+ // Step:467, total:467, score:12268
+ // Step:493, total:493, score:11724
+ // Step:519, total:519, score:11796
+ // Step:545, total:545, score:11932
+ // Step:571, total:571, score:12180
+ // Step:597, total:597, score:11740
+ // Step:623, total:623, score:11812
+ // Step:649, total:649, score:12172
+ // Step:675, total:675, score:11764
+ // Step:701, total:701, score:11972
+ // Step:727, total:727, score:11836
+ // Step:753, total:753, score:11740
+ // Step:779, total:779, score:11716
+ // Step:805, total:805, score:11732
+ // Step:831, total:831, score:11812
+ // Step:857, total:857, score:11788
+ // Step:883, total:883, score:11740
+ // Step:909, total:909, score:11860
+ // Step:935, total:935, score:11908
+ // Step:961, total:961, score:11748
+ // Step:987, total:987, score:11948
+ // Step:1013, total:1013, score:11676
+ // Step:1039, total:1039, score:12260
+ // Step:1065, total:1065, score:11764
+ // Step:1091, total:1091, score:11772
+ // Step:1117, total:1117, score:11764
+ // Step:1143, total:1143, score:11788
+ // Step:1169, total:1169, score:11972
+ // Step:1195, total:1195, score:11804
+ // Step:1221, total:1221, score:11884
+ // Step:1247, total:1247, score:11900
+ // Step:1273, total:1273, score:11868
+ // Step:1299, total:1299, score:12260
+ // Step:1325, total:1325, score:11732
+ // Step:1351, total:1351, score:11740
+ // Step:1377, total:1377, score:11900
+ // Step:1403, total:1403, score:11796
+ // Step:1429, total:1429, score:11740
+ // Step:1455, total:1455, score:11788
+ // Step:1481, total:1481, score:12236
+
+//target 2
+
+ // Step:25, total:25, score:28356
+ // Step:51, total:51, score:23300
+ // Step:77, total:77, score:24778
+ // Step:103, total:103, score:18426
+ // Step:129, total:129, score:16386
+ // Step:155, total:155, score:14418
+ // Step:181, total:181, score:13234
+ // Step:207, total:207, score:12794
+ // Step:233, total:233, score:12426
+ // Step:259, total:259, score:12746
+ // Step:285, total:285, score:11562
+ // Step:311, total:311, score:11978
+ // Step:337, total:337, score:11786
+ // Step:363, total:363, score:11282
+ // Step:389, total:389, score:12162
+ // Step:415, total:415, score:12074
+ // Step:441, total:441, score:12218
+ // Step:467, total:467, score:11530
+ // Step:493, total:493, score:11754
+ // Step:519, total:519, score:11994
+ // Step:545, total:545, score:11810
+ // Step:571, total:571, score:11658
+ // Step:597, total:597, score:11218
+ // Step:623, total:623, score:11898
+ // Step:649, total:649, score:11506
+ // Step:675, total:675, score:11442
+ // Step:701, total:701, score:11474
+ // Step:727, total:727, score:11970
+ // Step:753, total:753, score:12042
+ // Step:779, total:779, score:11594
+ // Step:805, total:805, score:11986
+ // Step:831, total:831, score:11450
+ // Step:857, total:857, score:11762
+ // Step:883, total:883, score:11522
+ // Step:909, total:909, score:11442
+ // Step:935, total:935, score:11890
+ // Step:961, total:961, score:11490
+ // Step:987, total:987, score:11986
+ // Step:1013, total:1013, score:11706
+ // Step:1039, total:1039, score:12186
+ // Step:1065, total:1065, score:11610
+ // Step:1091, total:1091, score:11866
+ // Step:1117, total:1117, score:11626
+ // Step:1143, total:1143, score:12106
+ // Step:1169, total:1169, score:11386
+ // Step:1195, total:1195, score:11450
+ // Step:1221, total:1221, score:11586
+ // Step:1247, total:1247, score:11418
+ // Step:1273, total:1273, score:11450
+ // Step:1299, total:1299, score:11610
+ // Step:1325, total:1325, score:11898
+ // Step:1351, total:1351, score:11658
+ // Step:1377, total:1377, score:11658
+ // Step:1403, total:1403, score:11746
+ // Step:1429, total:1429, score:11818
+ // Step:1455, total:1455, score:11730
+ // Step:1481, total:1481, score:11442
+ // Step:1507, total:1507, score:11786
+ // Step:1533, total:1533, score:11442
+ // Step:1559, total:1559, score:11874
+ // Step:1585, total:1585, score:11714
+ // Step:1611, total:1611, score:11626
+ // Step:1637, total:1637, score:11458
+ // Step:1663, total:1663, score:11178
+ // Step:1689, total:1689, score:12146
+ // Step:1715, total:1715, score:11634
+ // Step:1741, total:1741, score:12178
+ // Step:1767, total:1767, score:11850
+ // Step:1793, total:1793, score:11698
+ // Step:1819, total:1819, score:11634
+ // Step:1845, total:1845, score:11570
+ // Step:1871, total:1871, score:12026
+ // Step:1897, total:1897, score:11610
+ // Step:1923, total:1923, score:11490
+ // Step:1949, total:1949, score:12122
+ // Step:1975, total:1975, score:11914
+ // Step:2001, total:2001, score:11522
+ // Step:2027, total:2027, score:12314
+ // Step:2053, total:2053, score:11594
+ // Step:2079, total:2079, score:11866
+ // Step:2105, total:2105, score:11538
+ // Step:2131, total:2131, score:11362
+ // Step:2157, total:2157, score:11514
+ // Step:2183, total:2183, score:11650
+ // Step:2209, total:2209, score:11514
+ // Step:2235, total:2235, score:11554
+ // Step:2261, total:2261, score:11474
+ // Step:2287, total:2287, score:11954
+ // Step:2313, total:2313, score:11594
+ // Step:2339, total:2339, score:12098
+ // Step:2365, total:2365, score:11930
+ // Step:2391, total:2391, score:11746
+ // Step:2417, total:2417, score:11226
+ // Step:2443, total:2443, score:11506
+ // Step:2469, total:2469, score:11714
+ // Step:2495, total:2495, score:11786
+ // Step:2521, total:2521, score:12338
+ // Step:2547, total:2547, score:11618
+
+// target3
+
+// target4
+
+
+// target5
+
diff --git a/cpp_dnn/cmaes_initials.par b/cpp_dnn/cmaes_initials.par
new file mode 100644
index 0000000..c3609a9
--- /dev/null
+++ b/cpp_dnn/cmaes_initials.par
@@ -0,0 +1,116 @@
+function number 1 2 5 6 8 23 # the first number is read by example_restarts.c, see there.
+restarts 0 # read by example_restarts.c, remark that restarts
+ # with the same initialX might be sub-optimal
+#
+# Input parameter file for cmaes_t.
+# Comments start with '#' or '%' until end of line.
+# Actual recent parameter setting is written to file actparcmaes.par.
+#
+
+## --- OBLIGATORY SETTINGS
+# these settings, if read, overwrite the input values to cmaes_init
+
+ N 3 # Problem dimension, overwrites parameter in cmaes_init
+ initialX 10: # Initial search point. Syntax: 1==read one number, see also end of file.
+ 0.5
+ 1.1
+ 1.1
+# 0.1
+# 0.9
+# 0.1
+# 0.1
+# 0.4
+# 0.15
+# 0.1
+# 0.6
+# 2.8
+ typicalX 1: # Typical search point (useful for restarts, cave: boundaries), overwritten by initialX
+ 0.0 # these are the read value(s)
+ initialStandardDeviations 1: # 1==read only one number for all coordinates
+ 0.1 # numbers should not differ by orders of magnitude
+ # should be roughly 1/4 of the search interval
+ # this number essentially influences the global
+ # search ability (ie. the horizon where to search
+ # at all) on multimodal functions
+
+## --- OPTIONAL SETTINGS
+
+# stop-Parameters can be changed online via cmaes_signals.par
+
+stopMaxFunEvals 1e4 # max number of f-evaluations, 900*(N+3)*(N+3) is default
+# fac*maxFunEvals 1e0 # multiplies stopMaxFunEvals by read number, default is 1
+# stopMaxIter 1e3 # max number of iterations (generations), inf is default
+stopMaxIter 3 # max number of iterations (generations), inf is default
+
+
+stopFitness -500 # stop if function value is smaller than stopFitness
+ # commented == never stop on function value (default)
+stopTolFun 1e-1 # stop if function value differences are
+ # smaller than stopTolFun, default=1e-12
+stopTolFunHist 1e-1 # stop if function value differences of best values are
+ # smaller than stopTolFunHist, default was 0
+stopTolX 1e-2 # stop if step sizes/steps in x-space are
+ # smaller than TolX, default=0
+stopTolUpXFactor 1e3 # stop if std dev increases more than by TolUpXFactor, default 1e3
+
+seed 0 # 0 == by time, also regard maxTimeFractionForEigendecomposition
+ # below, as for values smaller than one the outcome might not be
+ # exactly reproducible even with the same seed
+
+# diffMinChange 1 # Minimal coordinate wise standard deviation. Syntax see below.
+# 1e-299 # Interferes with stopTolX termination criterion! default=0
+
+
+## --- internal CPU-time related settings
+
+maxTimeFractionForEigendecompostion 1 # maximal CPU-time fraction for eigensystem
+ # decomposition. Large values (up to one) are better
+ # w.r.t. the number of function evaluations to reach a
+ # certain function value. Only >=1 yields exactly
+ # reproducible results.
+ # 0.2==20% should often be faster than larger values
+ # w.r.t. the CPU-time to reach a certain function value.
