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3dpcp/.svn/pristine/01/01367402c246e1425d06318a597...

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//#define WANT_STREAM
#define WANT_MATH
#include "include.h"
#include "newmatap.h"
//#include "newmatio.h"
#include "tmt.h"
#ifdef use_namespace
using namespace NEWMAT;
#endif
// check D is sorted
void CheckIsSorted(const DiagonalMatrix& D, bool ascending = false)
{
DiagonalMatrix D1 = D;
if (ascending) SortAscending(D1); else SortDescending(D1);
D1 -= D; Print(D1);
}
void trymate()
{
Tracer et("Fourteenth test of Matrix package");
Tracer::PrintTrace();
{
Tracer et1("Stage 1");
Matrix A(8,5);
{
#ifndef ATandT
Real a[] = { 22, 10, 2, 3, 7,
14, 7, 10, 0, 8,
-1, 13, -1,-11, 3,
-3, -2, 13, -2, 4,
9, 8, 1, -2, 4,
9, 1, -7, 5, -1,
2, -6, 6, 5, 1,
4, 5, 0, -2, 2 };
#else
Real a[40];
a[ 0]=22; a[ 1]=10; a[ 2]= 2; a[ 3]= 3; a[ 4]= 7;
a[ 5]=14; a[ 6]= 7; a[ 7]=10; a[ 8]= 0; a[ 9]= 8;
a[10]=-1; a[11]=13; a[12]=-1; a[13]=-11;a[14]= 3;
a[15]=-3; a[16]=-2; a[17]=13; a[18]=-2; a[19]= 4;
a[20]= 9; a[21]= 8; a[22]= 1; a[23]=-2; a[24]= 4;
a[25]= 9; a[26]= 1; a[27]=-7; a[28]= 5; a[29]=-1;
a[30]= 2; a[31]=-6; a[32]= 6; a[33]= 5; a[34]= 1;
a[35]= 4; a[36]= 5; a[37]= 0; a[38]=-2; a[39]= 2;
#endif
A << a;
}
DiagonalMatrix D; Matrix U; Matrix V;
int anc = A.Ncols(); IdentityMatrix I(anc);
SymmetricMatrix S1; S1 << A.t() * A;
SymmetricMatrix S2; S2 << A * A.t();
Real zero = 0.0; SVD(A+zero,D,U,V); CheckIsSorted(D);
DiagonalMatrix D1; SVD(A,D1); CheckIsSorted(D1);
D1 -= D; Clean(D1,0.000000001);Print(D1);
Matrix W;
SVD(A, D1, W, W, true, false); D1 -= D; W -= U;
Clean(W,0.000000001); Print(W); Clean(D1,0.000000001); Print(D1);
Matrix WX;
SVD(A, D1, WX, W, false, true); D1 -= D; W -= V;
Clean(W,0.000000001); Print(W); Clean(D1,0.000000001); Print(D1);
Matrix SU = U.t() * U - I; Clean(SU,0.000000001); Print(SU);
Matrix SV = V.t() * V - I; Clean(SV,0.000000001); Print(SV);
Matrix B = U * D * V.t() - A; Clean(B,0.000000001); Print(B);
D1=0.0; SVD(A,D1,A); CheckIsSorted(D1);
A -= U; Clean(A,0.000000001); Print(A);
D(1) -= sqrt(1248.0); D(2) -= 20; D(3) -= sqrt(384.0);
Clean(D,0.000000001); Print(D);
Jacobi(S1, D, V); CheckIsSorted(D, true);
V = S1 - V * D * V.t(); Clean(V,0.000000001); Print(V);
D = D.Reverse(); D(1)-=1248; D(2)-=400; D(3)-=384;
Clean(D,0.000000001); Print(D);
Jacobi(S1, D); CheckIsSorted(D, true);
