tmt2.cpp
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00007 
00008 //#define WANT_STREAM
00009 
00010 
00011 #include "include.h"
00012 
00013 #include "newmat.h"
00014 
00015 #include "tmt.h"
00016 
00017 #ifdef use_namespace
00018 using namespace NEWMAT;
00019 #endif
00020 
00021 
00022 /**************************** test program ******************************/
00023 
00024 
00025 void trymat2()
00026 {
00027 //   cout << "\nSecond test of Matrix package\n\n";
00028    Tracer et("Second test of Matrix package");
00029    Tracer::PrintTrace();
00030 
00031    int i,j;
00032 
00033    Matrix M(3,5);
00034    for (i=1; i<=3; i++) for (j=1; j<=5; j++) M(i,j) = 100*i + j;
00035    Matrix X(8,10);
00036    for (i=1; i<=8; i++) for (j=1; j<=10; j++) X(i,j) = 1000*i + 10*j;
00037    Matrix Y = X; Matrix Z = X;
00038    { X.SubMatrix(2,4,3,7) << M; }
00039    for (i=1; i<=3; i++) for (j=1; j<=5; j++) Y(i+1,j+2) = 100*i + j;
00040    Print(Matrix(X-Y));
00041 
00042 
00043    Real a[15]; Real* r = a;
00044    for (i=1; i<=3; i++) for (j=1; j<=5; j++) *r++ = 100*i + j;
00045    { Z.SubMatrix(2,4,3,7) << a; }
00046    Print(Matrix(Z-Y));
00047 
00048    { M=33; X.SubMatrix(2,4,3,7) << M; }
00049    { Z.SubMatrix(2,4,3,7) = 33; }
00050    Print(Matrix(Z-X));
00051 
00052    for (i=1; i<=8; i++) for (j=1; j<=10; j++) X(i,j) = 1000*i + 10*j;
00053    Y = X;
00054    UpperTriangularMatrix U(5);
00055    for (i=1; i<=5; i++) for (j=i; j<=5; j++) U(i,j) = 100*i + j;
00056    { X.SubMatrix(3,7,5,9) << U; }
00057    for (i=1; i<=5; i++) for (j=i; j<=5; j++) Y(i+2,j+4) = 100*i + j;
00058    for (i=1; i<=5; i++) for (j=1; j<i; j++) Y(i+2,j+4) = 0.0;
00059    Print(Matrix(X-Y));
00060    for (i=1; i<=8; i++) for (j=1; j<=10; j++) X(i,j) = 1000*i + 10*j;
00061    Y = X;
00062    for (i=1; i<=5; i++) for (j=i; j<=5; j++) U(i,j) = 100*i + j;
00063    { X.SubMatrix(3,7,5,9).Inject(U); }
00064    for (i=1; i<=5; i++) for (j=i; j<=5; j++) Y(i+2,j+4) = 100*i + j;
00065    Print(Matrix(X-Y));
00066 
00067 
00068    // test growing and shrinking a vector
00069    {
00070       ColumnVector V(100);
00071       for (i=1;i<=100;i++) V(i) = i*i+i;