+
+# updatecov 1 # default is updating the eigensystem after
+ # every 1/ccov/N/10-th generation.
+# fac*updatecov 3 # multiplier for updatecov
+
+# resume allcmaes.dat # reads restart distribution from given file
+
+
+## --- Strategy internal parameter ---
+## default values are set in cmaes_readpara_SupplementDefaults()
+## -- Selection related parameters
+lambda 5 # number of offspring == samplesize was 12
+# mu
+# weights log # possible values: log (==default),
+ # lin (==linearely decreasing), or equal
+
+## -- Adaptation (distribution estimation) related parameters
+# diagonalCovarianceMatrix -1 # number of iterations, 1<==>always, -1<==>2+100*N/sqrt(lambda)
+# fac*damp 1 # increase or decrease damping for step size control.
+# ccumcov 1 # default is 4/(N+4), 1 means no cumulation for p_c.
+# mucov 1 # 1 means only rank 1 update of C
+# fac*ccov 1 # multiplier for default learning rate for cov. matrix
+
+
+## --- Syntax for input vectors xstart, sigma, mincoorstddev
+#
+# N 7 # dimension must be defined before
+# xstart 3 : # read 3 numbers from next lines, the colon is superfluous
+# 0.2 0.3
+# 0.4 0.5 0.6
+# 0.7
+#
+# reads the first three numbers starting from the
+# line following the keyword "xstart" and recycles
+# these numbers (or cut them) to length N, resulting in
+# xstart=[0.2 0.3 0.4 0.2 0.3 0.4 0.2]
+# No comments are allowed between the numbers. I.e.
+#
+# xstart 3 22 anything here is ok, 22 is disregarded # still ok
+# 0.2 0.3 # this comment fails, if >2 numbers to be read
+# 0.4 0.5 0.6
+#
+# would fail due to the comment between second and third number to
+# be read.
diff --git a/cpp_dnn/cmaes_signals.par b/cpp_dnn/cmaes_signals.par
new file mode 100644
index 0000000..e6536fb
--- /dev/null
+++ b/cpp_dnn/cmaes_signals.par
@@ -0,0 +1,77 @@
+#
+# Comment characters are '%' and '#' to end of line.
+# Uncomment one or more rows (and/or add an uncommented line)
+# and save this file to induce an action.
+#
+# Function cmaes_ReadSignals reads and interprets the syntax
+# as given in this file
+#
+
+## --- modify termination condition, see also initials.par for more docu
+# stop now # manual stop as soon as signals.par is parsed
+# stopMaxFunEvals 4.5e6 # stop after given number of function evaluations
+# stopMaxIter 3e3 # stop after given number of iterations (generations)
+# stopFitness 1e-6 # stop if function value is smaller than stopFitness
+# stopTolFun 1e-12 # stop if function value differences are small
+# stopTolFunHist 1e-12 # stop if f-value differences between best values are small
+# stopTolX 1e-1 # stop if step-sizes/steps in x-space are small
+# stopTolUpXFactor 1e3 # stop if std dev increases more than by TolUpXFactor
+
+# checkeigen 1 # number > 0 switches checking on, Check_Eigen() is O(n^3)!
+# maxTimeFractionForEigendecompostion 0.2
+
+# write resume allcmaes.dat # write data for restart
+
+## --- print data to stdout
+## syntax (in one row):
+## print <keyword for cmaes_WriteToFilePtr()>[+<another keyword>]
+## [<seconds>]
+## After the first iteration ONLY the action with the smallest
+## seconds value will take place.
+## For more info see write data below.
+
+# print gen+fitness 5
+## "few" prints Fevals Fval Sigma Max&MinCoorDev AxisRatio MinOfD
+# print fewinfo 30 # print every n seconds
+# print few+clock 2 # clock: used processor time since start
+ print few 2
+# print fitness+xmean 3
+# print gen+few 20
+# print gen+few+few(diag(D)) 0
+# print few(diag(D))
+# print all 100
+
+## --- write data to file (example2.c can also write into data file)
+## syntax (in one row):
+## write <keyword for cmaes_WriteToFilePtr()>[+<another keyword>]
+## [<filename>] [<seconds>]
+## After the first iteration ONLY the action with the smallest
+## seconds value will take place.
+
+## Default filename is tmpcmaes.dat. Default is seconds=1,
+## and during the first second or so it is 0 with a smooth
+## writing gap transition until up to one second. For
+## seconds=0 writing takes place every generation. For seconds < 0
+## writing is blocked after the first key where seconds < 0 was
+## written. Blocking lasts until all values for seconds are >=0
+## again. For keywords compare cmaes_interface.h and function
+## cmaes_WriteToFilePtr in cmaes.c.
+
+## KEYWORD(s) FILE SECONDS to wait until next writing
+# write with default format for plotting
+write iter+eval+sigma+axisratio+fbestever+fitness+fmedian+fworst+mindii+idxmaxSD+maxSD+idxminSD+minSD outcmaesfit.dat
+write iter+eval+sigma+axisratio+stddevratio+diag(D) outcmaesaxlen.dat
+write iter+eval+sigma+idxmaxSD+idxminSD+stddev outcmaesstddev.dat
+write iter+eval+sigma+0+0+xmean outcmaesxmean.dat
+write iter+eval+sigma+0+fitness+xbest outcmaesxrecentbest.dat
+
+## KEYWORD(s) FILE SECONDS to wait until next writing
+
+# write few+few(diag(D)) rescmaes.dat 0 # writes every generation
+# write few+diag(D) tmp.dat 0 # writes every generation
+# write few+few(diag(D)) rescmaes.dat # writes once per second
+# write few+few(diag(D)) rescmaes.dat -1 # writes once, blocks further writing
+# write gen+xbest xcmaes.dat 2
+# write B allcmaes.dat 100 # writes every 100 seconds
+# write all allcmaes.dat 100
+# write gen+arfitness tmpcmaes.dat 0
diff --git a/cpp_dnn/dnn.cpp b/cpp_dnn/dnn.cpp
index bd396fb..2f62ff2 100644
--- a/cpp_dnn/dnn.cpp
+++ b/cpp_dnn/dnn.cpp
@@ -474,30 +474,9 @@ namespace cpp_dnn {
this->sgd_step(lrate);
}
- std::cout<<"Loss: "<< sum_loss<<std::endl;
+ // std::cout<<"Loss: "<< sum_loss<<std::endl;
- // for(var i=0; i < iters; i++){
- // var j= getRandomInt(N);
- // var Xt = X.slice([0,j], [D,1]);
- // var Yt = Y.slice([0,j], [D,1]);
- // var Ypred = this.forward(Xt);
- // sum_loss.add(sum_loss, this.loss.forward(Ypred, Yt));
- // var err = this.loss.backward();
- // this.backward(err);
- // this.sgd_step(lrate);
-
- // }
- // D, N = X.shape
- // for it in range(iters):
- // j = np.random.randint(N)
- // Xt = X[:,j:j+1]; Yt = Y[:,j:j+1]
- // print(Xt)
- // Ypred=self.forward( Xt)
- // l=self.loss.forward(Ypred, Yt)
- // delta=self.loss.backward()
- // b=self.backward( delta)
- // s=self.sgd_step( lrate)
- // }
+
}
// private:
diff --git a/cpp_dnn/image.cpp b/cpp_dnn/image.cpp
index 0bb6dbe..21d060c 100644
--- a/cpp_dnn/image.cpp
+++ b/cpp_dnn/image.cpp
@@ -18,8 +18,8 @@ namespace cpp_dnn {
class Image {
public:
- scalar res=0.01;
- scalar bound =3.0;
+ scalar res=0.05;
+ scalar bound =2.5;
scalar minX;
scalar maxX;
scalar minY;
@@ -31,7 +31,7 @@ namespace cpp_dnn {
int height;
int width;
- Image():res(0.01),bound(3.0){
+ Image():res(0.05),bound(2.5){
minX=-bound;
maxX=bound;
minY=-bound;
@@ -99,11 +99,23 @@ namespace cpp_dnn {
saveImage(predict(target.t()) ,"Target_"+ name);
return target.t();
}
+
+ int getHamming(const MatrixXI& Ypred,const MatrixXI& YY){
+ int d=0;
+ for(unsigned i=0;i<Ypred.size();i++){
+ if((Ypred(i)>0.0 && YY(i)>0.0 )||(Ypred(i)<=0.0 && YY(i)<=0.0)){
+ }else{
+ d++;
+ }
+ }
+ return d;
+ }
void saveImage(const MatrixXI& Ypred,std::string name){
const int size=height*width*4;
int stride=0;
unsigned char pix[size];
+
for(unsigned i=0;i<Ypred.size();i++){
if(Ypred(i)>0.0){
@@ -123,7 +135,7 @@ namespace cpp_dnn {
////////////////create image and save it//////////////////////////
