# include # include # include # include # include using namespace std; # include "circle_monte_carlo.hpp" //****************************************************************************80 double circle01_length ( ) //****************************************************************************80 // // Purpose: // // CIRCLE01_LENGTH: length of the circumference of the unit circle in 2D. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 11 January 2014 // // Author: // // John Burkardt // // Parameters: // // Output, double CIRCLE01_LENGTH, the length. // { double length; const double r = 1.0; const double r8_pi = 3.141592653589793; length = 2.0 * r8_pi * r; return length; } //****************************************************************************80 double circle01_monomial_integral ( int e[2] ) //****************************************************************************80 // // Purpose: // // CIRCLE01_MONOMIAL_INTEGRAL returns monomial integrals on the unit circle. // // Discussion: // // The integration region is // // X^2 + Y^2 = 1. // // The monomial is F(X,Y) = X^E(1) * Y^E(2). // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 11 January 2014 // // Author: // // John Burkardt // // Reference: // // Philip Davis, Philip Rabinowitz, // Methods of Numerical Integration, // Second Edition, // Academic Press, 1984, page 263. // // Parameters: // // Input, int E[2], the exponents of X and Y in the // monomial. Each exponent must be nonnegative. // // Output, double CIRCLE01_MONOMIAL_INTEGRAL, the integral. // { double arg; int i; double integral; if ( e[0] < 0 || e[1] < 0 ) { cout << "\n"; cout << "CIRCLE01_MONOMIAL_INTEGRAL - Fatal error!\n"; cout << " All exponents must be nonnegative.\n"; cout << " E[0] = " << e[0] << "\n"; cout << " E[1] = " << e[1] << "\n"; exit ( 1 ); } if ( ( e[0] % 2 ) == 1 || ( e[1] % 2 ) == 1 ) { integral = 0.0; } else { integral = 2.0; for ( i = 0; i < 2; i++ ) { arg = 0.5 * ( double ) ( e[i] + 1 ); integral = integral * r8_gamma ( arg ); } arg = 0.5 * ( double ) ( e[0] + e[1] + 2 ); integral = integral / r8_gamma ( arg ); } return integral; } //****************************************************************************80 double *circle01_sample_ergodic ( int n, double &angle ) //****************************************************************************80 // // Purpose: // // CIRCLE01_SAMPLE_ERGODIC samples the circumference of the unit circle in 2D. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 06 June 2017 // // Author: // // John Burkardt // // Reference: // // Russell Cheng, // Random Variate Generation, // in Handbook of Simulation, // edited by Jerry Banks, // Wiley, 1998, pages 168. // // Reuven Rubinstein, // Monte Carlo Optimization, Simulation, and Sensitivity // of Queueing Networks, // Krieger, 1992, // ISBN: 0894647644, // LC: QA298.R79. // // Parameters: // // Input, int N, the number of points. // // Input/output, double ANGLE, the base angle, which could be anything // in the range [0,2 PI). // // Output, double X[2*N], the points. // { const double c[2] = { 0.0, 0.0 }; double golden_angle; double golden_ratio; int j; const double r = 1.0; const double r8_pi = 3.141592653589793; double *x; golden_ratio = ( 1.0 + sqrt ( 5.0 ) ) / 2.0; golden_angle = 2.0 * r8_pi / pow ( golden_ratio, 2 ); x = ( double * ) malloc ( 2 * n * sizeof ( double ) ); for ( j = 0; j < n; j++ ) { x[0+j*2] = c[0] + r * cos ( angle ); x[1+j*2] = c[1] + r * sin ( angle ); angle = fmod ( angle + golden_angle, 2.0 * r8_pi ) ; } return x; } //****************************************************************************80 double *circle01_sample_random ( int n, int &seed ) //****************************************************************************80 // // Purpose: // // CIRCLE01_SAMPLE_RANDOM samples the circumference of the unit circle in 2D. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 06 June 2017 // // Author: // // John Burkardt // // Reference: // // Russell Cheng, // Random Variate Generation, // in Handbook of Simulation, // edited by Jerry Banks, // Wiley, 1998, pages 168. // // Reuven Rubinstein, // Monte Carlo Optimization, Simulation, and Sensitivity // of Queueing Networks, // Krieger, 1992, // ISBN: 0894647644, // LC: QA298.R79. // // Parameters: // // Input, int N, the number of points. // // Input/output, int &SEED, a seed for the random // number generator. // // Output, double X[2*N], the points. // { const double c[2] = { 0.0, 0.0 }; int j; const double r = 1.0; const double r8_pi = 3.141592653589793; double *theta; double *x; theta = r8vec_uniform_01_new ( n, seed ); x = new double[2*n]; for ( j = 0; j < n; j++ ) { x[0+j*2] = c[0] + r * cos ( 2.0 * r8_pi * theta[j] ); x[1+j*2] = c[1] + r * sin ( 2.0 * r8_pi * theta[j] ); } delete [] theta; return x; } //****************************************************************************80 double *monomial_value ( int m, int n, int e[], double x[] ) //****************************************************************************80 // // Purpose: // // MONOMIAL_VALUE evaluates a monomial. // // Discussion: // // This routine evaluates a monomial of the form // // product ( 1 <= i <= m ) x(i)^e(i) // // where the exponents are nonnegative integers. Note that // if the combination 0^0 is encountered, it should be treated // as 1. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 08 May 2014 // // Author: // // John Burkardt // // Parameters: // // Input, int M, the spatial dimension. // // Input, int N, the number of points at which the // monomial is to be evaluated. // // Input, int E[M], the exponents. // // Input, double X[M*N], the point coordinates. // // Output, double MONOMIAL_VALUE[N], the value of the monomial. // { int i; int j; double *v; v = new double[n]; for ( j = 0; j < n; j++ ) { v[j] = 1.0; } for ( i = 0; i < m; i++ ) { if ( 0 != e[i] ) { for ( j = 0; j < n; j++ ) { v[j] = v[j] * pow ( x[i+j*m], e[i] ); } } } return v; } //****************************************************************************80 double r8_gamma ( double x ) //****************************************************************************80 // // Purpose: // // R8_GAMMA evaluates Gamma(X) for a real argument. // // Discussion: // // The C MATH library includes a function GAMMA ( X ) which should be // invoked instead of this function. // // This routine calculates the gamma function for a real argument X. // // Computation is based on an algorithm outlined in reference 1. // The program uses rational functions that approximate the gamma // function to at least 20 significant decimal digits. Coefficients // for the approximation over the interval (1,2) are unpublished. // Those for the approximation for 12 <= X are from reference 2. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 18 January 2008 // // Author: // // Original FORTRAN77 version by William Cody, Laura Stoltz. // C++ version by John Burkardt. // // Reference: // // William Cody, // An Overview of Software Development for Special Functions, // in Numerical Analysis Dundee, 1975, // edited by GA Watson, // Lecture Notes in Mathematics 506, // Springer, 1976. // // John Hart, Ward Cheney, Charles Lawson, Hans Maehly, // Charles Mesztenyi, John Rice, Henry Thatcher, // Christoph Witzgall, // Computer Approximations, // Wiley, 1968, // LC: QA297.C64. // // Parameters: // // Input, double X, the argument of the function. // // Output, double R8_GAMMA, the value of the function. // { double c[7] = { -1.910444077728E-03, 8.4171387781295E-04, -5.952379913043012E-04, 7.93650793500350248E-04, -2.777777777777681622553E-03, 8.333333333333333331554247E-02, 5.7083835261E-03 }; double eps = 2.22E-16; double fact; int i; int n; double p[8] = { -1.71618513886549492533811E+00, 2.47656508055759199108314E+01, -3.79804256470945635097577E+02, 6.29331155312818442661052E+02, 8.66966202790413211295064E+02, -3.14512729688483675254357E+04, -3.61444134186911729807069E+04, 6.64561438202405440627855E+04 }; bool parity; double pi = 3.1415926535897932384626434; double q[8] = { -3.08402300119738975254353E+01, 3.15350626979604161529144E+02, -1.01515636749021914166146E+03, -3.10777167157231109440444E+03, 2.25381184209801510330112E+04, 4.75584627752788110767815E+03, -1.34659959864969306392456E+05, -1.15132259675553483497211E+05 }; double res; double sqrtpi = 0.9189385332046727417803297; double sum; double value; double xbig = 171.624; double xden; double xinf = 1.79E+308; double xminin = 2.23E-308; double xnum; double y; double y1; double ysq; double z; parity = false; fact = 1.0; n = 0; y = x; // // Argument is negative. // if ( y <= 0.0 ) { y = - x; y1 = ( double ) ( int ) ( y ); res = y - y1; if ( res != 0.0 ) { if ( y1 != ( double ) ( int ) ( y1 * 0.5 ) * 2.0 ) { parity = true; } fact = - pi / sin ( pi * res ); y = y + 1.0; } else { res = xinf; value = res; return value; } } // // Argument is positive. // if ( y < eps ) { // // Argument < EPS. // if ( xminin <= y ) { res = 1.0 / y; } else { res = xinf; value = res; return value; } } else if ( y < 12.0 ) { y1 = y; // // 0.0 < argument < 1.0. // if ( y < 1.0 ) { z = y; y = y + 1.0; } // // 1.0 < argument < 12.0. // Reduce argument if necessary. // else { n = ( int ) ( y ) - 1; y = y - ( double ) ( n ); z = y - 1.0; } // // Evaluate approximation for 1.0 < argument < 2.0. // xnum = 0.0; xden = 1.0; for ( i = 0; i < 8; i++ ) { xnum = ( xnum + p[i] ) * z; xden = xden * z + q[i]; } res = xnum / xden + 1.0; // // Adjust result for case 0.0 < argument < 1.0. // if ( y1 < y ) { res = res / y1; } // // Adjust result for case 2.0 < argument < 12.0. // else if ( y < y1 ) { for ( i = 1; i <= n; i++ ) { res = res * y; y = y + 1.0; } } } else { // // Evaluate for 12.0 <= argument. // if ( y <= xbig ) { ysq = y * y; sum = c[6]; for ( i = 0; i < 6; i++ ) { sum = sum / ysq + c[i]; } sum = sum / y - y + sqrtpi; sum = sum + ( y - 0.5 ) * log ( y ); res = exp ( sum ); } else { res = xinf; value = res; return value; } } // // Final adjustments and return. // if ( parity ) { res = - res; } if ( fact != 1.0 ) { res = fact / res; } value = res; return value; } //****************************************************************************80 double r8vec_sum ( int n, double a[] ) //****************************************************************************80 // // Purpose: // // R8VEC_SUM returns the sum of an R8VEC. // // Discussion: // // An R8VEC is a vector of R8's. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 15 October 2004 // // Author: // // John Burkardt // // Parameters: // // Input, int N, the number of entries in the vector. // // Input, double A[N], the vector. // // Output, double R8VEC_SUM, the sum of the vector. // { int i; double value; value = 0.0; for ( i = 0; i < n; i++ ) { value = value + a[i]; } return value; } //****************************************************************************80 double *r8vec_uniform_01_new ( int n, int &seed ) //****************************************************************************80 // // Purpose: // // R8VEC_UNIFORM_01_NEW returns a new unit pseudorandom R8VEC. // // Discussion: // // This routine implements the recursion // // seed = ( 16807 * seed ) mod ( 2^31 - 1 ) // u = seed / ( 2^31 - 1 ) // // The integer arithmetic never requires more than 32 bits, // including a sign bit. // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 19 August 2004 // // Author: // // John Burkardt // // Reference: // // Paul Bratley, Bennett Fox, Linus Schrage, // A Guide to Simulation, // Second Edition, // Springer, 1987, // ISBN: 0387964673, // LC: QA76.9.C65.B73. // // Bennett Fox, // Algorithm 647: // Implementation and Relative Efficiency of Quasirandom // Sequence Generators, // ACM Transactions on Mathematical Software, // Volume 12, Number 4, December 1986, pages 362-376. // // Pierre L'Ecuyer, // Random Number Generation, // in Handbook of Simulation, // edited by Jerry Banks, // Wiley, 1998, // ISBN: 0471134031, // LC: T57.62.H37. // // Peter Lewis, Allen Goodman, James Miller, // A Pseudo-Random Number Generator for the System/360, // IBM Systems Journal, // Volume 8, Number 2, 1969, pages 136-143. // // Parameters: // // Input, int N, the number of entries in the vector. // // Input/output, int &SEED, a seed for the random number generator. // // Output, double R8VEC_UNIFORM_01_NEW[N], the vector of pseudorandom values. // { int i; int i4_huge = 2147483647; int k; double *r; if ( seed == 0 ) { cerr << "\n"; cerr << "R8VEC_UNIFORM_01_NEW - Fatal error!\n"; cerr << " Input value of SEED = 0.\n"; exit ( 1 ); } r = new double[n]; for ( i = 0; i < n; i++ ) { k = seed / 127773; seed = 16807 * ( seed - k * 127773 ) - k * 2836; if ( seed < 0 ) { seed = seed + i4_huge; } r[i] = ( double ) ( seed ) * 4.656612875E-10; } return r; } //****************************************************************************80 void timestamp ( ) //****************************************************************************80 // // Purpose: // // TIMESTAMP prints the current YMDHMS date as a time stamp. // // Example: // // 31 May 2001 09:45:54 AM // // Licensing: // // This code is distributed under the GNU LGPL license. // // Modified: // // 08 July 2009 // // Author: // // John Burkardt // // Parameters: // // None // { # define TIME_SIZE 40 static char time_buffer[TIME_SIZE]; const struct std::tm *tm_ptr; std::time_t now; now = std::time ( NULL ); tm_ptr = std::localtime ( &now ); std::strftime ( time_buffer, TIME_SIZE, "%d %B %Y %I:%M:%S %p", tm_ptr ); std::cout << time_buffer << "\n"; return; # undef TIME_SIZE }