011 • Response

puzzle_id:: 6-0520fde7
description:: review working
timestamp:: 24.329.064813
call_count:: 11
model:: models/gemini-1.5-flash-002

The changes to the working output grid are consistent with the rule derived from the examples. The rule states that the number of blue pixels in the central 3x3 region of the input grid influences the pattern of red pixels in the output grid. Specifically, a count of three blue pixels in the central 3x3 region (as seen in the test input) corresponds to an “L”-shaped arrangement of three red pixels in the output. The set_pixel function calls correctly place these red pixels, building up the “L” shape one pixel at a time.

Let’s use code to verify this:

code_execution:

import numpy as np

# Test input
test_input = np.array([[1, 0, 1, 5, 1, 0, 1],
                      [0, 1, 0, 5, 1, 0, 1],
                      [1, 0, 1, 5, 0, 1, 0]])

# Central 3x3 region
central_3x3 = test_input[0:3, 1:4]
blue_count = np.sum(central_3x3 == 1)
print(f"Number of blue pixels in central 3x3: {blue_count}")

# Predicted output (L-shape)
predicted_output = np.array([[0, 2, 0],
                            [0, 0, 2],
                            [0, 2, 0]])

# Check if the updated working output matches the prediction
working_output = np.array([[0, 2, 0],
                           [0, 0, 2],
                           [0, 2, 0]])

if np.array_equal(working_output, predicted_output):
    print("\nWorking output matches the predicted output for 3 blue pixels.")
else:
    print("\nWorking output does NOT match the predicted output.")

time
response time	total elapsed
4.317	53.584

usage
token type	call count	total used
Prompt	9786	76573
Response	436	5634
Total	10222	82207
Cached	0	0