# Training patterns to cut and paste. # This file contains some training patterns to teach a neural # network to follow a wall. The first part of each pattern is a # list of eight inputs - one for each cell surrounding the agent. # If the input is 1, there is a wall in that cell; if it is 0 # the cell is empty. # Training patterns representing various cases of wall, bumps in # the wall and obstacles. training_patterns_more_complex = [ [[1,0,0,1,0,1,0,0],[0,0]], [[1,1,1,0,0,0,0,0],[0,1]], [[0,0,1,0,1,0,0,1],[1,0]], [[0,0,0,0,0,1,1,1],[1,1]], [[1,0,0,1,0,1,1,1],[0,0]], [[1,1,1,1,1,0,0,0],[0,1]], [[1,1,1,0,1,0,0,1],[1,0]], [[0,0,1,0,1,1,1,1],[1,1]], [[1,0,0,0,0,0,0,0],[0,0]], [[0,0,1,0,0,0,0,0],[0,1]], [[0,0,0,0,0,0,0,1],[1,0]], [[0,0,0,0,0,1,0,0],[1,1]], [[0,0,0,1,0,0,0,0],[0,0]], [[0,1,0,0,0,0,0,0],[0,1]], [[0,0,0,0,1,0,0,0],[1,0]], [[0,0,0,0,0,0,1,0],[1,1]], [[1,0,0,1,0,0,0,0],[0,0]], [[0,1,1,0,0,0,0,0],[0,1]], [[0,0,0,0,1,0,0,1],[1,0]], [[0,0,0,0,0,1,1,0],[1,1]], [[0,0,0,1,0,1,0,0],[0,0]], [[1,1,0,0,0,0,0,0],[0,1]], [[0,0,1,0,1,0,0,0],[1,0]], [[0,0,0,0,0,0,1,1],[1,1]], [[0,0,0,0,0,0,0,0],[0,0]] ] # These patterns only represent cases that would arise in # simple rectangular room without any obstacles. training_patterns_simple = [ [[1,0,0,1,0,1,0,0],[0,0]], [[1,1,1,0,0,0,0,0],[0,1]], [[0,0,1,0,1,0,0,1],[1,0]], [[0,0,0,0,0,1,1,1],[1,1]], [[1,0,0,1,0,1,1,1],[0,0]], [[1,1,1,1,1,0,0,0],[0,1]], [[1,1,1,0,1,0,0,1],[1,0]], [[0,0,1,0,1,1,1,1],[1,1]], [[0,0,0,0,0,0,0,0],[0,0]] ]