D = D.Reverse(); D(1)-=1248; D(2)-=400; D(3)-=384;
Clean(D,0.000000001); Print(D);
SymmetricMatrix JW(5);
Jacobi(S1, D, JW); CheckIsSorted(D, true);
D = D.Reverse(); D(1)-=1248; D(2)-=400; D(3)-=384;
Clean(D,0.000000001); Print(D);
Jacobi(S2, D, V); CheckIsSorted(D, true);
V = S2 - V * D * V.t(); Clean(V,0.000000001); Print(V);
D = D.Reverse(); D(1)-=1248; D(2)-=400; D(3)-=384;
Clean(D,0.000000001); Print(D);
EigenValues(S1, D, V); CheckIsSorted(D, true);
V = S1 - V * D * V.t(); Clean(V,0.000000001); Print(V);
D(5)-=1248; D(4)-=400; D(3)-=384;
Clean(D,0.000000001); Print(D);
EigenValues(S2, D, V); CheckIsSorted(D, true);
V = S2 - V * D * V.t(); Clean(V,0.000000001); Print(V);
D(8)-=1248; D(7)-=400; D(6)-=384;
Clean(D,0.000000001); Print(D);
EigenValues(S1, D); CheckIsSorted(D, true);
D(5)-=1248; D(4)-=400; D(3)-=384;
Clean(D,0.000000001); Print(D);
SymmetricMatrix EW(S2);
EigenValues(S2, D, EW); CheckIsSorted(D, true);
D(8)-=1248; D(7)-=400; D(6)-=384;
Clean(D,0.000000001); Print(D);
}
{
Tracer et1("Stage 2");
Matrix A(20,21);
int i,j;
for (i=1; i<=20; i++) for (j=1; j<=21; j++)
{ if (i>j) A(i,j) = 0; else if (i==j) A(i,j) = 21-i; else A(i,j) = -1; }
A = A.t();
SymmetricMatrix S1; S1 << A.t() * A;
SymmetricMatrix S2; S2 << A * A.t();
DiagonalMatrix D; Matrix U; Matrix V;
#ifdef ATandT
int anc = A.Ncols(); DiagonalMatrix I(anc); // AT&T 2.1 bug
#else
DiagonalMatrix I(A.Ncols());
#endif
I=1.0;
SVD(A,D,U,V); CheckIsSorted(D);
Matrix SU = U.t() * U - I; Clean(SU,0.000000001); Print(SU);
Matrix SV = V.t() * V - I; Clean(SV,0.000000001); Print(SV);
Matrix B = U * D * V.t() - A; Clean(B,0.000000001); Print(B);
for (i=1; i<=20; i++) D(i) -= sqrt((22.0-i)*(21.0-i));
Clean(D,0.000000001); Print(D);
Jacobi(S1, D, V); CheckIsSorted(D, true);
V = S1 - V * D * V.t(); Clean(V,0.000000001); Print(V);
D = D.Reverse();
for (i=1; i<=20; i++) D(i) -= (22-i)*(21-i);
Clean(D,0.000000001); Print(D);
Jacobi(S2, D, V); CheckIsSorted(D, true);
V = S2 - V * D * V.t(); Clean(V,0.000000001); Print(V);
D = D.Reverse();
for (i=1; i<=20; i++) D(i) -= (22-i)*(21-i);
Clean(D,0.000000001); Print(D);
EigenValues(S1, D, V); CheckIsSorted(D, true);
V = S1 - V * D * V.t(); Clean(V,0.000000001); Print(V);
for (i=1; i<=20; i++) D(i) -= (i+1)*i;
Clean(D,0.000000001); Print(D);
EigenValues(S2, D, V); CheckIsSorted(D, true);
V = S2 - V * D * V.t(); Clean(V,0.000000001); Print(V);
for (i=2; i<=21; i++) D(i) -= (i-1)*i;