cv::Mat image = cv::Mat(width, height, CV_8UC4, pix);
- std::string fileName="img/"+name+".jpg";
+ std::string fileName="img_dnn/"+name+".png";
cv::imwrite( fileName, image );
bool preview=false;
if(preview){
diff --git a/cpp_dnn/main.cpp b/cpp_dnn/main.cpp
index badd39b..f2c6ac0 100644
--- a/cpp_dnn/main.cpp
+++ b/cpp_dnn/main.cpp
@@ -13,6 +13,8 @@
using namespace cpp_dnn;
using scalar=double;
+int COUNT=0;
+
scalar getRandom(){//between 0 and 1
return ((scalar) rand() / (RAND_MAX));
}
@@ -45,333 +47,1235 @@ std::vector<scalar> pointerToVector(double const *x, int N){
// /* the objective (fitness) function to be minimized */
double fitfun(double const *x, int N,MatrixX X,MatrixX Y,Sequential seq) { /* function "cigtab" */
+ Image Im;
+ MatrixX XX=Im.getLocMatrix();
seq.setWeights( pointerToVector(x, N));
- seq.step(X,Y);
- return seq.sumLoss();
+ auto Ypred=seq.step(X,Y);
+ auto YY=Im.predict(Y);
+ double res=(double) Im.getHamming(Ypred,YY);
+ if(COUNT%100==0){
+ Im.saveImage(Ypred,std::to_string(COUNT)); //test
+ cout<<"iter:"<<COUNT<<" score:"<<res<<endl;
+
+ }
+ COUNT++;
+
+
+ return res;
}
//--------------------------------------------------------------------------------------------------
int main(int const, char const**) {
- // //Create network
- // std::vector<Node*> network;
-
- // Linear l1(2, 12);
- // Tanh a1;
-
- // Linear l2(12, 12);
- // Tanh a2;
-
- // Linear l3(12, 6);
- // Tanh a3;
-
- // Linear l4(6, 6);
- // Tanh a4;
-
- // Linear l5(6, 2);
- // SoftMax a5;
-
- // NLL l;
- // network.push_back( &l1);
- // network.push_back( &a1);
- // network.push_back( &l2);
- // network.push_back( &a2);
- // network.push_back( &l3);
- // network.push_back( &a3);
- // network.push_back( &l4);
- // network.push_back( &a4);
- // network.push_back( &l5);
- // network.push_back( &a5);
-
- // Sequential seq(network, &l);
-
- // Linear linear_1(2,3);
- // scalar lrate = 0.005;
- // // arma::Mat<double> a=arma::randu(2,2);
-
-
- // arma::Mat<scalar> X;
- // arma::Mat<scalar> Y;
- // X<< 2<< 3<< 9<< 12<<arma::endr
- // << 5<< 2<< 6<< 5<<arma::endr;
- // Y<<0<< 1<< 0<< 1<<arma::endr
- // <<1<< 0<< 1<< 0<<arma::endr;
- // // arma::Mat<scalar> dL_dz1;
- // // dL_dz1<< 1.69467553e-09<<-1.33530535e-06<< 0.00000000e+00<<-0.00000000e+00<<arma::endr
- // // <<-5.24547376e-07<< 5.82459519e-04<< -3.84805202e-10<< 1.47943038e-09<<arma::endr
- // // <<-3.47063705e-02<< 2.55611604e-01<< -1.83538094e-02<< 1.11838432e-04<<arma::endr;
-
- // // std::cout << linear_1.forward(X) << '\n';
- // // std::cout << linear_1.backward(dL_dz1) << '\n';
- // // linear_1.sgd_step(lrate);
- // // std::cout << linear_1._W << '\n';
- // // std::cout << linear_1._W0 << '\n';
+ scalar res=0.05f;
+ scalar bound =2.5f;
-
- // std::cout << arma::index_max(Y,0) << '\n';
- // std::vector<Node*> network;
- // Linear l1(2, 3);
- // ReLU t;
- // Linear l2(3, 2);
- // SoftMax s;
- // NLL l;
- // network.push_back( &l1);
- // network.push_back( &t);
- // network.push_back( &l2);
- // network.push_back( &s);
-
- // arma::Mat<scalar> z_1 = l1.forward(X);
-
- // std::cout << z_1 << '\n';
-
- // arma::Mat<scalar> a_1 = t.forward(z_1);
-
- // std::cout << a_1 << '\n';
-
- // arma::Mat<scalar> z_2;
-
- // // z_2 <<0.40837833<<0.53900088<< 0.56956001<< 0.57209377<<arma::endr
- // // << -0.66368766<< 0.65353931<< 0.96361427<< 0.98919526<<arma::endr;
-
-
- // z_2<<5.28714248<< 3.64078533<< 10.92235599<< 12.36410102<<arma::endr
- // << 0.78906625<< 0.80620366<< 2.41861097<<4.44170662<<arma::endr;
- // // arma::Mat<scalar> z_2 = l2.forward(a_1);
+ scalar minX=-bound;
+ scalar maxX=bound;
+ scalar minY=-bound;
+ scalar maxY=bound;
+ scalar dx=res;
+ scalar dy=res;
+ int height=(int)((maxX-minX)/dx);
+ int width=(int)((maxY-minY)/dy);
- // // std::cout << z_2 << '\n';
+ cout<<"width: "<<width<<", height: "<<height <<std::endl;
- // arma::Mat<scalar> a_2 = s.forward(z_2);
+ //Create Image
+ Image Im;
+ MatrixX X=Im.getLocMatrix();
- // std::cout << a_2 << '\n';
-
- // arma::Mat<scalar> loss = l.forward(a_2, Y);
-
- // std::cout << loss<< '\n';
-
- // arma::Mat<scalar> dloss = l.backward();
-
- // std::cout << dloss<< '\n';
-
- // arma::Mat<scalar> dL_dz2 = s.backward(dloss);
-
- // std::cout << dL_dz2<< '\n';
-
- // arma::Mat<scalar> dL_da1;
-
- // // dL_da1<<0.47375374<< -0.3361494<< 0.25611147<<-0.38332583<<arma::endr
- // // <<-0.2210031<< 0.15681155<< -0.11947437<< 0.17881905<<arma::endr
- // // <<-0.56355604<< 0.39986813<< -0.30465863<< 0.45598708<<arma::endr;
-
- // dL_da1<< 6.28919807e-01<< -3.52832568e-02<< 6.35791049e-01<< -2.30458563e-04<<arma::endr
- // <<-2.93387075e-01<< 1.64594141e-02<< -2.96592466e-01<< 1.07507449e-04<<arma::endr
- // <<-7.48134578e-01<< 4.19713676e-02<< -7.56308297e-01<< 2.74143091e-04<<arma::endr;
-
- // std::cout << dL_da1<< '\n';
-
- // // dL_da1 = linear_2.backward(dL_dz2)
+ cpp_frep::Circle c(1.5f,0.0f,0.0f);
+ // std::vector<unsigned char> targ1 =drawFrep(&c,minX, maxX, minY, maxY, dx, dy,"circle_target",false);
+ MatrixX targ1=Im.getTarget( &c,"circle");
+
+ cpp_frep::Rectangle r(-1.5f,1.5f,-1.0f,1.0f);
+ cpp_frep::Rotate rot1 (&r,0.0f,0.0f,90.0f);
+ // std::vector<unsigned char> targ2 =drawFrep(&rot1,minX, maxX, minY, maxY, dx, dy,"rectange_target",false);
+ MatrixX targ2=Im.getTarget( &rot1,"rectange");
+
+ cpp_frep::InvoluteGear gear( 1.0f, 1.0f, 1.1f, 20.0f, 8.0f) ;
+ cpp_frep::Rotate rot (&gear,0.0f,0.0f,15.0f);
+ cpp_frep::Scale sc1(&rot,0.0f,0.0f,0.5f,0.5f);
+ cpp_frep::Add target(&sc1,&c);
+ // std::vector<unsigned char> targ3 =drawFrep(&target,minX, maxX, minY, maxY, dx, dy,"gear_target",false);
+ MatrixX targ3=Im.getTarget( &target,"gear");
+
+
+ cpp_frep::Circle c1(0.9f,-0.5f,0.8f);
+ cpp_frep::Circle c2(0.8f,0.2f,-0.2f);
+ cpp_frep::Circle c3(0.9f,1.5f,0.0f);
+ cpp_frep::Circle c4(0.9f,1.7f,-1.1f);
+ // Subtract ss(&rot1,&c1);
+ // Subtract sss(&ss,&c2);
+ cpp_frep::Add aa(&c1,&c3);
+ cpp_frep::Add aaa(&aa,&c2);
+ cpp_frep::Add a(&aaa,&c4);
+ cpp_frep::Morph m (&a,&target,0.6f);
+ cpp_frep::Rotate rot2 (&m,0.3f,0.0f,-90.0f);
+ // Scale scc (&m,0.0f,0.0f,1.0,1.0);
+ // std::vector<unsigned char> targ4 =drawFrep(&rot2,minX, maxX, minY, maxY, dx, dy,"blob_target",false);
+ MatrixX targ4=Im.getTarget( &rot2,"blob");
+
+ cpp_frep::Circle c5(0.5f,0.0f,0.0f);
+ cpp_frep::Subtract ss(&c,&c5);
+ // std::vector<unsigned char> targ5 =drawFrep(&ss,minX, maxX, minY, maxY, dx, dy,"hole_target",false);
+ MatrixX targ5=Im.getTarget( &ss,"hole");
+
+ ////////////////////////////////////////////////////
+
+ //Create network
+ std::vector<Node*> network;
- // arma::Mat<scalar> dL_dz1 = t.backward(dL_da1);
-
- // std::cout << dL_dz1<< '\n';
-
- // arma::Mat<scalar> dL_dX = l1.backward(dL_dz1);
-
- // std::cout << dL_dX<< '\n';
-
- // l1.sgd_step(lrate);
- // std::cout << l1._W << '\n';
- // std::cout << l1._W0 << '\n';
-
- // l1.sgd_step(lrate);
- // std::cout << l1._W << '\n';
- // std::cout << l1._W0 << '\n';
-
- // dL_dX = linear_1.backward(dL_dz1)
- // unit_test('dL_dX', test_values['dL_dX'], dL_dX)
-
- // Sequential seq(network, &l);
- // std::cout <<seq.getWeightsNum()<<'\n';
- // std::cout <<seq.step(X,Y)<< '\n';
-
+ Linear l1(2, 12);
+ Tanh a1;
- // std::vector<Node*> network;
- // Linear l1(2, 3);
- // Sigmoid r;
