本文整理汇总了C++中DiagonalMatrix::cleanup方法的典型用法代码示例。如果您正苦于以下问题：C++ DiagonalMatrix::cleanup方法的具体用法？C++ DiagonalMatrix::cleanup怎么用？C++ DiagonalMatrix::cleanup使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类DiagonalMatrix的用法示例。
在下文中一共展示了DiagonalMatrix::cleanup方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: trymate
//.........这里部分代码省略.........
// 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);
}
}