public void Test__WeightCorrectionCalculation()
{
//this is just check of calculation: ((hessian+mu*I)\gradient)' if is correct
/*
* >> %((hessian+mu*I)\gradient)'
* >> hessian = [1 2; 3 4]
* hessian =
*
* 1 2
* 3 4
* >> mu = 2
* mu =
* 2
* >> I = eye(2)
* I =
* 1 0
* 0 1
* >> gradient=[1;2]
* gradient =
* 1
* 2
* >> res=((hessian+mu*I)\gradient)'
* res =
* 0.1667 0.2500
*/
var hessian = new MatrixMB(2, 2);
//no matter if its hessian result or not
hessian[0, 0] = 1;
hessian[0, 1] = 2;
hessian[1, 0] = 3;
hessian[1, 1] = 4;
double mu = 2;
var I = MatrixMB.Eye(2);
var gradient = new MatrixMB(2, 1);
gradient[0, 0] = 1;
gradient[1, 0] = 2;
// var diff = ((hessian.HessianMat + (I * setting.MU)).Inverted * hessian.GradientMat).Transposed;
var add = hessian + I * mu;
var inv = add.Inverted;
var div = inv * gradient;
var res = div.Transposed;
Assert.AreEqual(1, res.Rows);
Assert.AreEqual(2, res.Cols);
Assert.AreEqual(0.1667, Math.Round(res[0, 0], 4));
Assert.AreEqual(0.25, Math.Round(res[0, 1], 4));
}
public void Weigths___Correction___Test()
{
var gradient = new MatrixMB(7, 1);
//gradient = [-0.839948683228052; 2.319597374905329; 2.319597374905329; -2.000000000000000; 5.523188311911530; 5.523188311911531; 6.889667569278484];
gradient[0, 0] = -0.839948683228052;
gradient[1, 0] = 2.319597374905329;
gradient[2, 0] = 2.319597374905329;
gradient[3, 0] = -2.000000000000000;
gradient[4, 0] = 5.523188311911530;
gradient[5, 0] = 5.523188311911531;
gradient[6, 0] = 6.889667569278484;
/*
* hessian = [
* 1.058270685684809 -0.705513790456539 -0.705513790456539 2.519846049684157 -1.679897366456105 -1.679897366456105 -0.639700008449225;
* -0.705513790456539 1.058270685684809 0.352756895228269 -1.679897366456105 2.519846049684157 0.839948683228052 1.279400016898449;
* -0.705513790456539 0.352756895228269 1.058270685684809 -1.679897366456105 0.839948683228052 2.519846049684157 1.279400016898449;
* 2.519846049684157 -1.679897366456105 -1.679897366456105 6.000000000000000 -4.000000000000000 -4.000000000000000 -1.523188311911530;
* -1.679897366456105 2.519846049684157 0.839948683228052 -4.000000000000000 6.000000000000000 2.000000000000000 3.046376623823059;
* -1.679897366456105 0.839948683228052 2.519846049684157 -4.000000000000000 2.000000000000000 6.000000000000000 3.046376623823059;
* -0.639700008449225 1.279400016898449 1.279400016898449 -1.523188311911530 3.046376623823059 3.046376623823059 3.480153950315843
* ];
*/
MatrixMB hessian = new MatrixMB(7, 7);
//col 1
hessian[0, 0] = 1.058270685684809;
hessian[1, 0] = -0.705513790456539;
hessian[2, 0] = -0.705513790456539;
hessian[3, 0] = 2.519846049684157;
hessian[4, 0] = -1.679897366456105;
hessian[5, 0] = -1.679897366456105;
hessian[6, 0] = -0.639700008449225;
//col 2
hessian[0, 1] = -0.705513790456539;
hessian[1, 1] = 1.058270685684809;
hessian[2, 1] = 0.352756895228269;
hessian[3, 1] = -1.679897366456105;
hessian[4, 1] = 2.519846049684157;
hessian[5, 1] = 0.839948683228052;
hessian[6, 1] = 1.279400016898449;
//col 3
hessian[0, 2] = -0.705513790456539;