- // Linear l2(3, 3);
- // Tanh t;
- // Tanh t1;
- // Linear l3(3, 2);
- // SoftMax s;
- // NLL l;
- // network.push_back( &l1);
- // network.push_back( &r);
- // // network.push_back( &l2);
- // // network.push_back( &t1);
- // network.push_back( &l3);
- // network.push_back( &s);
+ Linear l2(12, 12);
+ ReLU a2;
- // Sequential seq(network, &l);
- // std::cout <<seq.getWeightsNum()<<'\n';
- // std::cout <<seq.step(X,Y)<< '\n';
- arma::Mat<scalar> X;
- arma::Mat<scalar> Y;
+ Linear l3(12, 6);
+ ReLU a3;
- X <<-0.23390341<< 1.18151883<< -2.46493986<< 1.55322202<< 1.27621763<< 2.39710997<< -1.3440304<< -0.46903436<< -0.64673502<< -1.44029872<<
- -1.37537243<< 1.05994811<< -0.93311512<< 1.02735575<< -0.84138778<<-2.22585412<< -0.42591102<< 1.03561105<< 0.91125595<< -2.26550369<<arma::endr <<
- -0.92254932<< -1.1030963<< -2.41956036<< -1.15509002<< -1.04805327<< 0.08717325<< 0.8184725<< -0.75171045<< 0.60664705<< 0.80410947<<
- -0.11600488<< 1.03747218<< -0.67210575<< 0.99944446<< -0.65559838<<-0.40744784<< -0.58367642<< 1.0597278<< -0.95991874<< -1.41720255<<arma::endr;
+ Linear l4(6, 6);
+ Tanh a4;
- Y <<1.<< 1.<< 0.<< 1.<< 1.<< 1.<< 0.<< 0.<< 0.<< 0.<< 0.<< 1.<< 1.<< 1.<< 0.<< 0.<< 0.<< 1.<< 1.<< 0.<<arma::endr
- <<0.<< 0.<< 1.<< 0.<< 0.<< 0.<< 1.<< 1.<< 1.<< 1.<< 1.<< 0.<< 0.<< 0.<< 1.<< 1.<< 1.<< 0.<< 0.<< 1.<<arma::endr;
+ Linear l5(6, 2);
+ SoftMax a5;
- std::vector<Node*> network;
- Linear l1(2, 10);
- ReLU r1;
- Linear l2(10, 10);
- ReLU r2;
- Linear l3(10, 2);
- SoftMax s;
NLL l;
network.push_back( &l1);
- network.push_back( &r1);
+ network.push_back( &a1);
network.push_back( &l2);
- network.push_back( &r2);
+ network.push_back( &a2);
network.push_back( &l3);
- network.push_back( &s);
+ network.push_back( &a3);
+ network.push_back( &l4);
+ network.push_back( &a4);
+ network.push_back( &l5);
+ network.push_back( &a5);
- Sequential nn(network, &l);
+ Sequential seq(network, &l);
- // Sequential([Linear(2, 10), ReLU(), Linear(10, 10), ReLU(), Linear(10,2), SoftMax()], NLL())
+ ///////TARGET/////
+ MatrixX Y=targ5; /////////////////////////////////////////////target
+ Im.saveImage(seq.step(X,Y),"trial"); //test
+ std::cout<<"Number of Weights:"<<(int)seq.getWeightsNum()<<std::endl;
+
+ // ///////////////////////////// //Optimize neldermead
+ // unsigned numWeights=seq.getWeightsNum();
+ auto YY=Im.predict(Y);
+
+
+ ///////SGD/////
+
+ // unsigned epoch=2500;
+ // unsigned it=1000;//10000
+ // scalar lrate = 0.005;
+ // for (unsigned i=0;i<epoch;i++){
+ // if(i%25==0){
+ // auto Ypred=seq.step(X,Y);
+ // Im.saveImage(Ypred,std::to_string(i)); //test
+ // std::cout<<"iter:"<<i<<" score:"<<Im.getHamming(Ypred,YY)<<std::endl;
+ // }
+ // seq.sgd(X, Y, it, lrate);
+ // }
- unsigned it=10000;
- unsigned D= X.n_rows;
- unsigned N= X.n_cols;
- unsigned O= Y.n_rows;
- scalar lrate = 0.005;
+
// Modifies the weights and biases
- nn.sgd(X, Y, it, lrate);
-
- // Draw it...
- cout<<nn.predict(nn.forward(X))<<endl;
+ ////cmaes//
+ cmaes_t evo; /* an CMA-ES type struct or "object" */
+ double *arFunvals, *const*pop, *xfinal;
+ int i;
+ arFunvals = cmaes_init(&evo, 5, NULL, NULL, 0, 0, "cpp_dnn/cmaes_initials.par");
+ printf("%s\n", cmaes_SayHello(&evo));
+
+ cmaes_ReadSignals(&evo, "cmaes_signals.par"); /* write header and initial values */
+
+ /* Iterate until stop criterion holds */
+ while(!cmaes_TestForTermination(&evo))
+ {
+ /* generate lambda new search points, sample population */
+ pop = cmaes_SamplePopulation(&evo); /* do not change content of pop */
+
+ /* Here we may resample each solution point pop[i] until it
+ becomes feasible. function is_feasible(...) needs to be
+ user-defined.
+ Assumptions: the feasible domain is convex, the optimum is
+ not on (or very close to) the domain boundary, initialX is
+ feasible and initialStandardDeviations are sufficiently small
+ to prevent quasi-infinite looping. */
+ /* for (i = 0; i < cmaes_Get(&evo, "popsize"); ++i)
+ while (!is_feasible(pop[i]))
+ cmaes_ReSampleSingle(&evo, i);
+ */
+
+ /* evaluate the new search points using fitfun */
+ for (i = 0; i < cmaes_Get(&evo, "lambda"); ++i) {
+ arFunvals[i] = fitfun(pop[i], (int) cmaes_Get(&evo, "dim"),X,Y,seq);
+ }
+
+ /* update the search distribution used for cmaes_SamplePopulation() */
+ cmaes_UpdateDistribution(&evo, arFunvals);
+
+ /* read instructions for printing output or changing termination conditions */
+ cmaes_ReadSignals(&evo, "cmaes_signals.par");
+ fflush(stdout); /* useful in MinGW */
+ }
+ printf("Stop:\n%s\n", cmaes_TestForTermination(&evo)); /* print termination reason */
+ cmaes_WriteToFile(&evo, "all", "allcmaes.dat"); /* write final results */
- cout<<"hereeee "<<endl;
+ /* get best estimator for the optimum, xmean */
+ xfinal = cmaes_GetNew(&evo, "xmean"); /* "xbestever" might be used as well */
+ cmaes_exit(&evo); /* release memory */
+ /* do something with final solution and finally release memory */
+ std::cout<<*xfinal<<'\n';
- // X = np.array([[2, 3, 9, 12],
- // [5, 1, 6, 5]])
-
- // y = np.array([[1, 0, 1, 0]])
- // return X, for_softmax(y)
+ free(xfinal);
+ seq.setWeights( pointerToVector(pop[0], (int) cmaes_Get(&evo, "dim")));
+ Im.saveImage(seq.step(X,Y),"trialll");
+
+
+
return 0;
}
-// void oldCode(){
-// //Create Image
-// Image Im;
-// MatrixX X=Im.getLocMatrix();
-// cpp_frep::Circle frep(1.5f,0.0f,0.0f);
-// MatrixX Y=Im.getTarget( &frep ,"Circle");
-// // Im.saveImage(seq.step(X,Y),"trial"); //test
-
-// ///////////////////////////// //Optimize neldermead
-// unsigned numWeights=seq.getWeightsNum();
-
-// // std::vector<scalar> v=getRandomWeights(numWeights);
-// // std::cout << seq.sumLoss()<< '\n';
-// // seq.setWeights(v);
-// // Im.saveImage(seq.step(X,Y),"trial1"); //test
-// // std::cout << seq.sumLoss()<< '\n';
-
-
-// // float precision = 0.001;
-// // int dimension = (int)numWeights;
-// // unsigned maxSteps=1000;
-// // NelderMeadOptimizer o(dimension, precision);
-// // std::cout << dimension<< '\n';
-
-// // request a simplex to start with
-// // Vector v(getRandomWeights(numWeights));
-// // Vector v1(getRandomWeights(numWeights));
-// // Vector v2(getRandomWeights(numWeights));
-// // o.insert(v);
-// // o.insert(v1);
-// // o.insert(v2);
-// // unsigned count=0;
-
-// // while (!o.done() && count<maxSteps) {
-// // seq.setWeights(v.get());
-// // Im.saveImage(seq.step(X,Y),"trial"+std::to_string(count));
-// // v = o.step(v, -seq.sumLoss()); //later change f(v)
-// // count++;
-
-// // }
-
-// ///////////////////////////// //Optimize CMAES
-// cmaes_t evo; /* an CMA-ES type struct or "object" */
-// double *arFunvals, *const*pop, *xfinal;
-// int i;
-// arFunvals = cmaes_init(&evo, 5, NULL, NULL, 0, 0, "cpp_dnn/cmaes_initials.par");
-// printf("%s\n", cmaes_SayHello(&evo));
-
-// cmaes_ReadSignals(&evo, "cmaes_signals.par"); /* write header and initial values */
-
-// /* Iterate until stop criterion holds */
-// while(!cmaes_TestForTermination(&evo))
-// {
-// /* generate lambda new search points, sample population */
-// pop = cmaes_SamplePopulation(&evo); /* do not change content of pop */
-
-// /* Here we may resample each solution point pop[i] until it
-// becomes feasible. function is_feasible(...) needs to be
-// user-defined.
