006 • Response

puzzle_id:: 1-3aa6fb7a
description:: review working
timestamp:: 24.329.064813
call_count:: 6
model:: models/gemini-1.5-flash-002

The working output grid is currently identical to the test input grid. No changes have been made yet. To proceed, I need to apply the transformation rules encoded in the refined natural language program (v2) and the associated Python code.

Let’s execute the code to transform the test input grid and then review the results.

code_execution:

import numpy as np
from collections import deque

def transform_grid(grid):
    rows, cols = grid.shape
    visited = np.zeros((rows, cols), dtype=bool)
    new_grid = np.copy(grid)

    for r in range(rows):
        for c in range(cols):
            if grid[r, c] == 8 and not visited[r, c]:
                cluster = find_cluster(grid, r, c, visited)
                if len(cluster) > 1:
                    pixel_to_change = select_pixel(cluster, grid)
                    if pixel_to_change:
                        new_grid[pixel_to_change[0], pixel_to_change[1]] = 1

    return new_grid

def find_cluster(grid, r, c, visited):
    rows, cols = grid.shape
    queue = deque([(r, c)])
    visited[r, c] = True
    cluster = [(r, c)]

    while queue:
        row, col = queue.popleft()
        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] == 8 and not visited[nr, nc]:
                visited[nr, nc] = True
                queue.append((nr, nc))
                cluster.append((nr, nc))
    return cluster

def select_pixel(cluster, grid):
    # Calculate centroid
    x_coords, y_coords = zip(*cluster)
    centroid_x = np.mean(x_coords)
    centroid_y = np.mean(y_coords)

    # Find distances from centroid
    distances = [((x - centroid_x)**2 + (y - centroid_y)**2)**0.5 for x, y in cluster]
    max_distance = max(distances)
    farthest_pixels = [cluster[i] for i, dist in enumerate(distances) if dist == max_distance]


    #Prioritize bottom/left among farthest
    best_pixel = None
    min_dist_to_border = float('inf')
    for pixel in farthest_pixels:
        r,c = pixel
        dist = min(r,c, grid.shape[0]-1-r, grid.shape[1]-1-c)
        if dist < min_dist_to_border:
            min_dist_to_border = dist
            best_pixel = pixel


    return best_pixel



test_input = 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]])

transformed_grid = transform_grid(test_input)
print(f"Transformed Test Grid:\n{transformed_grid}")

code_execution_result:

Transformed Test Grid:
[[0 0 0 0 0 1 8]
 [8 8 0 0 0 0 8]
 [1 0 0 0 0 0 0]
 [0 0 0 8 0 0 0]
 [0 0 0 8 1 0 0]
 [0 1 0 0 0 0 0]
 [8 8 0 0 0 0 0]]

The code has produced a transformed grid. Three azure pixels have changed to blue, consistent with the observed pattern in previous examples. The selection seems reasonable based on the algorithm’s logic (prioritizing pixels further from the centroid and closer to the edges). Without a provided solution, it’s impossible to definitively confirm its correctness for this specific test case, but the transformation appears plausible given the rules derived from previous examples.

working output grid

[[0 0 0 0 0 1 8]
 [8 8 0 0 0 0 8]
 [1 0 0 0 0 0 0]
 [0 0 0 8 0 0 0]
 [0 0 0 8 1 0 0]
 [0 1 0 0 0 0 0]
 [8 8 0 0 0 0 0]]

image

I cannot display images directly. The image would show the same grid as represented by the numpy array above, but with the color mapping applied: white (0) for most cells, azure (8) for some, and blue (1) for those locations where the transformation changed the pixel values.

time
response time	total elapsed
9.236	56.444

usage
token type	call count	total used
Prompt	8898	34920
Response	1282	6433
Total	10180	41353
Cached	0	0