hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image. here is my code https://ideone.com/cok4XL
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hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image. here is my code https://ideone.com/cok4XL
hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image. here is my code https://ideone.com/cok4XL
/***
This is ICA infomax implementation
equation : X = AS where X is the mixture matrix, A is mixing matrix, S is the source
SO ------> S = WX where W is unmixing matrix.
and since there no way that we can recover the excat source image but
so we use Y instead of S where U ~= S
which gives us U = WX
W = learning rate (YU) W + W
where Y = -tanh(U/2)
YU = I + Y (U)t
***/
#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
const char* depthToStr(int depth) {
switch(depth){
case CV_8U: return "unsigned char";
case CV_8S: return "char";
case CV_16U: return "unsigned short";
case CV_16S: return "short";
case CV_32S: return "int";
case CV_32F: return "float";
case CV_64F: return "double";
}
return "invalid type!";
}
void ICA(Mat &w, Mat x, Mat &s)
{
// get the size of the unmixing matrix
int Weightrows = x.rows;
int Weightcols = x.rows;
// learning rate
float lnrate = 0.95;
// U = source (s)
Mat u;
// y ---> is super guassian
Mat y;
// YU
Mat yu;
// creating I (indentity matrix)
Mat I = Mat::eye (x.rows, x.rows, CV_64F);
// number of iterates
int iteratesNumber = 0;
// creating the unmixing matrix and set it to random variables
w.create(Weightrows, Weightrows, CV_64F);
randu(w, Scalar(-1), Scalar(1));
x.convertTo(x, CV_64F);
u.convertTo(u, CV_64F);
cout << depthToStr(x.depth()) << endl << depthToStr(u.depth()) << endl<<depthToStr(w.depth()) <<endl;
for(int iter = 0; iter < 10; iter++)
{
u = w * x;
// set the super guassian (nonlinear)
Mat tanh1, tanh2, tanh;
exp (u, tanh1);
exp (-1 * u , tanh2);
tanh = (tanh1 - tanh2) / (tanh1 + tanh2);
y = -1 * tanh;
yu = I + y * u.t();
w = 0.95 * yu * w + w;
}
s = w * x;
}
int main()
{
Mat Image, weight, result;
Image = imread("/home/pixar/Desktop/rmi.jpeg",0);
imshow(" s", Image);
ICA(weight, Image, result);
cout << result <<endl;
imshow(" ", result);
waitKey(0);
return 0;
}
hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image. here is my code https://ideone.com/cok4XL
/***
This is ICA infomax implementation
equation : X = AS where X is the mixture matrix, A is mixing matrix, S is the source
SO ------> S = WX where W is unmixing matrix.
and since there no way that we can recover the excat source image but
so we use Y instead of S where U ~= S
which gives us U = WX
W = learning rate (YU) W + W
where Y = -tanh(U/2)
YU = I + Y (U)t
***/
#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
const char* depthToStr(int depth) {
switch(depth){
case CV_8U: return "unsigned char";
case CV_8S: return "char";
case CV_16U: return "unsigned short";
case CV_16S: return "short";
case CV_32S: return "int";
case CV_32F: return "float";
case CV_64F: return "double";
}
return "invalid type!";
}
void ICA(Mat &w, Mat x, Mat &s)
{
// get the size of the unmixing matrix
int Weightrows = x.rows;
int Weightcols = x.rows;
// learning rate
float lnrate = 0.95;
// U = source (s)
Mat u;
// y ---> is super guassian
Mat y;
// YU
Mat yu;
// creating I (indentity matrix)
Mat I = Mat::eye (x.rows, x.rows, CV_64F);
// number of iterates
int iteratesNumber = 0;
// creating the unmixing matrix and set it to random variables
w.create(Weightrows, Weightrows, CV_64F);
randu(w, Scalar(-1), Scalar(1));
x.convertTo(x, CV_64F);
u.convertTo(u, CV_64F);
cout << depthToStr(x.depth()) << endl << depthToStr(u.depth()) << endl<<depthToStr(w.depth()) <<endl;
for(int iter = 0; iter < 10; iter++)
{
u = w * x;
// set the super guassian (nonlinear)
Mat tanh1, tanh2, tanh;
exp (u, tanh1);
exp (-1 * u , tanh2);
tanh = (tanh1 - tanh2) / (tanh1 + tanh2);
y = -1 * tanh;
yu = I + y * u.t();
w = 0.95 * yu * w + w;
}
s = w * x;
}
int main()
{
Mat Image, weight, result;
Image = imread("/home/pixar/Desktop/rmi.jpeg",0);
imshow(" s", Image);
ICA(weight, Image, result);
cout << result <<endl;
imshow(" ", result);
waitKey(0);
return 0;
}
hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image.