-// Assumptions: the feasible domain is convex, the optimum is
-// not on (or very close to) the domain boundary, initialX is
-// feasible and initialStandardDeviations are sufficiently small
-// to prevent quasi-infinite looping. */
-// /* for (i = 0; i < cmaes_Get(&evo, "popsize"); ++i)
-// while (!is_feasible(pop[i]))
-// cmaes_ReSampleSingle(&evo, i);
-// */
-
-// /* evaluate the new search points using fitfun */
-// for (i = 0; i < cmaes_Get(&evo, "lambda"); ++i) {
-// arFunvals[i] = fitfun(pop[i], (int) cmaes_Get(&evo, "dim"),X,Y,seq);
-// }
-
-// /* update the search distribution used for cmaes_SamplePopulation() */
-// cmaes_UpdateDistribution(&evo, arFunvals);
-
-// /* read instructions for printing output or changing termination conditions */
-// cmaes_ReadSignals(&evo, "cmaes_signals.par");
-// fflush(stdout); /* useful in MinGW */
-// }
-// printf("Stop:\n%s\n", cmaes_TestForTermination(&evo)); /* print termination reason */
-// cmaes_WriteToFile(&evo, "all", "allcmaes.dat"); /* write final results */
-
-// /* get best estimator for the optimum, xmean */
-// xfinal = cmaes_GetNew(&evo, "xmean"); /* "xbestever" might be used as well */
-// cmaes_exit(&evo); /* release memory */
-
-// /* do something with final solution and finally release memory */
-// std::cout<<*xfinal<<'\n';
-
-// free(xfinal);
-// seq.setWeights( pointerToVector(pop[0], (int) cmaes_Get(&evo, "dim")));
-// Im.saveImage(seq.step(X,Y),"trialll");
-
-// }
+//dnn_sgd target 1
+ // iter:0 score:7266
+ // iter:25 score:286
+ // iter:50 score:302
+ // iter:75 score:182
+ // iter:100 score:264
+ // iter:125 score:390
+ // iter:150 score:83
+ // iter:175 score:125
+ // iter:200 score:187
+ // iter:225 score:189
+ // iter:250 score:188
+ // iter:275 score:164
+ // iter:300 score:151
+ // iter:325 score:98
+ // iter:350 score:74
+ // iter:375 score:118
+ // iter:400 score:175
+ // iter:425 score:59
+ // iter:450 score:103
+ // iter:475 score:126
+ // iter:500 score:88
+ // iter:525 score:79
+ // iter:550 score:110
+ // iter:575 score:98
+ // iter:600 score:126
+ // iter:625 score:153
+ // iter:650 score:149
+ // iter:675 score:76
+ // iter:700 score:65
+ // iter:725 score:70
+ // iter:750 score:76
+ // iter:775 score:50
+ // iter:800 score:68
+ // iter:825 score:89
+ // iter:850 score:141
+ // iter:875 score:142
+ // iter:900 score:154
+ // iter:925 score:47
+ // iter:950 score:75
+ // iter:975 score:108
+ // iter:1000 score:53
+ // iter:1025 score:69
+ // iter:1050 score:120
+ // iter:1075 score:62
+ // iter:1100 score:70
+ // iter:1125 score:59
+ // iter:1150 score:122
+ // iter:1175 score:107
+ // iter:1200 score:84
+ // iter:1225 score:77
+ // iter:1250 score:67
+ // iter:1275 score:86
+ // iter:1300 score:76
+ // iter:1325 score:139
+ // iter:1350 score:43
+ // iter:1375 score:119
+ // iter:1400 score:113
+ // iter:1425 score:68
+ // iter:1450 score:63
+ // iter:1475 score:44
+ // iter:1500 score:163
+ // iter:1525 score:50
+ // iter:1550 score:118
+ // iter:1575 score:65
+ // iter:1600 score:72
+ // iter:1625 score:93
+ // iter:1650 score:33
+ // iter:1675 score:44
+ // iter:1700 score:52
+ // iter:1725 score:107
+ // iter:1750 score:67
+ // iter:1775 score:34
+ // iter:1800 score:66
+ // iter:1825 score:81
+ // iter:1850 score:47
+ // iter:1875 score:43
+ // iter:1900 score:63
+ // iter:1925 score:45
+ // iter:1950 score:43
+ // iter:1975 score:79
+ // iter:2000 score:53
+ // iter:2025 score:63
+ // iter:2050 score:52
+ // iter:2075 score:83
+ // iter:2100 score:62
+ // iter:2125 score:90
+ // iter:2150 score:61
+ // iter:2175 score:95
+ // iter:2200 score:77
+ // iter:2225 score:79
+ // iter:2250 score:85
+ // iter:2275 score:57
+ // iter:2300 score:89
+ // iter:2325 score:36
+ // iter:2350 score:56
+ // iter:2375 score:30
+ // iter:2400 score:54
+ // iter:2425 score:140
+ // iter:2450 score:52
+ // iter:2475 score:69
+//dnn_sgd target 2
+ // Number of Weights:326
+ // iter:0 score:7760
+ // iter:25 score:459
+ // iter:50 score:270
+ // iter:75 score:344
+ // iter:100 score:142
+ // iter:125 score:138
+ // iter:150 score:203
+ // iter:175 score:264
+ // iter:200 score:137
+ // iter:225 score:98
+ // iter:250 score:84
+ // iter:275 score:202
+ // iter:300 score:134
+ // iter:325 score:106
+ // iter:350 score:62
+ // iter:375 score:88
+ // iter:400 score:133
+ // iter:425 score:106
+ // iter:450 score:91
+ // iter:475 score:227
+ // iter:500 score:201
+ // iter:525 score:141
+ // iter:550 score:90
+ // iter:575 score:36
+ // iter:600 score:105
+ // iter:625 score:106
+ // iter:650 score:80
+ // iter:675 score:67
+ // iter:700 score:26
+ // iter:725 score:59
+ // iter:750 score:69
+ // iter:775 score:45
+ // iter:800 score:85
+ // iter:825 score:97
+ // iter:850 score:32
+ // iter:875 score:42
+ // iter:900 score:109
+ // iter:925 score:43
+ // iter:950 score:77
+ // iter:975 score:59
+ // iter:1000 score:73
+ // iter:1025 score:68
+ // iter:1050 score:101
+ // iter:1075 score:105
+ // iter:1100 score:46
+ // iter:1125 score:19
+ // iter:1150 score:96
+ // iter:1175 score:36
+ // iter:1200 score:56
+ // iter:1225 score:35
+ // iter:1250 score:32
+ // iter:1275 score:64
+ // iter:1300 score:70
+ // iter:1325 score:33
+ // iter:1350 score:20
+ // iter:1375 score:43
+ // iter:1400 score:35
+ // iter:1425 score:19
+ // iter:1450 score:33
+ // iter:1475 score:27
+ // iter:1500 score:18
+ // iter:1525 score:47
+ // iter:1550 score:16
+ // iter:1575 score:19
+ // iter:1600 score:6
+ // iter:1625 score:32
+ // iter:1650 score:16
+ // iter:1675 score:61
+ // iter:1700 score:22
+ // iter:1725 score:8
+ // iter:1750 score:15
+ // iter:1775 score:23
+ // iter:1800 score:10
+ // iter:1825 score:6
+ // iter:1850 score:29
+ // iter:1875 score:21
+ // iter:1900 score:10
+ // iter:1925 score:9
+ // iter:1950 score:4
+ // iter:1975 score:5
+ // iter:2000 score:23
+ // iter:2025 score:67
+ // iter:2050 score:111
+ // iter:2075 score:34
+ // iter:2100 score:6
+ // iter:2125 score:29
+ // iter:2150 score:18
+ // iter:2175 score:48
+ // iter:2200 score:5
+ // iter:2225 score:12
+ // iter:2250 score:10
+ // iter:2275 score:24
+ // iter:2300 score:14
+ // iter:2325 score:1
+ // iter:2350 score:8
+ // iter:2375 score:2
+ // iter:2400 score:17
+ // iter:2425 score:20
+ // iter:2450 score:3
+ // iter:2475 score:5
+//dnn_sgd target 3
+ // Number of Weights:326
+ // iter:0 score:5168
+ // iter:25 score:1175
+ // iter:50 score:1110
+ // iter:75 score:1031
+ // iter:100 score:1106
+ // iter:125 score:872
+ // iter:150 score:989
+ // iter:175 score:745
+ // iter:200 score:905
+ // iter:225 score:825
+ // iter:250 score:665
+ // iter:275 score:694
+ // iter:300 score:903
+ // iter:325 score:692
+ // iter:350 score:792
+ // iter:375 score:767
+ // iter:400 score:695
+ // iter:425 score:633
+ // iter:450 score:699
+ // iter:475 score:719
+ // iter:500 score:642
+ // iter:525 score:704
+ // iter:550 score:634
+ // iter:575 score:547
+ // iter:600 score:581
+ // iter:625 score:541
+ // iter:650 score:723
+ // iter:675 score:610
+ // iter:700 score:658
+ // iter:725 score:595
+ // iter:750 score:595
+ // iter:775 score:669
+ // iter:800 score:672
+ // iter:825 score:723
+ // iter:850 score:834
+ // iter:875 score:593
+ // iter:900 score:609
+ // iter:925 score:582
+ // iter:950 score:592
+ // iter:975 score:511
+ // iter:1000 score:579
+ // iter:1025 score:537
+ // iter:1050 score:536
+ // iter:1075 score:512
+ // iter:1100 score:606
+ // iter:1125 score:571
+ // iter:1150 score:532
+ // iter:1175 score:607
+ // iter:1200 score:606
+ // iter:1225 score:496
+ // iter:1250 score:492
+ // iter:1275 score:583