Clean(D,0.000000001); Print(D);
EigenValues(S1, D); CheckIsSorted(D, true);
for (i=1; i<=20; i++) D(i) -= (i+1)*i;
Clean(D,0.000000001); Print(D);
EigenValues(S2, D); CheckIsSorted(D, true);
for (i=2; i<=21; i++) D(i) -= (i-1)*i;
Clean(D,0.000000001); Print(D);
}
{
Tracer et1("Stage 3");
Matrix A(30,30);
int i,j;
for (i=1; i<=30; i++) for (j=1; j<=30; j++)
{ if (i>j) A(i,j) = 0; else if (i==j) A(i,j) = 1; else A(i,j) = -1; }
Real d1 = A.LogDeterminant().Value();
DiagonalMatrix D; Matrix U; Matrix V;
#ifdef ATandT
int anc = A.Ncols(); DiagonalMatrix I(anc); // AT&T 2.1 bug
#else
DiagonalMatrix I(A.Ncols());
#endif
I=1.0;
SVD(A,D,U,V); CheckIsSorted(D);
Matrix SU = U.t() * U - I; Clean(SU,0.000000001); Print(SU);
Matrix SV = V.t() * V - I; Clean(SV,0.000000001); Print(SV);
Real d2 = D.LogDeterminant().Value();
Matrix B = U * D * V.t() - A; Clean(B,0.000000001); Print(B);
Real d3 = D.LogDeterminant().Value();
ColumnVector Test(3);
Test(1) = d1 - 1; Test(2) = d2 - 1; Test(3) = d3 - 1;
Clean(Test,0.00000001); Print(Test); // only 8 decimal figures
A.ReSize(2,2);
Real a = 1.5; Real b = 2; Real c = 2 * (a*a + b*b);
A << a << b << a << b;
I.ReSize(2); I=1;
SVD(A,D,U,V); CheckIsSorted(D);
SU = U.t() * U - I; Clean(SU,0.000000001); Print(SU);
SV = V.t() * V - I; Clean(SV,0.000000001); Print(SV);
B = U * D * V.t() - A; Clean(B,0.000000001); Print(B);
D = D*D; SortDescending(D);
DiagonalMatrix D50(2); D50 << c << 0; D = D - D50;
Clean(D,0.000000001);
Print(D);
A << a << a << b << b;
SVD(A,D,U,V); CheckIsSorted(D);
SU = U.t() * U - I; Clean(SU,0.000000001); Print(SU);
SV = V.t() * V - I; Clean(SV,0.000000001); Print(SV);
B = U * D * V.t() - A; Clean(B,0.000000001); Print(B);
D = D*D; SortDescending(D);
D = D - D50;
Clean(D,0.000000001);
Print(D);
}
{
Tracer et1("Stage 4");
// test for bug found by Olof Runborg,
// Department of Numerical Analysis and Computer Science (NADA),
// KTH, Stockholm
Matrix A(22,20);
A = 0;
int a=1;
A(a+0,a+2) = 1; A(a+0,a+18) = -1;
A(a+1,a+9) = 1; A(a+1,a+12) = -1;
A(a+2,a+11) = 1; A(a+2,a+12) = -1;
A(a+3,a+10) = 1; A(a+3,a+19) = -1;
A(a+4,a+16) = 1; A(a+4,a+19) = -1;
A(a+5,a+17) = 1; A(a+5,a+18) = -1;
A(a+6,a+10) = 1; A(a+6,a+4) = -1;
A(a+7,a+3) = 1; A(a+7,a+2) = -1;
A(a+8,a+14) = 1; A(a+8,a+15) = -1;
A(a+9,a+13) = 1; A(a+9,a+16) = -1;
A(a+10,a+8) = 1; A(a+10,a+9) = -1;
A(a+11,a+1) = 1; A(a+11,a+15) = -1;
A(a+12,a+16) = 1; A(a+12,a+4) = -1;
A(a+13,a+6) = 1; A(a+13,a+9) = -1;