hessian[1, 2] = 0.352756895228269;
hessian[2, 2] = 1.058270685684809;
hessian[3, 2] = -1.679897366456105;
hessian[4, 2] = 0.839948683228052;
hessian[5, 2] = 2.519846049684157;
hessian[6, 2] = 1.279400016898449;
//col 4
hessian[0, 3] = 2.519846049684157;
hessian[1, 3] = -1.679897366456105;
hessian[2, 3] = -1.679897366456105;
hessian[3, 3] = 6.000000000000000;
hessian[4, 3] = -4.000000000000000;
hessian[5, 3] = -4.000000000000000;
hessian[6, 3] = -1.523188311911530;
//col 5
hessian[0, 4] = -1.679897366456105;
hessian[1, 4] = 2.519846049684157;
hessian[2, 4] = 0.839948683228052;
hessian[3, 4] = -4.000000000000000;
hessian[4, 4] = 6.000000000000000;
hessian[5, 4] = 2.000000000000000;
hessian[6, 4] = 3.046376623823059;
//col 6
hessian[0, 5] = -1.679897366456105;
hessian[1, 5] = 0.839948683228052;
hessian[2, 5] = 2.519846049684157;
hessian[3, 5] = -4.000000000000000;
hessian[4, 5] = 2.000000000000000;
hessian[5, 5] = 6.000000000000000;
hessian[6, 5] = 3.046376623823059;
//col 7
hessian[0, 6] = -0.639700008449225;
hessian[1, 6] = 1.279400016898449;
hessian[2, 6] = 1.279400016898449;
hessian[3, 6] = -1.523188311911530;
hessian[4, 6] = 3.046376623823059;
hessian[5, 6] = 3.046376623823059;
hessian[6, 6] = 3.480153950315843;
Weights weights = new Weights(7);
weights.FillWithNumber(1);
double mu = 0.01;
var I = MatrixMB.Eye(weights.Length);
Console.WriteLine("Testowanie obliczania korekty wag");
Console.WriteLine("Hesjan:");
Console.WriteLine(hessian.MatrixToString());
Console.WriteLine("Gradient:");
Console.WriteLine(gradient.MatrixToString());
Console.WriteLine("Wagi początkowe:");
Console.WriteLine(weights.MatrixToString());
Console.WriteLine("MU = " + mu.ToString());
Console.WriteLine("Macierz I");
Console.WriteLine(I.MatrixToString());
//korekta_wag = ((hessian+mu*I)\gradient)';
/*
* Lewa strona
* Columns 1 through 6
*
* 1.068270685684809 -0.705513790456539 -0.705513790456539 2.519846049684157 -1.679897366456105 -1.679897366456105
* -0.705513790456539 1.068270685684809 0.352756895228269 -1.679897366456105 2.519846049684157 0.839948683228052
* -0.705513790456539 0.352756895228269 1.068270685684809 -1.679897366456105 0.839948683228052 2.519846049684157
* 2.519846049684157 -1.679897366456105 -1.679897366456105 6.010000000000000 -4.000000000000000 -4.000000000000000
* -1.679897366456105 2.519846049684157 0.839948683228052 -4.000000000000000 6.010000000000000 2.000000000000000
* -1.679897366456105 0.839948683228052 2.519846049684157 -4.000000000000000 2.000000000000000 6.010000000000000
* -0.639700008449225 1.279400016898449 1.279400016898449 -1.523188311911530 3.046376623823059 3.046376623823059
*
* Column 7
*
* -0.639700008449225
* 1.279400016898449
* 1.279400016898449
* -1.523188311911530
* 3.046376623823059
* 3.046376623823059
* 3.490153950315843
*/
var lewa_strona_matlab = new MatrixMB(7, 7);
lewa_strona_matlab[0, 0] = 1.06827068568480900000;
lewa_strona_matlab[0, 1] = -0.70551379045653895000;
lewa_strona_matlab[0, 2] = -0.70551379045653895000;
