005 • Response

puzzle_id:: 5-150deff5
description:: test input
timestamp:: 24.357.081407
call_count:: 5
model:: gemini-2.0-flash-thinking-exp-1219

The user wants me to apply the learned transformation rule to the test input grid. The rule is to outline the contiguous block of gray (5) pixels in azure (8) and fill the interior with red (2).

Plan:

Identify the contiguous block of gray pixels in the test input grid.
Create a copy of the input grid as the initial output grid.
Identify the outline pixels of the gray object.
Change the color of the outline pixels to azure (8) in the output grid.
Identify the interior pixels of the gray object.
Change the color of the interior pixels to red (2) in the output grid.
Keep the white (0) background pixels as they are.

import numpy as np
from scipy.ndimage import label

input_test = np.array([[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]])

output_test = np.copy(input_test)
rows, cols = input_test.shape

def get_neighbors(grid, row, col):
    neighbors = []
    rows, cols = grid.shape
    for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
        new_row, new_col = row + dr, col + dc
        if 0 <= new_row < rows and 0 <= new_col < cols:
            neighbors.append(grid[new_row, new_col])
    return neighbors

def check_outline(grid, row, col, object_color, background_color):
    neighbors = get_neighbors(grid, row, col)
    return any(neighbor == background_color for neighbor in neighbors)

def check_interior(grid, row, col, object_color):
    neighbors = get_neighbors(grid, row, col)
    return all(neighbor == object_color for neighbor in neighbors)

labeled_grid_test, num_labels_test = label(input_test == 5)

if num_labels_test == 1:
    object_pixels_test = np.argwhere(labeled_grid_test == 1)
    for row, col in object_pixels_test:
        if check_outline(input_test, row, col, 5, 0):
            output_test[row, col] = 8
        elif check_interior(input_test, row, col, 5):
            output_test[row, col] = 2

print(output_test)

time
response time	total elapsed
7.146	76.685

usage
token type	call count	total used
Prompt	13388	40085
Response	835	9946
Total	14223	50031
Cached	0	0