| gpt3.5 | |
| --- | |
| I'm doing Python experiments. | |
| These are images. | |
| ```python | |
| input = {} | |
| output = {} | |
| input[0] = {'width':3,'height':3,(0,0):3,(1,0):3,(2,0):3,(0,1):0,(1,1):2,(2,1):2,(0,2):1,(1,2):1,(2,2):0} | |
| output[0] = {'width':6,'height':6,(0,0):3,(1,0):3,(2,0):3,(3,0):3,(4,0):3,(5,0):3,(0,1):0,(1,1):2,(2,1):2,(3,1):2,(4,1):2,(5,1):0,(0,2):1,(1,2):1,(2,2):0,(3,2):0,(4,2):1,(5,2):1,(0,3):1,(1,3):1,(2,3):0,(3,3):0,(4,3):1,(5,3):1,(0,4):0,(1,4):2,(2,4):2,(3,4):2,(4,4):2,(5,4):0,(0,5):3,(1,5):3,(2,5):3,(3,5):3,(4,5):3,(5,5):3} | |
| input[1] = {'width':3,'height':3,(0,0):3,(1,0):3,(2,0):1,(0,1):1,(1,1):3,(2,1):0,(0,2):0,(1,2):2,(2,2):2} | |
| output[1] = {'width':6,'height':6,(0,0):3,(1,0):3,(2,0):1,(3,0):1,(4,0):3,(5,0):3,(0,1):1,(1,1):3,(2,1):0,(3,1):0,(4,1):3,(5,1):1,(0,2):0,(1,2):2,(2,2):2,(3,2):2,(4,2):2,(5,2):0,(0,3):0,(1,3):2,(2,3):2,(3,3):2,(4,3):2,(5,3):0,(0,4):1,(1,4):3,(2,4):0,(3,4):0,(4,4):3,(5,4):1,(0,5):3,(1,5):3,(2,5):1,(3,5):1,(4,5):3,(5,5):3} | |
| input[2] = {'width':3,'height':3,(0,0):2,(1,0):1,(2,0):0,(0,1):0,(1,1):2,(2,1):3,(0,2):0,(1,2):3,(2,2):0} | |
| output[2] = {'width':6,'height':6,(0,0):2,(1,0):1,(2,0):0,(3,0):0,(4,0):1,(5,0):2,(0,1):0,(1,1):2,(2,1):3,(3,1):3,(4,1):2,(5,1):0,(0,2):0,(1,2):3,(2,2):0,(3,2):0,(4,2):3,(5,2):0,(0,3):0,(1,3):3,(2,3):0,(3,3):0,(4,3):3,(5,3):0,(0,4):0,(1,4):2,(2,4):3,(3,4):3,(4,4):2,(5,4):0,(0,5):2,(1,5):1,(2,5):0,(3,5):0,(4,5):1,(5,5):2} | |
| ``` | |
| # Task A | |
| Use at most 100 words. | |
| Think step by step. | |
| - Write notes about what shapes and patterns you observe. | |
| - The output is never the same as the input. | |
| - Is the output a cropped out area from the input. | |
| - Is the output similar to the input rotated. | |
| - Is the output similar to the input flipped. | |
| - Is the output similar to the input diagonally flipped. | |
| # Task B | |
| Use at most 300 words. | |
| Include a markdown formatted table with the most important observations about input and output images. | |
| The table has three columns: observation name, observation values, comments about the observation. | |
| The `observation values` column can contain: integers, Yes, No, Absent, IDs, shape names, decreasing order, and so on. | |
| Think step by step. | |
| - Count the mass of each layer. | |
| - Count how many strongly connected clusters there are inside each layer. | |
| - Is mass related to the sorting of layers. | |
| - Isolated pixels without an adjacent pixel of same layer, sometimes they change layer. | |
| - Are there horizontal lines, do they extend edge to edge. | |
| - Are there vertical lines, do they extend edge to edge. | |
| - Are there stripes with evenly spaced lines. | |
| - Are there filled rectangles. | |
| - Are there hollow boxes. | |
| - Are there L-shapes. | |
| - Are there T-shapes. | |
| - Are there H-shapes. | |
| - Are there E-shapes. | |
| - Are there Plus-shapes. | |
| - Are there Tetris-shapes. | |
| - Are there other shapes. | |
| - Does the output contain a tile that is being used a few times. Is that tile identical to the input image. | |
| - What shapes are there with holes, such as boxes, where the hole is separated from the outside. | |
| - What shapes are there where the border has small holes, such as a box with 1 pixel missing in the border, so it's no longer a hole. | |
| - Is there a line connecting two landmarks, is it the shortest path. | |
| - Does shape change layer, but preserves their shape, and what may be triggering it. | |
| - Does shape move relative x,y. | |
| - Boolean operations may happen: xor, and, or. | |
| - Does a tiny object change layer, because it's nearest to a bigger object in that layer. | |
| # Task C | |
| Use at most 100 words. | |
| Think step by step. | |
| What are the actions that converts input to output. | |
| # Task D | |
| With the following example input, I want you to predict what the output should be. | |
| ```python | |
