Fleet  0.0.9
Inference in the LOT
Classes | Functions | Variables
Main.cpp File Reference
#include <vector>
#include <string>
#include <random>
#include "Random.h"
#include "Grammar.h"
#include "Singleton.h"
#include "StochasticLOTHypothesis.h"
#include "TopN.h"
#include "ParallelTempering.h"
#include "Fleet.h"
Include dependency graph for Main.cpp:

Classes

class  MyHypothesis
 we don't need inputs/outputs for out MyHypothesis More...
 

Functions

double ANSzero (const double n1, const double n2)
 
double ANSdiff (const double n1, const double n2)
 

Variables

double recursion_penalty = -75.0
 

Function Documentation

◆ ANSdiff()

double ANSdiff ( const double  n1,
const double  n2 
)

◆ ANSzero()

double ANSzero ( const double  n1,
const double  n2 
)
Set up some basic variables for the model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
typedef int word;
typedef std::string set; /* This models sets as strings */
typedef char objectkind; // store this as that
typedef short wmset; // just need an integerish type
typedef float magnitude;
struct utterance { set s; objectkind o; };
std::vector<objectkind> OBJECTS = {'a', 'b', 'c', 'd', 'e'}; //, 'f', 'g', 'h', 'i', 'j'};
std::vector<std::string> WORDS = {"one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten"};
std::vector<magnitude> MAGNITUDES = {1,2,3,4,5,6,7,8,9,10};
const word U = -999;
const size_t MAX_SET_SIZE = 25;
const double alpha = 0.9;
const double W = 0.2; // weber ratio for ans
std::discrete_distribution<> number_distribution({0, 7187, 1484, 593, 334, 297, 165, 151, 86, 105, 112}); // 0-indexed
std::vector<int> data_amounts = {1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 200, 250, 300, 350, 400, 500, 600, 1000};//, 500, 600, 700, 800, 900, 1000};
/*

Define the primitives ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Variable Documentation

◆ recursion_penalty

double recursion_penalty = -75.0