002 • History

puzzle_id:: 1-3aa6fb7a
description:: example_2
timestamp:: 24.329.064813
call_count:: 2

Begin puzzle: 1-3aa6fb7a

example_1

input

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

output

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

images

observations

The input grid shows a cluster of azure (8) pixels. The output grid is identical except one azure pixel has changed to blue (1). The blue pixel is adjacent to, and below, a cluster of azure pixels.

code_execution:

import numpy as np

input_grid = np.array([[0, 0, 0, 0, 0, 0, 0],
                      [0, 8, 0, 0, 0, 0, 0],
                      [0, 8, 8, 0, 0, 0, 0],
                      [0, 0, 0, 0, 8, 8, 0],
                      [0, 0, 0, 0, 0, 8, 0],
                      [0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0]])

output_grid = np.array([[0, 0, 0, 0, 0, 0, 0],
                       [0, 8, 1, 0, 0, 0, 0],
                       [0, 8, 8, 0, 0, 0, 0],
                       [0, 0, 0, 0, 8, 8, 0],
                       [0, 0, 0, 0, 1, 8, 0],
                       [0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0]])

diff = np.where(input_grid != output_grid)
print(f"Differences found at coordinates: {diff}")
print(f"Input grid shape: {input_grid.shape}")
print(f"Number of azure pixels in input: {np.count_nonzero(input_grid == 8)}")
print(f"Number of blue pixels in output: {np.count_nonzero(output_grid == 1)}")

code_execution_result: 1

Differences found at coordinates: (array([1, 4]), array([2, 4]))
Input grid shape: (7, 7)
Number of azure pixels in input: 6
Number of blue pixels in output: 2

input:
  width: 7
  height: 7
  colors:
    - 0: 43
    - 8: 6
  objects:
    - size: 6, position: scattered, color: azure

output:
  width: 7
  height: 7
  colors:
    - 0: 43
    - 8: 4
    - 1: 2
  objects:
    - size: 4, position: scattered, color: azure
    - size: 2, position: scattered, color: blue

differences:
  cells_changed: 2
  colors_changed: Two azure pixels changed to blue.  The blue pixels are adjacent to the remaining cluster of azure pixels.
transformation:
  - A subset of azure pixels change to blue.  Specifically, azure pixels adjacent to other azure pixels are transformed.  The transformation might depend on the location and size of the cluster.

Based on the analysis, a possible natural language program for the transformation is: “Identify clusters of azure pixels. Change one azure pixel in each cluster to blue, selecting a pixel adjacent to other azure pixels within the cluster. If there’s only one azure pixel, leave it unchanged.” This needs further validation with more examples.

example_2

input

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

output

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

images

observations

INSTRUCTIONS:

Review Examples Phase

pairs of input and output grids will be shown to you one at a time

you will examine and analyze the text and image for each example

you may use code execution with tools like numpy to examine patterns after examining the grids, document the attributes of each as such

use a yaml block for the details

input:
width: X
height: Y
colors:
  - N: (count)
objects:
  - size, position and color - desc

output:
width: X
height: Y
colors:
  - N: (count)
objects:
  - size, position and color - desc

differences:
cells_changed: N
colors_changed: desc
transformation:
  - speculate on transformation rules

your response for this phase should contain the following content parts

begin with a verbal description of your perception of the input and output grid
run a code_execution part to test your perceptions - since the code you use may not be carried forward on following prompts, be sure to have the code print you findings in the output remember that you have access to many python libraries for analyzing the grids and validating patterns
review your findings and try to determine what the natural language program is for the transformation