00072       V = V.Rows(1,50);               // to get first 50 vlaues.
00073 
00074       {
00075          V.Release(); ColumnVector VX=V;
00076          V.ReSize(100); V = 0.0; V.Rows(1,50)=VX;
00077       }                               // V now length 100
00078 
00079       M=V; M=100;                     // to make sure V will hold its values
00080       for (i=1;i<=50;i++) V(i) -= i*i+i;
00081       Print(V);
00082 
00083 
00084            // test redimensioning vectors with two dimensions given
00085       ColumnVector CV1(10); CV1 = 10;
00086       ColumnVector CV2(5); CV2.ReSize(10,1); CV2 = 10;
00087       V = CV1-CV2; Print(V);
00088 
00089       RowVector RV1(20); RV1 = 100;
00090       RowVector RV2; RV2.ReSize(1,20); RV2 = 100;
00091       V = (RV1-RV2).t(); Print(V);
00092 
00093       X.ReSize(4,7);
00094       for (i=1; i<=4; i++) for (j=1; j<=7; j++) X(i,j) = 1000*i + 10*j;
00095       Y = 10.5 * X;
00096       Z = 7.25 - Y;
00097       M = Z + X * 10.5 - 7.25;
00098       Print(M);
00099       Y = 2.5 * X;
00100       Z = 9.25 + Y;
00101       M = Z - X * 2.5 - 9.25;
00102       Print(M);
00103       U.ReSize(8);
00104       for (i=1; i<=8; i++) for (j=i; j<=8; j++) U(i,j) = 100*i + j;
00105       Y = 100 - U;
00106       M = Y + U - 100;
00107       Print(M);
00108    }
00109 
00110    {
00111       SymmetricMatrix S,T;
00112 
00113       S << (U + U.t());
00114       T = 100 - S; M = T + S - 100; Print(M);
00115       T = 100 - 2 * S; M = T + S * 2 - 100; Print(M);
00116       X = 100 - 2 * S; M = X + S * 2 - 100; Print(M);
00117       T = S; T = 100 - T; M = T + S - 100; Print(M);
00118    }
00119 
00120    // test new
00121    {
00122       ColumnVector CV1; RowVector RV1;
00123       Matrix* MX; MX = new Matrix; if (!MX) Throw(Bad_alloc("New fails "));
00124       MX->ReSize(10,20);
00125       for (i = 1; i <= 10; i++) for (j = 1; j <= 20; j++)
00126          (*MX)(i,j) = 100 * i + j;
00127       ColumnVector* CV = new ColumnVector(10);
00128       if (!CV) Throw(Bad_alloc("New fails "));
00129       *CV << 1 << 2 << 3 << 4 << 5 << 6 << 7 << 8 << 9 << 10;
00130       RowVector* RV =  new RowVector(CV->t() | (*CV + 10).t());
00131       if (!RV) Throw(Bad_alloc("New fails "));
00132       CV1 = ColumnVector(10); CV1 = 1; RV1 = RowVector(20); RV1 = 1;
00133       *MX -= 100 * *CV * RV1 + CV1 * *RV;
00134       Print(*MX);
00135       delete MX; delete CV; delete RV;
00136    }
00137 
00138 
00139    // test copying of vectors and matrices with no elements
00140    {
00141       ColumnVector dims(16);
00142       Matrix M1; Matrix M2 = M1; Print(M2);
00143       dims(1) = M2.Nrows(); dims(2) = M2.Ncols();
00144       dims(3) = (Real)(unsigned long)M2.Store(); dims(4) = M2.Storage();
00145       M2 = M1;
00146       dims(5) = M2.Nrows(); dims(6) = M2.Ncols();
00147       dims(7) = (Real)(unsigned long)M2.Store(); dims(8) = M2.Storage();
00148       M2.ReSize(10,20); M2.CleanUp();
00149       dims(9) = M2.Nrows(); dims(10) = M2.Ncols();
00150       dims(11) = (Real)(unsigned long)M2.Store(); dims(12) = M2.Storage();
00151       M2.ReSize(20,10); M2.ReSize(0,0);