A(a+14,a+5) = 1; A(a+14,a+4) = -1;
A(a+15,a+0) = 1; A(a+15,a+1) = -1;
A(a+16,a+14) = 1; A(a+16,a+0) = -1;
A(a+17,a+7) = 1; A(a+17,a+6) = -1;
A(a+18,a+13) = 1; A(a+18,a+5) = -1;
A(a+19,a+7) = 1; A(a+19,a+8) = -1;
A(a+20,a+17) = 1; A(a+20,a+3) = -1;
A(a+21,a+6) = 1; A(a+21,a+11) = -1;
Matrix U, V; DiagonalMatrix S;
SVD(A, S, U, V, true, true); CheckIsSorted(S);
DiagonalMatrix D(20); D = 1;
Matrix tmp = U.t() * U - D;
Clean(tmp,0.000000001); Print(tmp);
tmp = V.t() * V - D;
Clean(tmp,0.000000001); Print(tmp);
tmp = U * S * V.t() - A ;
Clean(tmp,0.000000001); Print(tmp);
}
{
Tracer et1("Stage 5");
Matrix A(10,10);
A.Row(1) << 1.00 << 0.07 << 0.05 << 0.00 << 0.06
<< 0.09 << 0.03 << 0.02 << 0.02 << -0.03;
A.Row(2) << 0.07 << 1.00 << 0.05 << 0.05 << -0.03
<< 0.07 << 0.00 << 0.07 << 0.00 << 0.02;
A.Row(3) << 0.05 << 0.05 << 1.00 << 0.05 << 0.02
<< 0.01 << -0.05 << 0.04 << 0.05 << -0.03;
A.Row(4) << 0.00 << 0.05 << 0.05 << 1.00 << -0.05
<< 0.04 << 0.01 << 0.02 << -0.05 << 0.00;
A.Row(5) << 0.06 << -0.03 << 0.02 << -0.05 << 1.00
<< -0.03 << 0.02 << -0.02 << 0.04 << 0.00;
A.Row(6) << 0.09 << 0.07 << 0.01 << 0.04 << -0.03
<< 1.00 << -0.06 << 0.08 << -0.02 << -0.10;
A.Row(7) << 0.03 << 0.00 << -0.05 << 0.01 << 0.02
<< -0.06 << 1.00 << 0.09 << 0.12 << -0.03;
A.Row(8) << 0.02 << 0.07 << 0.04 << 0.02 << -0.02
<< 0.08 << 0.09 << 1.00 << 0.00 << -0.02;
A.Row(9) << 0.02 << 0.00 << 0.05 << -0.05 << 0.04
<< -0.02 << 0.12 << 0.00 << 1.00 << 0.02;
A.Row(10) << -0.03 << 0.02 << -0.03 << 0.00 << 0.00
<< -0.10 << -0.03 << -0.02 << 0.02 << 1.00;
SymmetricMatrix AS; AS << A;
Matrix V; DiagonalMatrix D, D1;
ColumnVector Check(6);
EigenValues(AS,D,V); CheckIsSorted(D, true);
Check(1) = MaximumAbsoluteValue(A - V * D * V.t());
DiagonalMatrix I(10); I = 1;
Check(2) = MaximumAbsoluteValue(V * V.t() - I);
Check(3) = MaximumAbsoluteValue(V.t() * V - I);
EigenValues(AS, D1); CheckIsSorted(D1, true);
D -= D1;
Clean(D,0.000000001); Print(D);
Jacobi(AS,D,V);
Check(4) = MaximumAbsoluteValue(A - V * D * V.t());
Check(5) = MaximumAbsoluteValue(V * V.t() - I);
Check(6) = MaximumAbsoluteValue(V.t() * V - I);
SortAscending(D);
D -= D1;
Clean(D,0.000000001); Print(D);
Clean(Check,0.000000001); Print(Check);
// Check loading rows
SymmetricMatrix B(10);
B.Row(1) << 1.00;
B.Row(2) << 0.07 << 1.00;
B.Row(3) << 0.05 << 0.05 << 1.00;
B.Row(4) << 0.00 << 0.05 << 0.05 << 1.00;
B.Row(5) << 0.06 << -0.03 << 0.02 << -0.05 << 1.00;
