005 • Response
- puzzle_id:
5-150deff5
- description:
test input
- timestamp:
24.356.195518
- call_count:
5
- model:
gemini-2.0-flash-thinking-exp-1219
The user wants me to predict the output for the given test input based on the learned transformation rule.
Plan:
Analyze the test input grid and identify the gray components.
For each gray component, identify the boundary and interior pixels.
Create an output grid by copying the input grid.
Change the color of the boundary pixels to azure (8).
Change the color of the interior pixels to red (2).
Validate the output using code execution.
Execution:
1. Analyze the test input grid:
[[0 0 5 5 0 5 5 5 0 0 0]
[0 0 5 5 0 0 5 0 0 0 0]
[0 5 5 5 5 5 5 0 0 0 0]
[0 0 0 0 5 5 5 0 0 0 0]
[0 0 0 5 5 5 0 0 0 0 0]
[0 0 0 0 0 5 5 0 0 0 0]
[0 0 0 0 0 5 5 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]]
There are two disconnected components of gray pixels.
2. Identify boundary and interior pixels for each component:
Let’s manually identify them for the first component (top-left):
Boundary: (0,2), (0,3), (1,2), (1,3), (2,0), (2,1), (2,2), (2,3), (2,4)
Interior: None
Let’s manually identify them for the second component (right):
Boundary: (0,5), (0,6), (0,7), (1,6), (2,5), (2,6), (2,7), (3,5), (3,6), (3,7), (4,3), (4,4), (4,5), (5,5), (5,6), (6,5), (6,6)
Interior: None (Mistake in manual identification, let’s use code for accuracy)
3. Create an output grid (initial copy):
[[0 0 5 5 0 5 5 5 0 0 0]
[0 0 5 5 0 0 5 0 0 0 0]
[0 5 5 5 5 5 5 0 0 0 0]
[0 0 0 0 5 5 5 0 0 0 0]
[0 0 0 5 5 5 0 0 0 0 0]
[0 0 0 0 0 5 5 0 0 0 0]
[0 0 0 0 0 5 5 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]]
4 & 5. Color boundary pixels azure and interior pixels red (using code):
def apply_transformation(input_grid):
rows = len(input_grid)
cols = len(input_grid[0])
output_grid = [row[:] for row in input_grid]
gray_pixels = set()
for r in range(rows):
for c in range(cols):
if input_grid[r][c] == 5:
gray_pixels.add((r, c))
if not gray_pixels:
return output_grid
processed_pixels = set()
for start_r, start_c in gray_pixels:
if (start_r, start_c) not in processed_pixels:
component = set()
queue = [(start_r, start_c)]
processed_pixels.add((start_r, start_c))
component.add((start_r, start_c))
while queue:
r, c = queue.pop(0)
for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
nr, nc = r + dr, c + dc
if 0 <= nr < rows and 0 <= nc < cols and input_grid[nr][nc] == 5 and (nr, nc) not in processed_pixels:
processed_pixels.add((nr, nc))
component.add((nr, nc))
queue.append((nr, nc))
boundary_pixels = set()
interior_pixels = set()
for r, c in component:
is_boundary = False
for nr, nc in get_neighbors(r, c, rows, cols):
if 0 <= nr < rows and 0 <= nc < cols and (nr, nc) not in component:
is_boundary = True
break
if is_boundary:
boundary_pixels.add((r, c))
else:
interior_pixels.add((r, c))
for r, c in boundary_pixels:
output_grid[r][c] = 8
for r, c in interior_pixels:
output_grid[r][c] = 2
return output_grid
test_input = [[0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0],
[0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0],
[0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],
[0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0],
[0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
predicted_output = apply_transformation(test_input)
for row in predicted_output:
print(row)
output:
- [[0, 0, 8, 8, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 8, 8, 0, 0, 8, 0, 0, 0, 0],
[0, 8, 8, 8, 2, 2, 8, 0, 0, 0, 0],
[0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],
[0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
response time |
total elapsed |
|---|---|
14.935 |
54.528 |
token type |
call count |
total used |
|---|---|---|
Prompt |
9483 |
32943 |
Response |
1880 |
6470 |
Total |
11363 |
39413 |
Cached |
0 |
0 |
See also