006 • Response

puzzle_id:: 4-0d3d703e
description:: test input
timestamp:: 24.329.064813
call_count:: 6
model:: models/gemini-1.5-flash-002

Based on the previous examples, the transformation involves a unique color mapping for each input grid. There’s no consistent arithmetic operation or sequential pattern across the examples. Each input grid is transformed by a different mapping that replaces each color with a specific, new color. The mapping is not based on any discernible numerical relationship between the input and output color values. To predict the output for the test input, we need to identify a pattern or rule that connects the input and output grids in the training examples, and then apply that pattern or rule to the test input.

code_execution:

import numpy as np

test_input = np.array([[8, 1, 3],
                      [8, 1, 3],
                      [8, 1, 3]])

#Let's try to find a pattern in existing examples
examples = {
    "example1": {"input": np.array([[3, 1, 2], [3, 1, 2], [3, 1, 2]]), "output": np.array([[4, 5, 6], [4, 5, 6], [4, 5, 6]])},
    "example2": {"input": np.array([[2, 3, 8], [2, 3, 8], [2, 3, 8]]), "output": np.array([[6, 4, 9], [6, 4, 9], [6, 4, 9]])},
    "example3": {"input": np.array([[5, 8, 6], [5, 8, 6], [5, 8, 6]]), "output": np.array([[1, 9, 2], [1, 9, 2], [1, 9, 2]])},
    "example4": {"input": np.array([[9, 4, 2], [9, 4, 2], [9, 4, 2]]), "output": np.array([[8, 3, 6], [8, 3, 6], [8, 3, 6]])},
}

#There is no consistent mathematical pattern, only unique mappings.
#Let's create a dictionary to store mappings for each example.
mappings = {}
for example, data in examples.items():
    mappings[example] = {}
    for i in range(data['input'].shape[1]):
        mappings[example][data['input'][0][i]] = data['output'][0][i]

#Check mappings for test_input elements
test_mapping = {}
for example, mapping in mappings.items():
  for key in test_input.flatten():
    if key in mapping:
      test_mapping[key] = mapping[key]

# Apply the extracted mapping to the test input.
test_output = np.zeros_like(test_input)
for i in range(test_input.shape[0]):
    for j in range(test_input.shape[1]):
      test_output[i, j] = test_mapping.get(test_input[i, j], test_input[i, j]) # Handle unseen colors

print("Test Input:\n", test_input)
print("\nTest Output:\n", test_output)

code_execution_result:

Test Input:
 [[8 1 3]
 [8 1 3]
 [8 1 3]]

Test Output:
 [[9 5 4]
 [9 5 4]
 [9 5 4]]

input:
  width: 3
  height: 3
  colors:
    - 8: 3
    - 1: 3
    - 3: 3
  objects:
    - size: 3x1, position: (0,0), color: azure
    - size: 3x1, position: (0,1), color: blue
    - size: 3x1, position: (0,2), color: green

output:
  width: 3
  height: 3
  colors:
    - 9: 3
    - 5: 3
    - 4: 3
  objects:
    - size: 3x1, position: (0,0), color: maroon
    - size: 3x1, position: (0,1), color: gray
    - size: 3x1, position: (0,2), color: yellow

differences:
  cells_changed: 9
  colors_changed: All colors are changed. Azure becomes maroon, blue becomes gray, and green becomes yellow.

transformation:
  - A color mapping is applied based on the mappings observed in the training examples. There is no consistent mathematical or sequential rule across the examples. The transformation requires memorizing or learning individual mappings rather than inferring a general rule.

The code successfully predicts the output based on the color mappings extracted from the training examples. The transformation is purely a mapping; there’s no numerical pattern or generalizable rule. The system needs to learn or memorize each individual mapping.

time
response time	total elapsed
8.199	47.807

usage
token type	call count	total used
Prompt	8326	33103
Response	1163	5442
Total	9489	38545
Cached	0	0