localizer.cpp

/**
	localizer.cpp

	Purpose: implements a 2-dimensional histogram filter
	for a robot living on a colored cyclical grid by 
	correctly implementing the "initialize_beliefs", 
	"sense", and "move" functions.

	This file is incomplete! Your job is to make these
	functions work. Feel free to look at localizer.py 
	for working implementations which are written in python.
*/

#include "helpers.cpp"
#include <stdlib.h>
//#include "debugging_helpers.cpp"

using namespace std;

/**
	TODO - implement this function 
    
    Initializes a grid of beliefs to a uniform distribution. 

    @param grid - a two dimensional grid map (vector of vectors 
    	   of chars) representing the robot's world. For example:
    	   
    	   g g g
    	   g r g
    	   g g g
		   
		   would be a 3x3 world where every cell is green except 
		   for the center, which is red.

    @return - a normalized two dimensional grid of floats. For 
           a 2x2 grid, for example, this would be:

           0.25 0.25
           0.25 0.25
*/
vector< vector <float> > initialize_beliefs(vector< vector <char> > grid) {
	vector< vector <float> > newGrid;

	// your code here
  int height = grid.size();
  int width = grid[0].size();
  int area = height * width;
  float belief_per_cell = 1.0 / area;
  vector <float> row_line;
  for (int i=0;i<height;i++){
    row_line.clear();
    for (int j=0;j<width;j++){
      row_line.push_back(belief_per_cell);
    }
    newGrid.push_back(row_line);
  }
  
	return newGrid;
}

/**
	TODO - implement this function 
    
    Implements robot sensing by updating beliefs based on the 
    color of a sensor measurement 

	@param color - the color the robot has sensed at its location

	@param grid - the current map of the world, stored as a grid
		   (vector of vectors of chars) where each char represents a 
		   color. For example:

		   g g g
    	   g r g
    	   g g g

   	@param beliefs - a two dimensional grid of floats representing
   		   the robot's beliefs for each cell before sensing. For 
   		   example, a robot which has almost certainly localized 
   		   itself in a 2D world might have the following beliefs:

   		   0.01 0.98
   		   0.00 0.01

    @param p_hit - the RELATIVE probability that any "sense" is 
    	   correct. The ratio of p_hit / p_miss indicates how many
    	   times MORE likely it is to have a correct "sense" than
    	   an incorrect one.

   	@param p_miss - the RELATIVE probability that any "sense" is 
    	   incorrect. The ratio of p_hit / p_miss indicates how many
    	   times MORE likely it is to have a correct "sense" than
    	   an incorrect one.

    @return - a normalized two dimensional grid of floats 
    	   representing the updated beliefs for the robot. 
*/
vector< vector <float> > sense(char color, 
	vector< vector <char> > grid, 
	vector< vector <float> > beliefs, 
	float p_hit,
	float p_miss) 
{
	vector< vector <float> > newGrid;

	// your code here
    int height = grid.size();
    int width = grid[0].size();
    vector<float> row_line;
    for (int i=0;i<height;i++){
      row_line.clear();
        for (int j=0; j<width;j++){
          if (color == grid[i][j]){
            row_line.push_back(beliefs[i][j]*p_hit);
          }
          else{
            row_line.push_back(beliefs[i][j]*p_miss);
          }            
        }
        newGrid.push_back(row_line);
    }
	return normalize(newGrid);
}


/**
	TODO - implement this function 
    
    Implements robot motion by updating beliefs based on the 
    intended dx and dy of the robot. 

    For example, if a localized robot with the following beliefs

    0.00  0.00  0.00
    0.00  1.00  0.00
    0.00  0.00  0.00 

    and dx and dy are both 1 and blurring is 0 (noiseless motion),
    than after calling this function the returned beliefs would be

    0.00  0.00  0.00
    0.00  0.00  0.00
    0.00  0.00  1.00 

	@param dy - the intended change in y position of the robot

	@param dx - the intended change in x position of the robot

   	@param beliefs - a two dimensional grid of floats representing
   		   the robot's beliefs for each cell before sensing. For 
   		   example, a robot which has almost certainly localized 
   		   itself in a 2D world might have the following beliefs:

   		   0.01 0.98
   		   0.00 0.01

    @param blurring - A number representing how noisy robot motion
           is. If blurring = 0.0 then motion is noiseless.

    @return - a normalized two dimensional grid of floats 
    	   representing the updated beliefs for the robot. 
*/
vector< vector <float> > move(int dy, int dx, 
	vector < vector <float> > beliefs,
	float blurring) 
{

	vector < vector <float> > newGrid;

	// your code here
  int height = beliefs.size();
  int width = beliefs[0].size();
  int new_i;
  int new_j;
  newGrid.assign(height, vector <float>(width,0.0));
  for (int i=0;i<height;i++){
    for (int j=0; j<width;j++){
      new_i = mod((i + dy),height);
      new_j = mod((j + dx),width);
      newGrid[new_i][new_j] = beliefs[i][j];
    }
          
  }     

	return blur(newGrid, blurring);
}