B.Row(6) << 0.09 << 0.07 << 0.01 << 0.04 << -0.03
<< 1.00;
B.Row(7) << 0.03 << 0.00 << -0.05 << 0.01 << 0.02
<< -0.06 << 1.00;
B.Row(8) << 0.02 << 0.07 << 0.04 << 0.02 << -0.02
<< 0.08 << 0.09 << 1.00;
B.Row(9) << 0.02 << 0.00 << 0.05 << -0.05 << 0.04
<< -0.02 << 0.12 << 0.00 << 1.00;
B.Row(10) << -0.03 << 0.02 << -0.03 << 0.00 << 0.00
<< -0.10 << -0.03 << -0.02 << 0.02 << 1.00;
B -= AS; Print(B);
}
{
Tracer et1("Stage 6");
// badly scaled matrix
Matrix A(9,9);
A.Row(1) << 1.13324e+012 << 3.68788e+011 << 3.35163e+009
<< 3.50193e+011 << 1.25335e+011 << 1.02212e+009
<< 3.16602e+009 << 1.02418e+009 << 9.42959e+006;
A.Row(2) << 3.68788e+011 << 1.67128e+011 << 1.27449e+009
<< 1.25335e+011 << 6.05413e+010 << 4.34573e+008
<< 1.02418e+009 << 4.69192e+008 << 3.61098e+006;
A.Row(3) << 3.35163e+009 << 1.27449e+009 << 1.25571e+007
<< 1.02212e+009 << 4.34573e+008 << 3.69769e+006
<< 9.42959e+006 << 3.61098e+006 << 3.59450e+004;
A.Row(4) << 3.50193e+011 << 1.25335e+011 << 1.02212e+009
<< 1.43514e+011 << 5.42310e+010 << 4.15822e+008
<< 1.23068e+009 << 4.31545e+008 << 3.58714e+006;
A.Row(5) << 1.25335e+011 << 6.05413e+010 << 4.34573e+008
<< 5.42310e+010 << 2.76601e+010 << 1.89102e+008
<< 4.31545e+008 << 2.09778e+008 << 1.51083e+006;
A.Row(6) << 1.02212e+009 << 4.34573e+008 << 3.69769e+006
<< 4.15822e+008 << 1.89102e+008 << 1.47143e+006
<< 3.58714e+006 << 1.51083e+006 << 1.30165e+004;
A.Row(7) << 3.16602e+009 << 1.02418e+009 << 9.42959e+006
<< 1.23068e+009 << 4.31545e+008 << 3.58714e+006
<< 1.12335e+007 << 3.54778e+006 << 3.34311e+004;
A.Row(8) << 1.02418e+009 << 4.69192e+008 << 3.61098e+006
<< 4.31545e+008 << 2.09778e+008 << 1.51083e+006
<< 3.54778e+006 << 1.62552e+006 << 1.25885e+004;
A.Row(9) << 9.42959e+006 << 3.61098e+006 << 3.59450e+004
<< 3.58714e+006 << 1.51083e+006 << 1.30165e+004
<< 3.34311e+004 << 1.25885e+004 << 1.28000e+002;
SymmetricMatrix AS; AS << A;
Matrix V; DiagonalMatrix D, D1;
ColumnVector Check(6);
EigenValues(AS,D,V); CheckIsSorted(D, true);
Check(1) = MaximumAbsoluteValue(A - V * D * V.t()) / 100000;
DiagonalMatrix I(9); I = 1;
Check(2) = MaximumAbsoluteValue(V * V.t() - I);
Check(3) = MaximumAbsoluteValue(V.t() * V - I);
EigenValues(AS, D1);
D -= D1;
Clean(D,0.001); Print(D);
Jacobi(AS,D,V);
Check(4) = MaximumAbsoluteValue(A - V * D * V.t()) / 100000;
Check(5) = MaximumAbsoluteValue(V * V.t() - I);
Check(6) = MaximumAbsoluteValue(V.t() * V - I);
