004 • Response
- puzzle_id:
1-3aa6fb7a
- description:
test input
- timestamp:
24.356.194941
- call_count:
4
- model:
gemini-2.0-flash-thinking-exp-1219
The user has provided a test input grid and wants me to predict the output based on the derived natural language program.
Plan:
Analyze the test input grid: Identify the azure objects.
Apply the transformation rule: For each azure object, identify the topmost and bottommost pixels and change the adjacent white pixels to blue according to the rule.
Construct the predicted output grid.
Use ``code_execution`` to verify the prediction.
Analyze the test input grid:
[[0 0 0 0 0 8 8]
[8 8 0 0 0 0 8]
[8 0 0 0 0 0 0]
[0 0 0 8 0 0 0]
[0 0 0 8 8 0 0]
[0 8 0 0 0 0 0]
[8 8 0 0 0 0 0]]
Object 1: Located on the right side. Pixels: (0,5), (0,6), (1,6).
Topmost: (0,5)
Bottommost: (1,6)
Object 2: Located on the left side. Pixels: (1,0), (1,1), (2,0).
Topmost: (1,0)
Bottommost: (2,0)
Object 3: Located in the middle. Pixels: (3,3), (4,3), (4,4).
Topmost: (3,3)
Bottommost: (4,4)
Object 4: Located on the left side. Pixels: (5,1).
Topmost: (5,1)
Bottommost: (5,1)
Object 5: Located on the left side. Pixels: (6,0), (6,1).
Topmost: (6,0)
Bottommost: (6,1)
Apply the transformation rule:
Object 1 (Right):
Topmost (0,5): Right neighbor (0,6) is azure, no change.
Bottommost (1,6): Left neighbor (1,5) is white, change to blue.
Object 2 (Left-Top):
Topmost (1,0): Right neighbor (1,1) is azure, no change.
Bottommost (2,0): Left neighbor (2,-1) is out of bounds, no change.
Object 3 (Middle):
Topmost (3,3): Right neighbor (3,4) is white, change to blue.
Bottommost (4,4): Left neighbor (4,3) is azure, no change.
Object 4 (Left-Middle):
Topmost (5,1): Right neighbor (5,2) is white, change to blue.
Bottommost (5,1): Left neighbor (5,0) is white, change to blue.
Object 5 (Left-Bottom):
Topmost (6,0): Right neighbor (6,1) is azure, no change.
Bottommost (6,1): Left neighbor (6,0) is azure, no change.
Construct the predicted output grid:
[[0, 0, 0, 0, 0, 8, 8],
[8, 8, 0, 0, 1, 0, 8],
[8, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 8, 1, 0, 0],
[0, 0, 0, 8, 8, 0, 0],
[0, 8, 1, 0, 0, 0, 0],
[8, 8, 0, 0, 0, 0, 0]]
Validate with code execution:
def find_objects(grid, color):
rows = len(grid)
cols = len(grid[0])
visited = [[False for _ in range(cols)] for _ in range(rows)]
objects = []
for r in range(rows):
for c in range(cols):
if grid[r][c] == color and not visited[r][c]:
object_pixels = []
stack = [(r, c)]
visited[r][c] = True
while stack:
row, col = stack.pop()
object_pixels.append((row, col))
for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
nr, nc = row + dr, col + dc
if 0 <= nr < rows and 0 <= nc < cols and \
grid[nr][nc] == color and not visited[nr][nc]:
visited[nr][nc] = True
stack.append((nr, nc))
objects.append(object_pixels)
return objects
def transform_grid(input_grid):
rows = len(input_grid)
cols = len(input_grid[0])
output_grid = [row[:] for row in input_grid]
azure_objects = find_objects(input_grid, 8)
for obj in azure_objects:
if not obj:
continue
topmost_pixel = min(obj, key=lambda x: x[0])
bottommost_pixel = max(obj, key=lambda x: x[0])
# Check right of topmost
r, c = topmost_pixel
if c + 1 < cols and input_grid[r][c + 1] == 0:
output_grid[r][c + 1] = 1
# Check left of bottommost
r, c = bottommost_pixel
if c - 1 >= 0 and input_grid[r][c - 1] == 0:
output_grid[r][c - 1] = 1
return output_grid
test_input = [[0, 0, 0, 0, 0, 8, 8],
[8, 8, 0, 0, 0, 0, 8],
[8, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 8, 0, 0, 0],
[0, 0, 0, 8, 8, 0, 0],
[0, 8, 0, 0, 0, 0, 0],
[8, 8, 0, 0, 0, 0, 0]]
predicted_output = transform_grid(test_input)
print(predicted_output)
response time |
total elapsed |
|---|---|
13.217 |
55.021 |
token type |
call count |
total used |
|---|---|---|
Prompt |
8785 |
22020 |
Response |
1639 |
6799 |
Total |
10424 |
28819 |
Cached |
0 |
0 |
See also