005 • History

puzzle_id:: 4-0d3d703e
description:: example_summary
timestamp:: 24.329.071439
call_count:: 5

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

This example follows the same pattern as the previous two, with each column containing a single color. The output grid also has three columns with each column a different single color.

input:
  width: 3
  height: 3
  colors:
    - 5: 3
    - 8: 3
    - 6: 3
  objects:
    - three columns of color
output:
  width: 3
  height: 3
  colors:
    - 1: 3
    - 9: 3
    - 2: 3
  objects:
    - three columns of color
differences:
  cells_changed: 9
  colors_changed: all colors changed to a new color
transformation:
  color value mapping 5->1, 8->9, 6->2

code_execution:

import numpy as np
input_grid = np.array([[5, 8, 6], [5, 8, 6], [5, 8, 6]])
output_grid = np.array([[1, 9, 2], [1, 9, 2], [1, 9, 2]])

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(5): np.int64(1), np.int64(8): np.int64(9), np.int64(6): np.int64(2)}

The code confirms the color mapping speculation: 5->1, 8->9, 6->2.

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 5->1, 8->9, 6->2.

example_4

input

[[9 4 2]
 [9 4 2]
 [9 4 2]]

output

[[8 3 6]
 [8 3 6]
 [8 3 6]]

images

observations

This example follows the same pattern as the previous three, with each column containing a single color. The output grid also has three columns with each column a different single color.

input:
  width: 3
  height: 3
  colors:
    - 9: 3
    - 4: 3
    - 2: 3
  objects:
    - three columns of color
output:
  width: 3
  height: 3
  colors:
    - 8: 3
    - 3: 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 9->8, 4->3, 2->6

code_execution:

import numpy as np
input_grid = np.array([[9, 4, 2], [9, 4, 2], [9, 4, 2]])
output_grid = np.array([[8, 3, 6], [8, 3, 6], [8, 3, 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(9): np.int64(8), np.int64(4): np.int64(3), np.int64(2): np.int64(6)}

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

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 9->8, 4->3, 2->6.

examples summary

INSTRUCTIONS:

summarize your observations to explain the transformation of the input to output
use code_execution to investigate properties, patterns and differences in the grids