003 • History

puzzle_id:: 4-0d3d703e
description:: example_3
timestamp:: 24.329.071439
call_count:: 3

Begin puzzle: 4-0d3d703e

example_1

input

[[3 1 2]
 [3 1 2]
 [3 1 2]]

output

[[4 5 6]
 [4 5 6]
 [4 5 6]]

images

observations

This is a very straightforward transformation. The input grid has three columns with each column containing a different color. The output grid is the same size and the cell colors are changed. There appears to be a one-to-one mapping between input colors and output colors, 3->4, 1->5, 2->6.

input:
  width: 3
  height: 3
  colors:
    - 3: 3
    - 1: 3
    - 2: 3
  objects:
    - three columns of color
output:
  width: 3
  height: 3
  colors:
    - 4: 3
    - 5: 3
    - 6: 3
  objects:
    - three columns of color
differences:
  cells_changed: 9
  colors_changed: all colors changed to a new color
transformation:
  color value mapping 3->4, 1->5, 2->6

code_execution:

import numpy as np
input_grid = np.array([[3, 1, 2], [3, 1, 2], [3, 1, 2]])
output_grid = np.array([[4, 5, 6], [4, 5, 6], [4, 5, 6]])

def find_color_mapping(in_grid, out_grid):
    color_map = {}
    for i in range(in_grid.shape[0]):
        for j in range(in_grid.shape[1]):
            color_map[in_grid[i, j]] = out_grid[i, j]
    return color_map

color_map = find_color_mapping(input_grid, output_grid)
print(color_map)

code_execution_result: 1

{np.int64(3): np.int64(4), np.int64(1): np.int64(5), np.int64(2): np.int64(6)}

The code confirms the color mapping speculation.

The natural language program for this transformation is to map the color values in the input grid to the specified color values in the output grid according to the mapping 3->4, 1->5, 2->6.

example_2

input

[[2 3 8]
 [2 3 8]
 [2 3 8]]

output

[[6 4 9]
 [6 4 9]
 [6 4 9]]

images

observations

This example is very similar to example 1, with each column containing a different color. The output grid has the same dimensions as the input and also shows columns of a single color.

input:
  width: 3
  height: 3
  colors:
    - 2: 3
    - 3: 3
    - 8: 3
  objects:
    - three columns of color
output:
  width: 3
  height: 3
  colors:
    - 6: 3
    - 4: 3
    - 9: 3
  objects:
    - three columns of color
differences:
  cells_changed: 9
  colors_changed: all colors changed to a new color
transformation:
  color value mapping 2->6, 3->4, 8->9

code_execution:

import numpy as np
input_grid = np.array([[2, 3, 8], [2, 3, 8], [2, 3, 8]])
output_grid = np.array([[6, 4, 9], [6, 4, 9], [6, 4, 9]])

def find_color_mapping(in_grid, out_grid):
    color_map = {}
    for i in range(in_grid.shape[0]):
        for j in range(in_grid.shape[1]):
            color_map[in_grid[i, j]] = out_grid[i, j]
    return color_map

color_map = find_color_mapping(input_grid, output_grid)
print(color_map)

code_execution_result: 1

{np.int64(2): np.int64(6), np.int64(3): np.int64(4), np.int64(8): np.int64(9)}

The code confirms the color mapping speculation: 2->6, 3->4, 8->9.

The natural language program for this transformation is to map the color values in the input grid to the specified color values in the output grid according to the mapping 2->6, 3->4, 8->9.

example_3

input

[[5 8 6]
 [5 8 6]
 [5 8 6]]

output

[[1 9 2]
 [1 9 2]
 [1 9 2]]

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