00152       dims(13) = M2.Nrows(); dims(14) = M2.Ncols();
00153       dims(15) = (Real)(unsigned long)M2.Store(); dims(16) = M2.Storage();
00154       Print(dims);
00155    }
00156 
00157    {
00158       ColumnVector dims(16);
00159       ColumnVector M1; ColumnVector M2 = M1; Print(M2);
00160       dims(1) = M2.Nrows(); dims(2) = M2.Ncols()-1;
00161       dims(3) = (Real)(unsigned long)M2.Store(); dims(4) = M2.Storage();
00162       M2 = M1;
00163       dims(5) = M2.Nrows(); dims(6) = M2.Ncols()-1;
00164       dims(7) = (Real)(unsigned long)M2.Store(); dims(8) = M2.Storage();
00165       M2.ReSize(10); M2.CleanUp();
00166       dims(9) = M2.Nrows(); dims(10) = M2.Ncols()-1;
00167       dims(11) = (Real)(unsigned long)M2.Store(); dims(12) = M2.Storage();
00168       M2.ReSize(10); M2.ReSize(0);
00169       dims(13) = M2.Nrows(); dims(14) = M2.Ncols()-1;
00170       dims(15) = (Real)(unsigned long)M2.Store(); dims(16) = M2.Storage();
00171       Print(dims);
00172    }
00173 
00174    {
00175       ColumnVector dims(16);
00176       RowVector M1; RowVector M2 = M1; Print(M2);
00177       dims(1) = M2.Nrows()-1; dims(2) = M2.Ncols();
00178       dims(3) = (Real)(unsigned long)M2.Store(); dims(4) = M2.Storage();
00179       M2 = M1;
00180       dims(5) = M2.Nrows()-1; dims(6) = M2.Ncols();
00181       dims(7) = (Real)(unsigned long)M2.Store(); dims(8) = M2.Storage();
00182       M2.ReSize(10); M2.CleanUp();
00183       dims(9) = M2.Nrows()-1; dims(10) = M2.Ncols();
00184       dims(11) = (Real)(unsigned long)M2.Store(); dims(12) = M2.Storage();
00185       M2.ReSize(10); M2.ReSize(0);
00186       dims(13) = M2.Nrows()-1; dims(14) = M2.Ncols();
00187       dims(15) = (Real)(unsigned long)M2.Store(); dims(16) = M2.Storage();
00188       Print(dims);
00189    }
00190 
00191    // test identity matrix
00192    {
00193       Matrix M;
00194       IdentityMatrix I(10); DiagonalMatrix D(10); D = 1;
00195       M = I; M -= D; Print(M);
00196       D -= I; Print(D);
00197       ColumnVector X(8);
00198       D = 1;
00199       X(1) = Sum(D) - Sum(I);
00200       X(2) = SumAbsoluteValue(D) - SumAbsoluteValue(I);
00201       X(3) = SumSquare(D) - SumSquare(I);
00202       X(4) = Trace(D) - Trace(I);
00203       X(5) = Maximum(D) - Maximum(I);
00204       X(6) = Minimum(D) - Minimum(I);
00205       X(7) = LogDeterminant(D).LogValue() - LogDeterminant(I).LogValue();
00206       X(8) = LogDeterminant(D).Sign() - LogDeterminant(I).Sign();
00207       Clean(X,0.00000001); Print(X);
00208 
00209       for (i = 1; i <= 10; i++) for (j = 1; j <= 10; j++)
00210          M(i,j) = 100 * i + j;
00211       Matrix N;
00212       N = M * I - M; Print(N);
00213       N = I * M - M; Print(N);
00214       N = M * I.i() - M; Print(N);
00215       N = I.i() * M - M; Print(N);
00216       N = I.i(); N -= I; Print(N);
00217       N = I.t(); N -= I; Print(N);
00218       N = I.t(); N += (-I); Print(N); // <----------------
00219       D = I; N = D; D = 1; N -= D; Print(N);
00220       N = I; D = 1; N -= D; Print(N);
00221       N = M + 2 * IdentityMatrix(10); N -= (M + 2 * D); Print(N);
00222 
00223       I *= 4;
00224 