+ // iter:1300 score:621
+ // iter:1325 score:521
+ // iter:1350 score:672
+ // iter:1375 score:643
+ // iter:1400 score:607
+ // iter:1425 score:551
+ // iter:1450 score:540
+ // iter:1475 score:573
+ // iter:1500 score:622
+ // iter:1525 score:499
+ // iter:1550 score:510
+ // iter:1575 score:584
+ // iter:1600 score:542
+ // iter:1625 score:541
+ // iter:1650 score:573
+ // iter:1675 score:607
+ // iter:1700 score:546
+ // iter:1725 score:661
+ // iter:1750 score:548
+ // iter:1775 score:533
+ // iter:1800 score:695
+ // iter:1825 score:592
+ // iter:1850 score:553
+ // iter:1875 score:557
+ // iter:1900 score:590
+ // iter:1925 score:515
+ // iter:1950 score:559
+ // iter:1975 score:514
+ // iter:2000 score:490
+ // iter:2025 score:539
+ // iter:2050 score:591
+ // iter:2075 score:517
+ // iter:2100 score:547
+ // iter:2125 score:560
+ // iter:2150 score:554
+ // iter:2175 score:468
+ // iter:2200 score:547
+ // iter:2225 score:559
+ // iter:2250 score:551
+ // iter:2275 score:651
+ // iter:2300 score:582
+ // iter:2325 score:617
+ // iter:2350 score:606
+ // iter:2375 score:550
+ // iter:2400 score:506
+ // iter:2425 score:837
+ // iter:2450 score:534
+ // iter:2475 score:566
+//dnn_sgd target 4
+ // width: 99, height: 99
+ // Number of Weights:326
+ // iter:0 score:6479
+ // iter:25 score:484
+ // iter:50 score:220
+ // iter:75 score:244
+ // iter:100 score:350
+ // iter:125 score:325
+ // iter:150 score:374
+ // iter:175 score:303
+ // iter:200 score:425
+ // iter:225 score:184
+ // iter:250 score:248
+ // iter:275 score:229
+ // iter:300 score:197
+ // iter:325 score:252
+ // iter:350 score:316
+ // iter:375 score:222
+ // iter:400 score:194
+ // iter:425 score:134
+ // iter:450 score:201
+ // iter:475 score:146
+ // iter:500 score:166
+ // iter:525 score:187
+ // iter:550 score:198
+ // iter:575 score:237
+ // iter:600 score:185
+ // iter:625 score:154
+ // iter:650 score:173
+ // iter:675 score:364
+ // iter:700 score:155
+ // iter:725 score:381
+ // iter:750 score:165
+ // iter:775 score:224
+ // iter:800 score:169
+ // iter:825 score:157
+ // iter:850 score:182
+ // iter:875 score:163
+ // iter:900 score:154
+ // iter:925 score:143
+ // iter:950 score:201
+ // iter:975 score:291
+ // iter:1000 score:198
+ // iter:1025 score:161
+ // iter:1050 score:131
+ // iter:1075 score:188
+ // iter:1100 score:185
+ // iter:1125 score:132
+ // iter:1150 score:144
+ // iter:1175 score:217
+ // iter:1200 score:210
+ // iter:1225 score:160
+ // iter:1250 score:183
+ // iter:1275 score:202
+ // iter:1300 score:257
+ // iter:1325 score:210
+ // iter:1350 score:163
+ // iter:1375 score:118
+ // iter:1400 score:197
+ // iter:1425 score:160
+ // iter:1450 score:158
+ // iter:1475 score:166
+ // iter:1500 score:154
+ // iter:1525 score:162
+ // iter:1550 score:211
+ // iter:1575 score:150
+ // iter:1600 score:164
+ // iter:1625 score:193
+ // iter:1650 score:123
+ // iter:1675 score:146
+ // iter:1700 score:105
+ // iter:1725 score:185
+ // iter:1750 score:234
+ // iter:1775 score:101
+ // iter:1800 score:136
+ // iter:1825 score:122
+ // iter:1850 score:115
+ // iter:1875 score:199
+ // iter:1900 score:121
+ // iter:1925 score:130
+ // iter:1950 score:180
+ // iter:1975 score:85
+ // iter:2000 score:154
+ // iter:2025 score:154
+ // iter:2050 score:155
+ // iter:2075 score:161
+ // iter:2100 score:169
+ // iter:2125 score:132
+ // iter:2150 score:165
+ // iter:2175 score:126
+ // iter:2200 score:123
+ // iter:2225 score:115
+ // iter:2250 score:143
+ // iter:2275 score:145
+ // iter:2300 score:166
+ // iter:2325 score:119
+ // iter:2350 score:252
+ // iter:2375 score:85
+ // iter:2400 score:128
+ // iter:2425 score:151
+ // iter:2450 score:160
+ // iter:2475 score:119
+//dnn_sgd target 5
+ // Number of Weights:326
+ // iter:0 score:7535
+ // iter:25 score:516
+ // iter:50 score:376
+ // iter:75 score:350
+ // iter:100 score:295
+ // iter:125 score:301
+ // iter:150 score:218
+ // iter:175 score:255
+ // iter:200 score:196
+ // iter:225 score:249
+ // iter:250 score:195
+ // iter:275 score:164
+ // iter:300 score:176
+ // iter:325 score:209
+ // iter:350 score:225
+ // iter:375 score:157
+ // iter:400 score:183
+ // iter:425 score:188
+ // iter:450 score:182
+ // iter:475 score:244
+ // iter:500 score:81
+ // iter:525 score:84
+ // iter:550 score:122
+ // iter:575 score:121
+ // iter:600 score:150
+ // iter:625 score:162
+ // iter:650 score:209
+ // iter:675 score:244
+ // iter:700 score:135
+ // iter:725 score:93
+ // iter:750 score:117
+ // iter:775 score:78
+ // iter:800 score:114
+ // iter:825 score:112
+ // iter:850 score:131
+ // iter:875 score:183
+ // iter:900 score:145
+ // iter:925 score:113
+ // iter:950 score:96
+ // iter:975 score:87
+ // iter:1000 score:88
+ // iter:1025 score:112
+ // iter:1050 score:104
+ // iter:1075 score:110
+ // iter:1100 score:92
+ // iter:1125 score:131
+ // iter:1150 score:97
+ // iter:1175 score:176
+ // iter:1200 score:110
+ // iter:1225 score:93
+ // iter:1250 score:114
+ // iter:1275 score:73
+ // iter:1300 score:90
+ // iter:1325 score:117
+ // iter:1350 score:97
+ // iter:1375 score:163
+ // iter:1400 score:166
+ // iter:1425 score:111
+ // iter:1450 score:102
+ // iter:1475 score:117
+ // iter:1500 score:88
+ // iter:1525 score:78
+ // iter:1550 score:119
+ // iter:1575 score:92
+ // iter:1600 score:115
+ // iter:1625 score:169
+ // iter:1650 score:71
+ // iter:1675 score:77
+ // iter:1700 score:92
+ // iter:1725 score:100
+ // iter:1750 score:117
+ // iter:1775 score:81
+ // iter:1800 score:97
+ // iter:1825 score:93
+ // iter:1850 score:79
+ // iter:1875 score:84
+ // iter:1900 score:82
+ // iter:1925 score:86
+ // iter:1950 score:87
+ // iter:1975 score:119
+ // iter:2000 score:106
+ // iter:2025 score:124
+ // iter:2050 score:90
+ // iter:2075 score:111
+ // iter:2100 score:132
+ // iter:2125 score:78
+ // iter:2150 score:68
+ // iter:2175 score:98
+ // iter:2200 score:102
+ // iter:2225 score:86
+ // iter:2250 score:91
+ // iter:2275 score:84
+ // iter:2300 score:95
+ // iter:2325 score:78
+ // iter:2350 score:105
+ // iter:2375 score:68
+ // iter:2400 score:152
+ // iter:2425 score:82
+ // iter:2450 score:83
+ // iter:2475 score:103
+
+
+//dnn_cmaes target 1
+ // iter:0 score:7191
+ // iter:100 score:2809
+ // iter:200 score:2809
+ // iter:300 score:3091
+ // iter:400 score:3515
+ // iter:500 score:2859
+ // iter:600 score:2629
+ // iter:700 score:1527
+ // iter:800 score:1445
+ // iter:900 score:1498
+ // iter:1000 score:2193
+ // iter:1100 score:2199
+ // iter:1200 score:1046
+ // iter:1300 score:1008
+ // iter:1400 score:1141
+ // iter:1500 score:1034
+ // iter:1600 score:1113
+ // iter:1700 score:908
+ // iter:1800 score:1668
+ // iter:1900 score:1101
+ // iter:2000 score:870
+ // iter:2100 score:652
+ // iter:2200 score:1490
+ // iter:2300 score:1479
+ // iter:2400 score:725
+ // iter:2500 score:797
+ // iter:2600 score:744
+ // iter:2700 score:556
+ // iter:2800 score:849
+ // iter:2900 score:1052
+ // iter:3000 score:2033
+ // iter:3100 score:889
+ // iter:3200 score:949
+ // iter:3300 score:1250
+ // iter:3400 score:649
+ // iter:3500 score:862
+ // iter:3600 score:1601
+ // iter:3700 score:503
+ // iter:3800 score:453
+ // iter:3900 score:456
+ // iter:4000 score:555
+ // iter:4100 score:915
+ // iter:4200 score:616
+ // iter:4300 score:561
+ // iter:4400 score:1006
+ // iter:4500 score:480
+ // iter:4600 score:541
+ // iter:4700 score:442
+ // iter:4800 score:433
+ // iter:4900 score:535
+ // iter:5000 score:1155
+ // iter:5100 score:445
+ // iter:5200 score:505
