came up with a solution to example 1

json_tests.py

@@ -6,10 +6,10 @@ import numpy as np
 from main import load_task, grid2FOL, FOL2grid, FOL2prolog, prolog2FOL_array
 
 
-class MyTestCase(unittest.TestCase):
+class TaskTester(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
-        cls.task = load_task()
+        cls.task = load_task()#"data/training/0b148d64.json")
         # Example of accessing input/output grids for the first example
         cls.input_grid = cls.task['train'][0]['input']
         cls.output_grid = cls.task['train'][0]['output']
@@ -25,10 +25,14 @@ class MyTestCase(unittest.TestCase):
 
     def test_print(self):
         out_prolog = FOL2prolog(self.out_preds)
-
         out_FOL_array = prolog2FOL_array(out_prolog)
-
         out_grid = FOL2grid(out_FOL_array)
+        self.assertTrue((out_grid == self.output_grid).all())
+
+    def test_prolog_program(self):
+        in_prolog = FOL2prolog(self.in_preds)
+        with open('prolog/input_example_1.pl', 'w') as file:
+            file.write(in_prolog)
 
 
 

main.py

@@ -128,27 +128,6 @@ def load_jsons():
     # Load a single ARC task
     task = load_task()
 
-    # Example of accessing input/output grids for the first example
-    input_grid = task['train'][0]['input']
-    output_grid = task['train'][0]['output']
-
-    # rgb_grid = array_and_plot_grid(input_grid)
-    array_and_plot_grid(output_grid)
-
-    in_preds = grid2FOL(input_grid, "input")
-    out_preds = grid2FOL(output_grid, "output")
-    array_and_plot_grid(FOL2grid(out_preds))
-
-    old_out_preds = copy.deepcopy(out_preds)
-    np.random.shuffle(out_preds)
-    [np.random.shuffle(x) for x in out_preds]
-
-    assert (FOL2grid(out_preds) == FOL2grid(old_out_preds)).all()
-
-    out_grid = FOL2grid(out_preds)
-    array_and_plot_grid(out_grid)
-
-    return
 
 
 def load_task(json_file='data/training/0a938d79.json'):

prolog/solution_1.pl

+
+
+nrow(NR):-
+    input_colour(NR,_,_),
+    input_colour(R2,_,_),
+    \+ R2 > NR.
+
+ncol(NC):-
+    input_colour(_,NC,_),
+    input_colour(_,C2,_),
+    \+ C2 > NC.
+
+vertical_colour(C,Colour):-
+    input_colour(R,C,Colour),
+    R is 0,
+    \+ Colour is 'black'.
+
+vertical_colour(C,Colour):-
+    nrow(NR),
+    input_colour(NR,C,Colour),
+    \+ Colour is 'black'.
+
+
+horizontal_colour(R,Colour):-
+    input_colour(R,C,Colour),
+    C is 0,
+    \+ Colour is 'black'.
+
+horizontal_colour(R,Colour):-
+    ncol(NC),
+    input_colour(R,NC,Colour),
+    \+ Colour is 'black'.
+
+
+horizontal_diff(N):-
+    horizontal_colour(R,_),
+    horizontal_colour(R2,_),
+    R2 > R,
+    N is R2 - R.
+
+vertical_diff(N):-
+    vertical_colour(C,_),
+    vertical_colour(C2,_),
+    C2 > C,
+    N is C2 - C.
+
+remainder(N,D,R):-
+    integer(X),
+    R is N - X * D.
+
+output_colour(_,C,Colour):-
+    vertical_colour(Cstart,Colour),
+    vertical_diff(VD),
+    C >= Cstart,
+    VDtwo is 2 * VD,
+    remainder(C-Cstart, VDtwo, R),
+    R == 0.
+
+output_colour(R,_,Colour):-
+    horizontal_colour(Rstart,Colour),
+    horizontal_diff(VD),
+    R >= Rstart,
+    VDtwo is 2 * VD,
+    remainder(R-Rstart, VDtwo, Rem),
+    Rem == 0.
+
+
+output_colour(R,C,black):-
+    \+ output_colour(R,C,_).
+