00225       D = 4;
00226 
00227       X.ReSize(14);
00228       X(1) = Sum(D) - Sum(I);
00229       X(2) = SumAbsoluteValue(D) - SumAbsoluteValue(I);
00230       X(3) = SumSquare(D) - SumSquare(I);
00231       X(4) = Trace(D) - Trace(I);
00232       X(5) = Maximum(D) - Maximum(I);
00233       X(6) = Minimum(D) - Minimum(I);
00234       X(7) = LogDeterminant(D).LogValue() - LogDeterminant(I).LogValue();  // <--
00235       X(8) = LogDeterminant(D).Sign() - LogDeterminant(I).Sign();
00236       int i,j;
00237       X(9) = I.Maximum1(i) - 4; X(10) = i-1;
00238       X(11) = I.Maximum2(i,j) - 4; X(12) = i-10; X(13) = j-10;
00239       X(14) = I.Nrows() - 10;
00240       Clean(X,0.00000001); Print(X);
00241 
00242 
00243       N = D.i();
00244       N += I / (-16);
00245       Print(N);
00246       N = M * I - 4 * M; Print(N);
00247       N = I * M - 4 * M; Print(N);
00248       N = M * I.i() - 0.25 * M; Print(N);
00249       N = I.i() * M - 0.25 * M; Print(N);
00250       N = I.i(); N -= I * 0.0625; Print(N);
00251       N = I.i(); N = N - 0.0625 * I; Print(N);
00252       N = I.t(); N -= I; Print(N);
00253       D = I * 2; N = D; D = 1; N -= 8 * D; Print(N);
00254       N = I * 2; N -= 8 * D; Print(N);
00255       N = 0.5 * I + M; N -= M; N -= 2.0 * D; Print(N);
00256 
00257       IdentityMatrix J(10); J = 8;
00258       D = 4;
00259       DiagonalMatrix E(10); E = 8;
00260       N = (I + J) - (D + E); Print(N);
00261       N = (5*I + 3*J) - (5*D + 3*E); Print(N);
00262       N = (-I + J) - (-D + E); Print(N);
00263       N = (I - J) - (D - E); Print(N);
00264       N = (I | J) - (D | E); Print(N);
00265       N = (I & J) - (D & E); Print(N);
00266       N = SP(I,J) - SP(D,E); Print(N);
00267       N = D.SubMatrix(2,5,3,8) - I.SubMatrix(2,5,3,8); Print(N);
00268 
00269       N = M; N.Inject(I); D << M; N -= (M + I); N += D; Print(N);
00270       D = 4;
00271 
00272       IdentityMatrix K = I.i()*7 - J.t()/4;
00273       N = D.i() * 7 - E / 4 - K; Print(N);
00274       K = I * J; N = K - D * E; Print(N);
00275       N = I * J; N -= D * E; Print(N);
00276       K = 5*I - 3*J;
00277       N = K - (5*D - 3*E); Print(N);
00278       K = I.i(); N = K - 0.0625 * I; Print(N);
00279       K = I.t(); N = K - I; Print(N);
00280 
00281 
00282       K.ReSize(20); D.ReSize(20); D = 1;
00283       D -= K; Print(D);
00284 
00285       I.ReSize(3); J.ReSize(3); K = I * J; N = K - I; Print(N);
00286       K << D; N = K - D; Print(N);
00287    }
00288    
00289    // test add integer
00290    {
00291       Matrix X(2,3);
00292       X << 5.25 << 7.75 << 1.25
00293         << 9.00 << 1.00 << 2.50;
00294       Matrix Y = X;
00295       X = 10 + X;
00296       X += (-10);
00297       X -= Y;
00298       Print(X);
00299       
00300       // also test f suffix
00301       X << 5.25f << 7.75f << 1.25f
00302         << 9.00f << 1.00f << 2.50f;
00303       X -= Y; Print(X);
00304       
00305    }
00306    
00307    
00308 
00309 
00310 //   cout << "\nEnd of second test\n";
00311 }
00312 
00313 


kni
Author(s): Neuronics AG (see AUTHORS.txt); ROS wrapper by Martin Günther
autogenerated on Mon Oct 6 2014 10:45:33