005 • Response

puzzle_id:: 5-150deff5
description:: test input
timestamp:: 24.329.064813
call_count:: 5
model:: models/gemini-1.5-flash-002

Here’s a report analyzing the provided test input grid and applying the insights gained from the previous examples.

Review Examples Phase (Recap and Refinement):

The previous examples showed a consistent pattern: a gray (5) blob is transformed into a similar shape using light blue (8) and red (2) pixels. The rule for replacement appeared to depend on the number of gray (5) neighbors for each pixel. However, the simple adjacency check in the previous code was insufficient. A more sophisticated approach is required.

Code Execution (Neighborhood Analysis Refinement):

This refined code attempts to better capture the relationship between the number of gray neighbors and the color assignment in the output grid.

code_execution:

import numpy as np

def transform_grid_refined(grid):
    new_grid = np.copy(grid)
    gray_indices = np.where(grid == 5)
    for i, j in zip(*gray_indices):
        count = 0
        for x, y in [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]:
            if 0 <= x < grid.shape[0] and 0 <= y < grid.shape[1] and grid[x, y] == 5:
                count += 1
        if count == 0 or count ==1:  #Edge cases and isolated pixels
            new_grid[i, j] = 2
        elif count == 2: #Consider further refinements based on additional examples
            new_grid[i, j] = 2
        elif count == 3 :
            new_grid[i, j] = 8
        else:
            new_grid[i, j] = 8 #Interior pixels

    return new_grid

# Test input grid
test_input = 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]])

test_output = transform_grid_refined(test_input)
print(test_output)

code_execution_result: 1

[[0 0 2 2 0 2 8 2 0 0 0]
 [0 0 8 8 0 0 2 0 0 0 0]
 [0 2 8 8 8 8 8 0 0 0 0]
 [0 0 0 0 8 8 2 0 0 0 0]
 [0 0 0 2 8 8 0 0 0 0 0]
 [0 0 0 0 0 8 2 0 0 0 0]
 [0 0 0 0 0 2 2 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0]]

The refined code produces a more accurate output compared to the simple adjacency check. Note that even this refined solution might not be perfect without additional examples or a more detailed specification of edge handling. The current solution uses a heuristic to handle edge cases based on the limited example set.

Final Report:

The transformation replaces gray (5) shapes with similar shapes using light blue (8) and red (2). The rule seems to be based on a refined neighborhood analysis, considering the number of adjacent gray pixels. Pixels with 0 or 1 gray neighbors become red. Pixels with 2 gray neighbors become red (this may need further refinement based on additional data). Pixels with 3 or more gray neighbors become light blue. Edge cases require careful consideration, and a more formal rule might be needed for perfect accuracy. The presented transform_grid_refined function provides a good approximation but may require further refinement depending on unseen edge cases.

time
response time	total elapsed
11.202	94.524

usage
token type	call count	total used
Prompt	13287	37911
Response	1098	9743
Total	14385	47654
Cached	0	0