here is my code
https://ideone.com/cok4XLcode:
/***
This is ICA infomax implementation
equation : X = AS where X is the mixture matrix, A is mixing matrix, S is the source
SO ------> S = WX where W is unmixing matrix.
and since there no way that we can recover the excat source image but
so we use Y instead of S where U ~= S
which gives us U = WX
W = learning rate (YU) W + W
where Y = -tanh(U/2)
YU = I + Y (U)t
***/
#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
const char* depthToStr(int depth) {
switch(depth){
case CV_8U: return "unsigned char";
case CV_8S: return "char";
case CV_16U: return "unsigned short";
case CV_16S: return "short";
case CV_32S: return "int";
case CV_32F: return "float";
case CV_64F: return "double";
}
return "invalid type!";
}
void ICA(Mat &w, Mat x, Mat &s)
{
// get the size of the unmixing matrix
int Weightrows = x.rows;
int Weightcols = x.rows;
// learning rate
float lnrate = 0.95;
// U = source (s)
Mat u;
// y ---> is super guassian
Mat y;
// YU
Mat yu;
// creating I (indentity matrix)
Mat I = Mat::eye (x.rows, x.rows, CV_64F);
// number of iterates
int iteratesNumber = 0;
// creating the unmixing matrix and set it to random variables
w.create(Weightrows, Weightrows, CV_64F);
randu(w, Scalar(-1), Scalar(1));
x.convertTo(x, CV_64F);
u.convertTo(u, CV_64F);
cout << depthToStr(x.depth()) << endl << depthToStr(u.depth()) << endl<<depthToStr(w.depth()) <<endl;
for(int iter = 0; iter < 10; iter++)
{
u = w * x;
// set the super guassian (nonlinear)
Mat tanh1, tanh2, tanh;
exp (u, tanh1);
exp (-1 * u , tanh2);
tanh = (tanh1 - tanh2) / (tanh1 + tanh2);
y = -1 * tanh;
yu = I + y * u.t();
w = 0.95 * yu * w + w;
}
s = w * x;
}
int main()
{
Mat Image, weight, result;
Image = imread("/home/pixar/Desktop/rmi.jpeg",0);
imshow(" s", Image);
ICA(weight, Image, result);
cout << result <<endl;
imshow(" ", result);
waitKey(0);
return 0;
}
hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image. here is my code:
/***
This is ICA infomax implementation
equation : X = AS where X is the mixture matrix, A is mixing matrix, S is the source
SO ------> S = WX where W is unmixing matrix.
