004 • Response
- puzzle_id:
1-3aa6fb7a
- description:
test input
- timestamp:
24.329.071439
- call_count:
4
- model:
models/gemini-exp-1114
code_execution_result: 1
Example 1 - Top left of input cluster: (1, 1)
Example 1 - Top left of input cluster: (3, 4)
Example 2 - Top left of input cluster: (0, 4)
Example 2 - Top left of input cluster: (2, 2)
Example 2 - Top left of input cluster: (5, 4)
Review of Findings
The code execution has correctly identified the top left pixel of each cluster in both examples. This validates our natural language program: Identify the top left pixel of each cluster of azure (8) pixels and change its color from white (0) to blue (1).
Pre-Test Phase
Test Input Grid Analysis
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]]
In this test grid, we have several clusters of azure (8) pixels.
In the top row, there’s a pair of horizontal azure (8) pixels.
In the second row, there are two azure (8) pixels followed by one azure (8) pixel separated by white (0) pixels.
In the third row, there is one azure (8) pixel at the beginning.
In the fourth row, there is one azure (8) pixel in the middle.
In the fifth row, there’s a pair of horizontal azure (8) pixels in the middle.
In the sixth row, there is one azure (8) pixel.
In the last row, there’s a pair of horizontal azure (8) pixels at the beginning.
Based on the program developed, we need to identify the top left pixel of each cluster and change its color from white (0) to blue (1). Let’s use code execution to determine the clusters and the top left pixel of each cluster.
code_execution:
import numpy as np
def find_clusters(grid, color):
clusters = []
visited = np.zeros_like(grid, dtype=bool)
def explore(row, col, cluster):
if row < 0 or row >= grid.shape[0] or col < 0 or col >= grid.shape[1] or visited[row, col] or grid[row, col] != color:
return
visited[row, col] = True
cluster.append((row, col))
explore(row + 1, col, cluster)
explore(row - 1, col, cluster)
explore(row, col + 1, cluster)
explore(row, col - 1, cluster)
for r in range(grid.shape[0]):
for c in range(grid.shape[1]):
if grid[r, c] == color and not visited[r, c]:
cluster = []
explore(r, c, cluster)
clusters.append(cluster)
return clusters
def find_top_left(cluster):
return min(cluster, key=lambda x: (x[0], x[1]))
# Test with the test input grid
test_input_grid = np.array([[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]])
test_clusters = find_clusters(test_input_grid, 8)
for cluster in test_clusters:
top_left = find_top_left(cluster)
print("Test Input - Top left of cluster:", top_left)
# Construct the output grid based on the transformation
test_output_grid = np.copy(test_input_grid)
for cluster in test_clusters:
top_left = find_top_left(cluster)
if test_output_grid[top_left] == 0: # Check if the pixel is white
test_output_grid[top_left] = 1
elif test_output_grid[top_left] == 8: # Handle the case where the top left is already blue
# Locate the nearest white pixel above or to the left and change it to blue
row, col = top_left
# Check for a white pixel above
found = False
for r in range(row - 1, -1, -1):
if test_output_grid[r, col] == 0:
test_output_grid[r, col] = 1
found = True
break
# If no white pixel found above, check to the left
if not found:
for c in range(col - 1, -1, -1):
if test_output_grid[row, c] == 0:
test_output_grid[row, c] = 1
break
print("Test Output Grid:")
print(test_output_grid)
response time |
total elapsed |
|---|---|
43.075 |
176.389 |
token type |
call count |
total used |
|---|---|---|
Prompt |
7466 |
20201 |
Response |
1329 |
4264 |
Total |
8795 |
24465 |
Cached |
0 |
0 |
See also