+ // iter:5300 score:523
+ // iter:5400 score:560
+ // iter:5500 score:489
+ // iter:5600 score:569
+ // iter:5700 score:425
+ // iter:5800 score:958
+ // iter:5900 score:543
+ // iter:6000 score:422
+ // iter:6100 score:464
+ // iter:6200 score:551
+ // iter:6300 score:628
+ // iter:6400 score:543
+ // iter:6500 score:585
+ // iter:6600 score:436
+ // iter:6700 score:382
+ // iter:6800 score:385
+ // iter:6900 score:353
+ // iter:7000 score:512
+ // iter:7100 score:412
+ // iter:7200 score:519
+ // iter:7300 score:268
+ // iter:7400 score:2101
+ // iter:7500 score:426
+ // iter:7600 score:361
+ // iter:7700 score:301
+ // iter:7800 score:414
+ // iter:7900 score:335
+ // iter:8000 score:507
+ // iter:8100 score:317
+ // iter:8200 score:387
+ // iter:8300 score:325
+ // iter:8400 score:245
+ // iter:8500 score:394
+ // iter:8600 score:408
+ // iter:8700 score:285
+ // iter:8800 score:246
+ // iter:8900 score:769
+ // iter:9000 score:365
+ // iter:9100 score:281
+ // iter:9200 score:254
+ // iter:9300 score:325
+ // iter:9400 score:388
+ // iter:9500 score:380
+ // iter:9600 score:219
+ // iter:9700 score:291
+ // iter:9800 score:273
+ // iter:9900 score:333
+
+//dnn_cmaes target 2
+ // iter:0 score:7667
+ // iter:100 score:2333
+ // iter:200 score:3545
+ // iter:300 score:2288
+ // iter:400 score:2677
+ // iter:500 score:2320
+ // iter:600 score:2012
+ // iter:700 score:2441
+ // iter:800 score:1645
+ // iter:900 score:2071
+ // iter:1000 score:2333
+ // iter:1100 score:3033
+ // iter:1200 score:1211
+ // iter:1300 score:1150
+ // iter:1400 score:1299
+ // iter:1500 score:618
+ // iter:1600 score:7659
+ // iter:1700 score:1016
+ // iter:1800 score:1656
+ // iter:1900 score:1206
+ // iter:2000 score:1612
+ // iter:2100 score:1639
+ // iter:2200 score:1738
+ // iter:2300 score:957
+ // iter:2400 score:2271
+ // iter:2500 score:845
+ // iter:2600 score:1154
+ // iter:2700 score:2330
+ // iter:2800 score:1548
+ // iter:2900 score:842
+ // iter:3000 score:1151
+ // iter:3100 score:743
+ // iter:3200 score:2299
+ // iter:3300 score:567
+ // iter:3400 score:708
+ // iter:3500 score:540
+ // iter:3600 score:1110
+ // iter:3700 score:850
+ // iter:3800 score:488
+ // iter:3900 score:625
+ // iter:4000 score:644
+ // iter:4100 score:923
+ // iter:4200 score:1773
+ // iter:4300 score:389
+ // iter:4400 score:1115
+ // iter:4500 score:564
+ // iter:4600 score:487
+ // iter:4700 score:748
+ // iter:4800 score:755
+ // iter:4900 score:817
+ // iter:5000 score:718
+ // iter:5100 score:602
+ // iter:5200 score:590
+ // iter:5300 score:571
+ // iter:5400 score:863
+ // iter:5500 score:580
+ // iter:5600 score:410
+ // iter:5700 score:420
+ // iter:5800 score:1180
+ // iter:5900 score:683
+ // iter:6000 score:405
+ // iter:6100 score:294
+ // iter:6200 score:594
+ // iter:6300 score:265
+ // iter:6400 score:473
+ // iter:6500 score:561
+ // iter:6600 score:321
+ // iter:6700 score:506
+ // iter:6800 score:382
+ // iter:6900 score:253
+ // iter:7000 score:213
+ // iter:7100 score:571
+ // iter:7200 score:447
+ // iter:7300 score:403
+ // iter:7400 score:828
+ // iter:7500 score:183
+ // iter:7600 score:292
+ // iter:7700 score:385
+ // iter:7800 score:335
+ // iter:7900 score:863
+ // iter:8000 score:7475
+ // iter:8100 score:358
+ // iter:8200 score:339
+ // iter:8300 score:296
+ // iter:8400 score:193
+ // iter:8500 score:223
+ // iter:8600 score:150
+ // iter:8700 score:231
+ // iter:8800 score:347
+ // iter:8900 score:238
+ // iter:9000 score:564
+ // iter:9100 score:1489
+ // iter:9200 score:583
+ // iter:9300 score:287
+ // iter:9400 score:144
+ // iter:9500 score:214
+ // iter:9600 score:844
+ // iter:9700 score:244
+ // iter:9800 score:205
+ // iter:9900 score:293
+
+//dnn_cmaes target 3
+ // iter:0 score:4978
+ // iter:100 score:4505
+ // iter:200 score:3940
+ // iter:300 score:4155
+ // iter:400 score:3182
+ // iter:500 score:4082
+ // iter:600 score:3220
+ // iter:700 score:3862
+ // iter:800 score:2531
+ // iter:900 score:2587
+ // iter:1000 score:2382
+ // iter:1100 score:2284
+ // iter:1200 score:3236
+ // iter:1300 score:2925
+ // iter:1400 score:3395
+ // iter:1500 score:2708
+ // iter:1600 score:2866
+ // iter:1700 score:2339
+ // iter:1800 score:3431
+ // iter:1900 score:2518
+ // iter:2000 score:2686
+ // iter:2100 score:2005
+ // iter:2200 score:2502
+ // iter:2300 score:2039
+ // iter:2400 score:2573
+ // iter:2500 score:2158
+ // iter:2600 score:2220
+ // iter:2700 score:3201
+ // iter:2800 score:2396
+ // iter:2900 score:2666
+ // iter:3000 score:2115
+ // iter:3100 score:2310
+ // iter:3200 score:2642
+ // iter:3300 score:2457
+ // iter:3400 score:2469
+ // iter:3500 score:2318
+ // iter:3600 score:2487
+ // iter:3700 score:2140
+ // iter:3800 score:2007
+ // iter:3900 score:2665
+ // iter:4000 score:2054
+ // iter:4100 score:1915
+ // iter:4200 score:2116
+ // iter:4300 score:2198
+ // iter:4400 score:2009
+ // iter:4500 score:2228
+ // iter:4600 score:2975
+ // iter:4700 score:2802
+ // iter:4800 score:1998
+ // iter:4900 score:2767
+ // iter:5000 score:1869
+ // iter:5100 score:2063
+ // iter:5200 score:1799
+ // iter:5300 score:2035
+ // iter:5400 score:1827
+ // iter:5500 score:1649
+ // iter:5600 score:1709
+ // iter:5700 score:2662
+ // iter:5800 score:1669
+ // iter:5900 score:1738
+ // iter:6000 score:1524
+ // iter:6100 score:1636
+ // iter:6200 score:1774
+ // iter:6300 score:1601
+ // iter:6400 score:1594
+ // iter:6500 score:1496
+ // iter:6600 score:1514
+ // iter:6700 score:1195
+ // iter:6800 score:1391
+ // iter:6900 score:1420
+ // iter:7000 score:1356
+ // iter:7100 score:1385
+ // iter:7200 score:1117
+ // iter:7300 score:1182
+ // iter:7400 score:1266
+ // iter:7500 score:1985
+ // iter:7600 score:1427
+ // iter:7700 score:1465
+ // iter:7800 score:1162
+ // iter:7900 score:1212
+ // iter:8000 score:1171
+ // iter:8100 score:1269
+ // iter:8200 score:1363
+ // iter:8300 score:1244
+ // iter:8400 score:1288
+ // iter:8500 score:1084
+ // iter:8600 score:1005
+ // iter:8700 score:1335
+ // iter:8800 score:1179
+ // iter:8900 score:967
+ // iter:9000 score:1049
+ // iter:9100 score:1043
+ // iter:9200 score:1015
+ // iter:9300 score:927
+ // iter:9400 score:1060
+ // iter:9500 score:973
+ // iter:9600 score:953
+ // iter:9700 score:915
+ // iter:9800 score:1064
+ // iter:9900 score:1032
+
+//dnn_cmaes target 4
+ // iter:0 score:3654
+ // iter:100 score:3662
+ // iter:200 score:3761
+ // iter:300 score:2381
+ // iter:400 score:3166
+ // iter:500 score:2930
+ // iter:600 score:3159
+ // iter:700 score:3242
+ // iter:800 score:1955
+ // iter:900 score:2193
+ // iter:1000 score:2459
+ // iter:1100 score:1792
+ // iter:1200 score:1677
+ // iter:1300 score:2123
+ // iter:1400 score:1566
+ // iter:1500 score:2084
+ // iter:1600 score:1739
+ // iter:1700 score:1559
+ // iter:1800 score:2688
+ // iter:1900 score:3921
+ // iter:2000 score:1308
+ // iter:2100 score:2527
+ // iter:2200 score:1826
+ // iter:2300 score:1617
+ // iter:2400 score:1653
+ // iter:2500 score:2763
+ // iter:2600 score:1414
+ // iter:2700 score:1603
+ // iter:2800 score:1159
+ // iter:2900 score:1680
+ // iter:3000 score:1256
+ // iter:3100 score:1563
+ // iter:3200 score:1615
+ // iter:3300 score:1537
+ // iter:3400 score:2101
+ // iter:3500 score:1629
+ // iter:3600 score:1231
+ // iter:3700 score:1288
+ // iter:3800 score:2119
+ // iter:3900 score:794
+ // iter:4000 score:888
+ // iter:4100 score:874
+ // iter:4200 score:1019
+ // iter:4300 score:1890
+ // iter:4400 score:1564
+ // iter:4500 score:1140
+ // iter:4600 score:1536
+ // iter:4700 score:1118