and since there no way that we can recover the excat source image but
so we use Y instead of S where U ~= S
which gives us U = WX
W = learning rate (YU) W + W
where Y = -tanh(U/2)
YU = I + Y (U)t
***/
#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
const char* depthToStr(int depth) {
switch(depth){
case CV_8U: return "unsigned char";
case CV_8S: return "char";
case CV_16U: return "unsigned short";
case CV_16S: return "short";
case CV_32S: return "int";
case CV_32F: return "float";
case CV_64F: return "double";
}
return "invalid type!";
}
void ICA(Mat &w, Mat x, Mat &s)
{
// get the size of the unmixing matrix
int Weightrows = x.rows;
int Weightcols = x.rows;
// learning rate
float lnrate = 0.95;
// U = source (s)
Mat u;
// y ---> is super guassian
Mat y;
// YU
Mat yu;
// creating I (indentity matrix)
Mat I = Mat::eye (x.rows, x.rows, CV_64F);
// number of iterates
int iteratesNumber = 0;
// creating the unmixing matrix and set it to random variables
w.create(Weightrows, Weightrows, CV_64F);
randu(w, Scalar(-1), Scalar(1));
x.convertTo(x, CV_64F);
u.convertTo(u, CV_64F);
cout << depthToStr(x.depth()) << endl << depthToStr(u.depth()) << endl<<depthToStr(w.depth()) <<endl;
for(int iter = 0; iter < 10; iter++)
{
u = w * x;
// set the super guassian (nonlinear)
Mat tanh1, tanh2, tanh;
exp (u, tanh1);
exp (-1 * u , tanh2);
tanh = (tanh1 - tanh2) / (tanh1 + tanh2);
y = -1 * tanh;
yu = I + y * u.t();
w = 0.95 * yu * w + w;
}
s = w * x;
}
int main()
{
Mat Image, weight, result;
Image = imread("/home/pixar/Desktop/rmi.jpeg",0);
imshow(" s", Image);
ICA(weight, Image, result);
cout << result <<endl;
imshow(" ", result);
waitKey(0);
return 0;
}
hi. sorry guys i would like to know if my concept of implementation ICA infomax is wrong or what because the result is blank image. here is my code:
/***
This is ICA infomax implementation
equation : X = AS where X is the mixture matrix, A is mixing matrix, S is the source
SO ------> S = WX where W is unmixing matrix.
and since there no way that we can recover the excat source image but
so we use Y U instead of S where U ~= S
which gives us U = WX
W = learning rate (YU) W + W
where Y = -tanh(U/2)
YU = I + Y (U)t
***/
#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
const char* depthToStr(int depth) {
switch(depth){
case CV_8U: return "unsigned char";
case CV_8S: return "char";
case CV_16U: return "unsigned short";
case CV_16S: return "short";
case CV_32S: return "int";
case CV_32F: return "float";
case CV_64F: return "double";
}
return "invalid type!";
}
void ICA(Mat &w, Mat x, Mat &s)
{
// get the size of the unmixing matrix
int Weightrows = x.rows;
int Weightcols = x.rows;
// learning rate
float lnrate = 0.95;
// U = source (s)
Mat u;
// y ---> is super guassian
Mat y;
// YU
Mat yu;
// creating I (indentity matrix)
Mat I = Mat::eye (x.rows, x.rows, CV_64F);
// number of iterates
int iteratesNumber = 0;
// creating the unmixing matrix and set it to random variables
w.create(Weightrows, Weightrows, CV_64F);
randu(w, Scalar(-1), Scalar(1));
x.convertTo(x, CV_64F);
u.convertTo(u, CV_64F);
cout << depthToStr(x.depth()) << endl << depthToStr(u.depth()) << endl<<depthToStr(w.depth()) <<endl;
for(int iter = 0; iter < 10; iter++)
{
u = w * x;
// set the super guassian (nonlinear)
Mat tanh1, tanh2, tanh;
exp (u, tanh1);
exp (-1 * u , tanh2);
tanh = (tanh1 - tanh2) / (tanh1 + tanh2);
y = -1 * tanh;
yu = I + y * u.t();
w = 0.95 * yu * w + w;
}
s = w * x;
}
int main()
{
Mat Image, weight, result;
Image = imread("/home/pixar/Desktop/rmi.jpeg",0);
imshow(" s", Image);
ICA(weight, Image, result);
cout << result <<endl;
imshow(" ", result);
waitKey(0);
return 0;
}