/* Points.hpp - Defining a data structure that holds a collection 
   of n-dimensional points, and implements a function that is the
   closest distance to any of the points.
 
   James S. Plank
   CS494/594 Lecture notes.
   Fri May 15 15:28:39 EDT 2020
  */

#pragma once 

#include <vector>
#include <istream>

/* This is a data structure to help demonstrate Bayesian Optimization.  See the 
   accompanying lecture notes. */

class Points {
  public:

    /* The only piece of data is a vector of points.  Each element of the vector is 
       a vector of n points, and each point will be a double between 0 and 10.  I'm 
       making it public, because I'm not in the mood to get all object oriented 
       about this. */

    std::vector < std::vector <double> > p;
  
    /* You'll note -- no constructors, destructors or assignment overloads.  Since the
       only piece of data is that vector of vectors, all the defaults will work. */

    /* Reads from a stream and double-checks that everything is correct.  If it
       encounters an error, it will throw an exception of type std::runtim_error. */

    void Read(std::istream &in);

    /* Here is a function that we want to maximize.  Given a point in this
       n-dimensional space, what is the smallest distance to a point in p? 
       If x's size is not equal to the number of dimensions of p, it will throw
       an exception of type std::runtim_error. */
       
    double Min_Distance(const std::vector <double> &x) const;
};