+ // iter:4800 score:1006
+ // iter:4900 score:1492
+ // iter:5000 score:1186
+ // iter:5100 score:980
+ // iter:5200 score:1271
+ // iter:5300 score:918
+ // iter:5400 score:769
+ // iter:5500 score:765
+ // iter:5600 score:886
+ // iter:5700 score:952
+ // iter:5800 score:1087
+ // iter:5900 score:1911
+ // iter:6000 score:954
+ // iter:6100 score:2255
+ // iter:6200 score:903
+ // iter:6300 score:674
+ // iter:6400 score:1305
+ // iter:6500 score:755
+ // iter:6600 score:696
+ // iter:6700 score:1306
+ // iter:6800 score:636
+ // iter:6900 score:660
+ // iter:7000 score:644
+ // iter:7100 score:786
+ // iter:7200 score:675
+ // iter:7300 score:708
+ // iter:7400 score:689
+ // iter:7500 score:1006
+ // iter:7600 score:563
+ // iter:7700 score:1100
+ // iter:7800 score:802
+ // iter:7900 score:753
+ // iter:8000 score:2403
+ // iter:8100 score:1479
+ // iter:8200 score:683
+ // iter:8300 score:736
+ // iter:8400 score:919
+ // iter:8500 score:1132
+ // iter:8600 score:1125
+ // iter:8700 score:763
+ // iter:8800 score:679
+ // iter:8900 score:2588
+ // iter:9000 score:637
+ // iter:9100 score:547
+ // iter:9200 score:660
+ // iter:9300 score:598
+ // iter:9400 score:499
+ // iter:9500 score:648
+ // iter:9600 score:805
+ // iter:9700 score:589
+ // iter:9800 score:577
+ // iter:9900 score:426
+
+
+//dnn_cmaes target 5
+ // iter:0 score:2496
+ // iter:100 score:7168
+ // iter:200 score:3541
+ // iter:300 score:2496
+ // iter:400 score:2496
+ // iter:500 score:6930
+ // iter:600 score:2602
+ // iter:700 score:2182
+ // iter:800 score:2555
+ // iter:900 score:2200
+ // iter:1000 score:3032
+ // iter:1100 score:2509
+ // iter:1200 score:1907
+ // iter:1300 score:1459
+ // iter:1400 score:2356
+ // iter:1500 score:1897
+ // iter:1600 score:1724
+ // iter:1700 score:1491
+ // iter:1800 score:1731
+ // iter:1900 score:997
+ // iter:2000 score:1302
+ // iter:2100 score:1409
+ // iter:2200 score:1380
+ // iter:2300 score:1426
+ // iter:2400 score:1360
+ // iter:2500 score:978
+ // iter:2600 score:1006
+ // iter:2700 score:1147
+ // iter:2800 score:1110
+ // iter:2900 score:1251
+ // iter:3000 score:1530
+ // iter:3100 score:1143
+ // iter:3200 score:1082
+ // iter:3300 score:1072
+ // iter:3400 score:804
+ // iter:3500 score:877
+ // iter:3600 score:768
+ // iter:3700 score:1062
+ // iter:3800 score:771
+ // iter:3900 score:945
+ // iter:4000 score:1151
+ // iter:4100 score:957
+ // iter:4200 score:1349
+ // iter:4300 score:805
+ // iter:4400 score:965
+ // iter:4500 score:898
+ // iter:4600 score:817
+ // iter:4700 score:1461
+ // iter:4800 score:910
+ // iter:4900 score:1547
+ // iter:5000 score:7044
+ // iter:5100 score:850
+ // iter:5200 score:1996
+ // iter:5300 score:910
+ // iter:5400 score:1347
+ // iter:5500 score:803
+ // iter:5600 score:854
+ // iter:5700 score:725
+ // iter:5800 score:803
+ // iter:5900 score:805
+ // iter:6000 score:744
+ // iter:6100 score:922
+ // iter:6200 score:879
+ // iter:6300 score:701
+ // iter:6400 score:760
+ // iter:6500 score:1082
+ // iter:6600 score:1482
+ // iter:6700 score:821
+ // iter:6800 score:667
+ // iter:6900 score:843
+ // iter:7000 score:893
+ // iter:7100 score:1179
+ // iter:7200 score:852
+ // iter:7300 score:728
+ // iter:7400 score:777
+ // iter:7500 score:638
+ // iter:7600 score:716
+ // iter:7700 score:1334
+ // iter:7800 score:669
+ // iter:7900 score:633
+ // iter:8000 score:673
+ // iter:8100 score:545
+ // iter:8200 score:589
+ // iter:8300 score:602
+ // iter:8400 score:644
+ // iter:8500 score:787
+ // iter:8600 score:580
+ // iter:8700 score:751
+ // iter:8800 score:678
+ // iter:8900 score:551
+ // iter:9000 score:608
+ // iter:9100 score:699
+ // iter:9200 score:5326
+ // iter:9300 score:745
+ // iter:9400 score:682
+ // iter:9500 score:576
+ // iter:9600 score:975
+ // iter:9700 score:1511
+ // iter:9800 score:662
+ // iter:9900 score:683
+
+
diff --git a/cpp_voxel/main.cpp b/cpp_voxel/main.cpp
index 173f1a0..b2b933c 100644
--- a/cpp_voxel/main.cpp
+++ b/cpp_voxel/main.cpp
@@ -476,10 +476,10 @@ int main(int const, char const**) {
std::vector<unsigned char> targ5 =drawFrep(&ss,minX, maxX, minY, maxY, dx, dy,"hole_target",false);
- // EM( width, height, targ5);
+ EM( width, height, targ3);
- EM_df( width, height, targ5);
+ // EM_df( width, height, targ5);
@@ -596,56 +596,56 @@ int main(int const, char const**) {
// itr:50, distance:6
//voxel target 3
- // itr:2500, distance:4848
- // itr:2450, distance:4619
- // itr:2400, distance:4413
- // itr:2350, distance:4181
- // itr:2300, distance:3988
- // itr:2250, distance:3774
- // itr:2200, distance:3571
- // itr:2150, distance:3360
- // itr:2100, distance:3155
- // itr:2050, distance:2976
- // itr:2000, distance:2783
- // itr:1950, distance:2550
- // itr:1900, distance:2404
- // itr:1850, distance:2217
- // itr:1800, distance:2101
- // itr:1750, distance:1909
- // itr:1700, distance:1753
- // itr:1650, distance:1609
- // itr:1600, distance:1456
- // itr:1550, distance:1308
- // itr:1500, distance:1191
- // itr:1450, distance:1050
- // itr:1400, distance:941
- // itr:1350, distance:804
- // itr:1300, distance:720
- // itr:1250, distance:622
- // itr:1200, distance:515
- // itr:1150, distance:451
- // itr:1100, distance:372
- // itr:1050, distance:287
- // itr:1000, distance:219
- // itr:950, distance:165
- // itr:900, distance:124
- // itr:850, distance:80
- // itr:800, distance:47
- // itr:750, distance:26
- // itr:700, distance:12
- // itr:650, distance:6
- // itr:600, distance:6
- // itr:550, distance:6
- // itr:500, distance:6
- // itr:450, distance:6
- // itr:400, distance:6
- // itr:350, distance:6
- // itr:300, distance:6
- // itr:250, distance:6
- // itr:200, distance:6
- // itr:150, distance:6
- // itr:100, distance:6
- // itr:50, distance:6
+ // itr:2500, distance:4875
+ // itr:2450, distance:4634
+ // itr:2400, distance:4407
+ // itr:2350, distance:4196
+ // itr:2300, distance:3976
+ // itr:2250, distance:3803
+ // itr:2200, distance:3578
+ // itr:2150, distance:3374
+ // itr:2100, distance:3151
+ // itr:2050, distance:2964
+ // itr:2000, distance:2762
+ // itr:1950, distance:2554
+ // itr:1900, distance:2399
+ // itr:1850, distance:2205
+ // itr:1800, distance:2060
+ // itr:1750, distance:1885
+ // itr:1700, distance:1746
+ // itr:1650, distance:1563
+ // itr:1600, distance:1447
+ // itr:1550, distance:1288
+ // itr:1500, distance:1122
+ // itr:1450, distance:1020
+ // itr:1400, distance:938
+ // itr:1350, distance:813
+ // itr:1300, distance:683
+ // itr:1250, distance:593
+ // itr:1200, distance:514
+ // itr:1150, distance:427
+ // itr:1100, distance:343
+ // itr:1050, distance:272
+ // itr:1000, distance:206
+ // itr:950, distance:152
+ // itr:900, distance:105
+ // itr:850, distance:66
+ // itr:800, distance:39
+ // itr:750, distance:15
+ // itr:700, distance:2
+ // itr:650, distance:0
+ // itr:600, distance:0
+ // itr:550, distance:0
+ // itr:500, distance:0
+ // itr:450, distance:0
+ // itr:400, distance:0
+ // itr:350, distance:0
+ // itr:300, distance:0
+ // itr:250, distance:0
+ // itr:200, distance:0
+ // itr:150, distance:0
+ // itr:100, distance:0
+ // itr:50, distance:0
//voxel target 4
// itr:2500, distance:4875
diff --git a/img/df/1.png b/img/df/1.png
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GIT binary patch
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GIT binary patch
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diff --git a/img/voxel/2.png b/img/voxel/2.png
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--
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