lewa_strona_matlab[0, 3] = 2.51984604968415700000;
lewa_strona_matlab[0, 4] = -1.67989736645610500000;
lewa_strona_matlab[0, 5] = -1.67989736645610500000;
lewa_strona_matlab[0, 6] = -0.63970000844922503000;
lewa_strona_matlab[1, 0] = -0.70551379045653895000;
lewa_strona_matlab[1, 1] = 1.06827068568480900000;
lewa_strona_matlab[1, 2] = 0.35275689522826897000;
lewa_strona_matlab[1, 3] = -1.67989736645610500000;
lewa_strona_matlab[1, 4] = 2.51984604968415700000;
lewa_strona_matlab[1, 5] = 0.83994868322805205000;
lewa_strona_matlab[1, 6] = 1.27940001689844900000;
lewa_strona_matlab[2, 0] = -0.70551379045653895000;
lewa_strona_matlab[2, 1] = 0.35275689522826897000;
lewa_strona_matlab[2, 2] = 1.06827068568480900000;
lewa_strona_matlab[2, 3] = -1.67989736645610500000;
lewa_strona_matlab[2, 4] = 0.83994868322805205000;
lewa_strona_matlab[2, 5] = 2.51984604968415700000;
lewa_strona_matlab[2, 6] = 1.27940001689844900000;
lewa_strona_matlab[3, 0] = 2.51984604968415700000;
lewa_strona_matlab[3, 1] = -1.67989736645610500000;
lewa_strona_matlab[3, 2] = -1.67989736645610500000;
lewa_strona_matlab[3, 3] = 6.00999999999999980000;
lewa_strona_matlab[3, 4] = -4.00000000000000000000;
lewa_strona_matlab[3, 5] = -4.00000000000000000000;
lewa_strona_matlab[3, 6] = -1.52318831191152990000;
lewa_strona_matlab[4, 0] = -1.67989736645610500000;
lewa_strona_matlab[4, 1] = 2.51984604968415700000;
lewa_strona_matlab[4, 2] = 0.83994868322805205000;
lewa_strona_matlab[4, 3] = -4.00000000000000000000;
lewa_strona_matlab[4, 4] = 6.00999999999999980000;
lewa_strona_matlab[4, 5] = 2.00000000000000000000;
lewa_strona_matlab[4, 6] = 3.04637662382305900000;
lewa_strona_matlab[5, 0] = -1.67989736645610500000;
lewa_strona_matlab[5, 1] = 0.83994868322805205000;
lewa_strona_matlab[5, 2] = 2.51984604968415700000;
lewa_strona_matlab[5, 3] = -4.00000000000000000000;
lewa_strona_matlab[5, 4] = 2.00000000000000000000;
lewa_strona_matlab[5, 5] = 6.00999999999999980000;
lewa_strona_matlab[5, 6] = 3.04637662382305900000;
lewa_strona_matlab[6, 0] = -0.63970000844922503000;
lewa_strona_matlab[6, 1] = 1.27940001689844900000;
lewa_strona_matlab[6, 2] = 1.27940001689844900000;
lewa_strona_matlab[6, 3] = -1.52318831191152990000;
lewa_strona_matlab[6, 4] = 3.04637662382305900000;
lewa_strona_matlab[6, 5] = 3.04637662382305900000;
lewa_strona_matlab[6, 6] = 3.49015395031584270000;
var lewa_strona_csharp = hessian + (I * mu);
Console.WriteLine("Lewa strona dzielenia - obliczanie korekty wag => (hessian+mu*I)");
for (int i = 0; i < lewa_strona_csharp.Rows; i++)
{
for (int j = 0; j < lewa_strona_csharp.Cols; j++)
{
var result = Math.Round(((decimal)lewa_strona_csharp[i, j]), 15);
var expected = Math.Round(((decimal)lewa_strona_matlab[i, j]), 15);
Console.WriteLine(string.Format("Poz[{0},{1}] => NBN C#: {2}\tMatLab NBN: {3}\t{4}", i, j, result, expected, result == expected ? "OK" : "źle"));
}
}
for (int i = 0; i < lewa_strona_csharp.Rows; i++)
{
for (int j = 0; j < lewa_strona_csharp.Cols; j++)
{
var result = Math.Round(((decimal)lewa_strona_csharp[i, j]), 15);
