002 • History

puzzle_id:: 6-0520fde7
description:: example_2
timestamp:: 24.329.071439
call_count:: 2

Begin puzzle: 6-0520fde7

example_1

input

[[1 0 0 5 0 1 0]
 [0 1 0 5 1 1 1]
 [1 0 0 5 0 0 0]]

output

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

images

observations

The input grid appears to be a 3x7 rectangle with a central vertical stripe of 5s (gray) and various 1s (blue) and 0s (white) surrounding it. The output grid is a 3x3 square, which is much smaller, with a single 2 (red) in the center and 0s (white) everywhere else.

It seems the transformation involves significant size reduction and a change in the central element’s color and value, while the surrounding pixels are turned into 0s. The color change is from 5 (gray) to 2 (red), and the size change is from 3x7 to 3x3.

input:
  width: 7
  height: 3
  colors:
    - 0: 12
    - 1: 6
    - 5: 3
  objects:
    - central column of 5 (gray)
    - scattered 1 (blue)
    - background of 0 (white)

output:
  width: 3
  height: 3
  colors:
    - 0: 8
    - 2: 1
  objects:
    - single central 2 (red)
    - background of 0 (white)

differences:
  cells_changed: 20
  colors_changed: gray (5) to red (2), blue (1) to white (0)
transformation:
  - Reduce size from 3x7 to 3x3.
  - Change the central element's color from gray (5) to red (2).
  - Change all other elements to white (0).

Code Execution

code_execution:

import numpy as np

# Input grid
input_grid = np.array([
    [1, 0, 0, 5, 0, 1, 0],
    [0, 1, 0, 5, 1, 1, 1],
    [1, 0, 0, 5, 0, 0, 0]
])

# Output grid
output_grid = np.array([
    [0, 0, 0],
    [0, 2, 0],
    [0, 0, 0]
])

# Get input grid properties
input_height, input_width = input_grid.shape
input_colors, input_color_counts = np.unique(input_grid, return_counts=True)
input_color_map = dict(zip(input_colors, input_color_counts))

# Get output grid properties
output_height, output_width = output_grid.shape
output_colors, output_color_counts = np.unique(output_grid, return_counts=True)
output_color_map = dict(zip(output_colors, output_color_counts))

print("Input Grid Properties:")
print("Width:", input_width)
print("Height:", input_height)
print("Color Map:", input_color_map)
print("\nOutput Grid Properties:")
print("Width:", output_width)
print("Height:", output_height)
print("Color Map:", output_color_map)

print("\nChanges:")
print("Size change:", f"{input_height}x{input_width} to {output_height}x{output_width}")
print("Color changes:")
for color in input_colors:
    if color not in output_colors:
        print(f"  {color} removed")
for color in output_colors:
    if color not in input_colors:
        print(f"  {color} added")

code_execution_result: 1

Input Grid Properties:
Width: 7
Height: 3
Color Map: {np.int64(0): np.int64(11), np.int64(1): np.int64(7), np.int64(5): np.int64(3)}

Output Grid Properties:
Width: 3
Height: 3
Color Map: {np.int64(0): np.int64(8), np.int64(2): np.int64(1)}

Changes:
Size change: 3x7 to 3x3
Color changes:
  1 removed
  5 removed
  2 added

Analysis of Code Execution Results

The code execution confirms the observations about the size and color changes. The input grid has dimensions 3x7, while the output grid has dimensions 3x3. The colors in the input grid are 0 (white), 1 (blue), and 5 (gray), while the colors in the output grid are 0 (white) and 2 (red).

The changes are:

Size reduction from 3x7 to 3x3.
Color 1 (blue) is removed.
Color 5 (gray) is removed.
Color 2 (red) is added.

Natural Language Program

Transform the input grid by reducing its size to a 3x3 grid. Change the central column color, which is 5 (gray), to 2 (red) and place it in the center of the new grid. Fill all other cells of the new 3x3 grid with 0 (white).

example_2

input

[[1 1 0 5 0 1 0]
 [0 0 1 5 1 1 1]
 [1 1 0 5 0 1 0]]

output

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