//整理 by RobinKin #include <blitz/vector.h> using namespace blitz; int main() { // In this example, the function cos(x)^2 and its second derivative // 2 (sin(x)^2 - cos(x)^2) are sampled over the range [0,1). // The second derivative is approximated numerically using a // [ 1 -2 1 ] mask, and the approximation error is computed. /* cos(x)^2 的二阶导数 是2 (sin(x)^2 - cos(x)^2) 下面在[0,1) 的范围中,以delta为步长 ,用[ 1 -2 1 ] 的方法计算二阶导数 看看和精确值的误差 */ const int numSamples = 100; // Number of samples double delta = 1. / numSamples; // Spacing of samples Range R(0, numSamples - 1); // Index set of the vector
// Sample the function y = cos(x)^2 over [0,1) // // An object of type Range can be treated as a vector, and used // as a term in vector expressions. // // The initialization for y (below) will be translated via expression // templates into something of the flavour // // for (unsigned i=0; i < 99; ++i) // { // double _t1 = cos(i * delta); // y[i] = _t1 * _t1; // } Vector<double> y = sqr(cos(R * delta)); // Sample the exact second derivative Vector<double> y2exact = 2.0 * (sqr(sin(R * delta)) - sqr(cos(R * delta))); // Approximate the 2nd derivative using a [ 1 -2 1 ] mask // We can only apply this mask to the elements 1 .. 98, since // we need one element on either side to apply the mask. Range I(1,numSamples-2); Vector<double> y2(numSamples); y2(I) = (y(I-1) - 2 * y(I) + y(I+1)) / (delta*delta); // The above difference equation will be transformed into // something along the lines of // // double _t2 = delta*delta; // for (int i=1; i < 99; ++i) // y2[i] = (y[i-1] - 2 * y[i] + y[i+1]) / _t2; // Now calculate the root mean square approximation error: double error = sqrt(mean(sqr(y2(I) - y2exact(I)))); // Display a few elements from the vectors. // This range constructor means elements 1 to 91 in increments // of 15. Range displayRange(1, 91, 15); cout << "Exact derivative:" << y2exact(displayRange) << endl << "Approximation: " << y2(Range(displayRange)) << endl << "RMS Error: " << error << endl; return 0; } Output:
Exact derivative:[ -1.9996 -1.89847 -1.62776 -1.21164 -0.687291 -0.1015495 ] Approximation: [ -1.99953 -1.89841 -1.6277 -1.2116 -0.687269 -0.1015468 ] RMS Error: 4.24826e-05 
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