var expected = Math.Round(((decimal)lewa_strona_matlab[i, j]), 15);
Assert.AreEqual(expected, result);
}
}
var diff = ((hessian + (I * mu)).Inverted * gradient).Transposed;
/*
* Expected weights diff
* % Otrzymana korekta wag:
* %
* % 0.27954017281149085000
* % 0.21238647096009383000
* % 0.21238647096010940000
* % 0.66561250322368737000
* % 0.50571296842532187000
* % 0.50571296842531543000
* % 1.27722267527372440000
* % Koniec
*
*/
var diffMatLab = new double[]
{
0.27954017281149085000,
0.21238647096009383000,
0.21238647096010940000,
0.66561250322368737000,
0.50571296842532187000,
0.50571296842531543000,
1.27722267527372440000
};
/*
* Test matlabowy
* function obliczanie_korekty_wag()
* %ww = ww_backup - ((hessian+mu*I)\gradient)';
* clear();
* ww = [1 1 1 1 1 1 1];
* mu = 0.01;
* I = eye(size(ww,2));%tyle co wag
* format long;
* gradient = [-0.839948683228052; 2.319597374905329; 2.319597374905329; -2.000000000000000; 5.523188311911530; 5.523188311911531; 6.889667569278484];
* hessian = [
* 1.058270685684809 -0.705513790456539 -0.705513790456539 2.519846049684157 -1.679897366456105 -1.679897366456105 -0.639700008449225;
* -0.705513790456539 1.058270685684809 0.352756895228269 -1.679897366456105 2.519846049684157 0.839948683228052 1.279400016898449;
* -0.705513790456539 0.352756895228269 1.058270685684809 -1.679897366456105 0.839948683228052 2.519846049684157 1.279400016898449;
* 2.519846049684157 -1.679897366456105 -1.679897366456105 6.000000000000000 -4.000000000000000 -4.000000000000000 -1.523188311911530;
* -1.679897366456105 2.519846049684157 0.839948683228052 -4.000000000000000 6.000000000000000 2.000000000000000 3.046376623823059;
* -1.679897366456105 0.839948683228052 2.519846049684157 -4.000000000000000 2.000000000000000 6.000000000000000 3.046376623823059;
* -0.639700008449225 1.279400016898449 1.279400016898449 -1.523188311911530 3.046376623823059 3.046376623823059 3.480153950315843
* ];
*
* korekta_wag = ((hessian+mu*I)\gradient)';
* fprintf('\nOtrzymana korekta wag:\n');
* for i=1:size(ww,2)
* fprintf('\n%.20f',korekta_wag(i));
* end
* fprintf('\nKoniec\n');
* end
*
* % Otrzymana korekta wag:
* %
* % 0.27954017281149085000
* % 0.21238647096009383000
* % 0.21238647096010940000
* % 0.66561250322368737000
* % 0.50571296842532187000
* % 0.50571296842531543000
* % 1.27722267527372440000
* % Koniec
*
*/
Console.WriteLine("Korekta wag:");
int accuracy = 15;
for (int i = 0; i < diffMatLab.Length; i++)
{
decimal result = (decimal)diff[0, i];
decimal expected = (decimal)diffMatLab[i];
result = Math.Round(result, accuracy);
expected = Math.Round(expected, accuracy);
Console.WriteLine(string.Format("NBN C#: {0}\tMatLab NBN: {1}\t {2}", result, expected, result == expected ? "OK" : "źle"));
}
for (int i = 0; i < diffMatLab.Length; i++)
{
decimal result = (decimal)diff[0, i];
decimal expected = (decimal)diffMatLab[i];
result = Math.Round(result, accuracy);
expected = Math.Round(expected, accuracy);
Assert.AreEqual(expected, result);
}
}