SortAscending(D);
D -= D1;
Clean(D,0.001); Print(D);
Clean(Check,0.0000001); Print(Check);
}
{
Tracer et1("Stage 7");
// matrix with all singular values close to 1
Matrix A(8,8);
A.Row(1)<<-0.4343<<-0.0445<<-0.4582<<-0.1612<<-0.3191<<-0.6784<<0.1068<<0;
A.Row(2)<<0.5791<<0.5517<<0.2575<<-0.1055<<-0.0437<<-0.5282<<0.0442<<0;
A.Row(3)<<0.5709<<-0.5179<<-0.3275<<0.2598<<-0.196<<-0.1451<<-0.4143<<0;
A.Row(4)<<0.2785<<-0.5258<<0.1251<<-0.4382<<0.0514<<-0.0446<<0.6586<<0;
A.Row(5)<<0.2654<<0.3736<<-0.7436<<-0.0122<<0.0376<<0.3465<<0.3397<<0;
A.Row(6)<<0.0173<<-0.0056<<-0.1903<<-0.7027<<0.4863<<-0.0199<<-0.4825<<0;
A.Row(7)<<0.0434<<0.0966<<0.1083<<-0.4576<<-0.7857<<0.3425<<-0.1818<<0;
A.Row(8)<<0.0<<0.0<<0.0<<0.0<<0.0<<0.0<<0.0<<-1.0;
Matrix U,V; DiagonalMatrix D;
SVD(A,D,U,V); CheckIsSorted(D);
Matrix B = U * D * V.t() - A; Clean(B,0.000000001); Print(B);
DiagonalMatrix I(8); I = 1; D -= I; Clean(D,0.0001); Print(D);
U *= U.t(); U -= I; Clean(U,0.000000001); Print(U);
V *= V.t(); V -= I; Clean(V,0.000000001); Print(V);
}
{
Tracer et1("Stage 8");
// check SortSV functions
Matrix A(15, 10);
int i, j;
for (i = 1; i <= 15; ++i) for (j = 1; j <= 10; ++j)
A(i, j) = i + j / 1000.0;
DiagonalMatrix D(10);
D << 0.2 << 0.5 << 0.1 << 0.7 << 0.8 << 0.3 << 0.4 << 0.7 << 0.9 << 0.6;
Matrix U = A; Matrix V = 10 - 2 * A;
Matrix Prod = U * D * V.t();
DiagonalMatrix D2 = D; SortDescending(D2);
DiagonalMatrix D1 = D; SortSV(D1, U, V); Matrix X = D1 - D2; Print(X);
X = Prod - U * D1 * V.t(); Clean(X,0.000000001); Print(X);
U = A; V = 10 - 2 * A;
D1 = D; SortSV(D1, U); X = D1 - D2; Print(X);
D1 = D; SortSV(D1, V); X = D1 - D2; Print(X);
X = Prod - U * D1 * V.t(); Clean(X,0.000000001); Print(X);
D2 = D; SortAscending(D2);
U = A; V = 10 - 2 * A;
D1 = D; SortSV(D1, U, V, true); X = D1 - D2; Print(X);
X = Prod - U * D1 * V.t(); Clean(X,0.000000001); Print(X);
U = A; V = 10 - 2 * A;
D1 = D; SortSV(D1, U, true); X = D1 - D2; Print(X);
D1 = D; SortSV(D1, V, true); X = D1 - D2; Print(X);
X = Prod - U * D1 * V.t(); Clean(X,0.000000001); Print(X);
}
{
Tracer et1("Stage 9");
// Tom William's example
Matrix A(10,10);
Matrix U;
Matrix V;
DiagonalMatrix Sigma;
Real myVals[] =
{
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
};
A << myVals;
SVD(A, Sigma, U, V); CheckIsSorted(Sigma);
A -= U * Sigma * V.t();
Clean(A, 0.000000001); Print(A);
}
}
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