/* Point_bayes.cpp - use bayesian optimization

   James S. Plank
   CS494/594 Lecture notes.
   Mon May 18 12:10:21 EDT 2020
 */

#include "points.hpp"
#include "bayesopt/bayesopt.h"                 // This is what you include to get the "C" API
#include <cmath>
#include <iostream>
#include <fstream>
#include <stdexcept>
#include <sstream>
#include <cstdio>
#include <cstdlib>
using namespace std;

typedef std::runtime_error SRE;

bool Print = false;

/* To use BayesOpt, you need to define a testing function with their prototype: */

double min_function(unsigned int n,      // Number of dimensions
                    const double *x,     // The point to test - an array of n dobules
                    double *gradient,    // Just set this to NULL.
                    void *func_data)     // You use this to pass data to the function. 
                                         // In our case, it will be a pointer to the Points class
{
  Points *p;
  vector <double> xv;
  unsigned int i;
  double rv;

  gradient = NULL;                        // We don't use this, so set it to NULL

  /* My code simply converts the data from their format into a Min_Distance() call. */

  p = (Points *) func_data;
  for (i = 0; i < n; i++) xv.push_back(x[i]);
  rv = p->Min_Distance(xv);
  if (Print) {
    for (i = 0; i < n; i++) printf(" %lg", x[i]);
    printf("  %lg\n", rv);
  }
  return -rv;      /* I return the negation, because the optimizer does minimization. */
}

int main(int argc, char **argv)
{
  Points points;
  double *lower, *upper, *x, *minf;
  long long seed;
  long long iterations;
  bopt_params params;
  size_t i;

  if (argc != 4) { 
    fprintf(stderr, "usage: points_bayes iterations seed print(t/f) - points on stdin\n"); 
    exit(1); 
  }
  if (sscanf(argv[1], "%lld", &iterations) != 1) { fprintf(stderr, "Bad iterations\n"); exit(0); }
  if (sscanf(argv[2], "%lld", &seed) != 1) { fprintf(stderr, "Bad seed\n"); exit(0); }
  if ((string) argv[3] == "t") Print = true;

  /* Read the points from stdin. */

  try {
    points.Read(cin);
  } catch (const SRE &e) {
    fprintf(stderr, "Error reading points on stdin:\n%s\n", e.what());
    exit(1);
  }

  /* Set the parameters of the Bayesian Optimizer */

  params = initialize_parameters_to_default();
  params.n_iterations = iterations;
  params.random_seed = seed;
  params.verbose_level = 0;
  lower = (double *) malloc(sizeof(double) * points.p[0].size());
  upper = (double *) malloc(sizeof(double) * points.p[0].size());
  x = (double *) malloc(sizeof(double) * points.p[0].size());
  minf = (double *) malloc(sizeof(double) * points.p[0].size());

  for (i = 0; i < points.p[0].size(); i++) {
    lower[i] = 1.0;
    upper[i] = 9.0;
  }

  bayes_optimization((int) points.p[0].size(),
                     min_function,
                     (void *) &points,
                     lower,
                     upper,
                     x,
                     minf,
                     params);

  printf("Best x:");
  for (i = 0; i < points.p[0].size(); i++) printf(" %lg", x[i]); 
  printf("\n");
  printf("Max: %lg\n", -minf[0]);
  return 0;
}