/// \ingroup newmat ///@{ /// \file tmte.cpp /// Part of matrix library test program. //#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); { 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 }; int ai[] = { 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 }; A << a; Matrix AI(8,5); AI << ai; AI -= A; Print(AI); int b[] = { 13, -1,-11, -2, 13, -2, 8, 1, -2, 1, -7, 5 }; Matrix B(8, 5); B = 23; B.SubMatrix(3,6,2,4) << b; AI = A; AI.Rows(1,2) = 23; AI.Rows(7,8) = 23; AI.Column(1) = 23; AI.Column(5) = 23; AI -= B; Print(AI); } 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; DiagonalMatrix I(A.Ncols()); 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; DiagonalMatrix I(A.Ncols()); 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); } { Tracer et1("Stage 10"); // 2x2 evalue test SymmetricMatrix A(2); DiagonalMatrix D; Matrix V; Real a[] = {0.616556, 0.61544, 0.716556}; A << a; EigenValues(A,D,V); Matrix X = V * D * V.t() - A; Clean(X, 0.000000001); Print(X); Matrix Y = V * V.t() - IdentityMatrix(2); Clean(Y, 0.000000001); Print(Y); D.cleanup(); V.cleanup(); Jacobi(A,D,V); X = V * D * V.t() - A; Clean(X, 0.000000001); Print(X); Y = V * V.t() - IdentityMatrix(2); Clean(Y, 0.000000001); Print(Y); } } ///@}