# 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 MIT 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 MIT 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 * tgamma ( arg ); } arg = 0.5 * ( double ) ( e[0] + e[1] + 2 ); integral = integral / tgamma ( 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 MIT 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 MIT 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 MIT 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 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 MIT 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 MIT 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 MIT 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 }