| input[3] = {'width':3,'height':3,(0,0):1,(1,0):1,(2,0):0,(0,1):0,(1,1):3,(2,1):2,(0,2):3,(1,2):3,(2,2):0} | |
| ``` | |
| Print your reasoning before printing the code. | |
| Don't print other markdown code blocks than the code block containing your predictions. | |
| Fill your predictions into the following template and replace PREDICT with your predictions. | |
| ```python | |
| output[3] = PREDICT | |
| ``` | |
| --- | |
| # Task A | |
| Observations: | |
| 1. The output is never the same as the input, indicating some transformation is applied. | |
| 2. The output is not simply a cropped-out area from the input. | |
| 3. The output appears to be similar to the input rotated, flipped, or diagonally flipped in some cases. | |
| # Task B | |
| | Observation Name | Observation Values | Comments | | |
| |--------------------------------------|----------------------------------------|-------------------------------------------------| | |
| | Count the mass of each layer | Positive integers | Identify the number of pixels in each layer. | | |
| | Count strongly connected clusters | Non-negative integers | Identify isolated clusters of pixels. | | |
| | Mass related to layer sorting | Yes, No | Determine if mass correlates with sorting. | | |
| | Isolated pixels change layer | Yes, No | Observe if isolated pixels change layers. | | |
| | Horizontal lines | Yes, No | Detect presence of horizontal lines. | | |
| | Vertical lines | Yes, No | Detect presence of vertical lines. | | |
| | Evenly spaced stripes | Yes, No | Identify evenly spaced stripes. | | |
| | Filled rectangles | Yes, No | Look for filled rectangle shapes. | | |
| | Hollow boxes | Yes, No | Identify shapes with holes like boxes. | | |
| | L-shapes | Yes, No | Look for L-shaped patterns. | | |
| | T-shapes | Yes, No | Identify T-shaped patterns. | | |
| | H-shapes | Yes, No | Identify H-shaped patterns. | | |
| | E-shapes | Yes, No | Identify E-shaped patterns. | | |
| | Plus-shapes | Yes, No | Look for plus-shaped patterns. | | |
| | Tetris-shapes | Yes, No | Identify Tetris-like shapes. | | |
| | Other shapes | Yes, No | Look for any other distinct shapes. | | |
| | Repeated identical tiles | Yes, No | Check for tiles that are repeated in the output.| | |
| | Shapes with holes | Yes, No | Identify shapes with separate holes. | | |
| | Shapes with border holes | Yes, No | Identify shapes with small border holes. | | |
| | Shortest path line | Yes, No | Detect lines connecting landmarks. | | |
| | Shape changes layer but preserves shape | Yes, No | Identify shapes that change layers while retaining their form. | | |
| | Relative x,y movement of shapes | Yes, No | Observe if shapes move relative to x, y. | | |
| | Boolean operations | Yes, No | Look for boolean operations (xor, and, or). | | |
| | Tiny object changes layer due to proximity | Yes, No | Detect if tiny objects change layers due to proximity to larger objects. | | |
| # Task C | |
| Actions to convert input to output: | |
| - Rotations: Some shapes appear to be rotated in the output, leading to a different arrangement of pixels. | |
| - Flips: Certain shapes seem to be flipped horizontally or vertically, changing their orientation. | |
| - Diagonal Flips: Diagonal flipping of shapes can also be observed in some cases, altering their appearance. | |
| - Mass Redistribution: The number of pixels in each layer seems to be redistributed in the output. | |
| # Task D | |
| Prediction: | |
| Based on the given input, the output should be: | |
| ```python | |
| output[3] = {'width': 6, 'height': 6, (0, 0): 1, (1, 0): 1, (2, 0): 0, (3, 0): 0, (4, 0): 1, (5, 0): 1, | |
| (0, 1): 0, (1, 1): 3, (2, 1): 2, (3, 1): 2, (4, 1): 3, (5, 1): 0, | |
| (0, 2): 3, (1, 2): 3, (2, 2): 0, (3, 2): 0, (4, 2): 3, (5, 2): 3, | |
| (0, 3): 3, (1, 3): 3, (2, 3): 0, (3, 3): 0, (4, 3): 3, (5, 3): 3, | |
| (0, 4): 0, (1, 4): 2, (2, 4): 2, (3, 4): 2, (4, 4): 2, (5, 4): 0, | |
| (0, 5): 1, (1, 5): 1, (2, 5): 0, (3, 5): 0, (4, 5): 1, (5, 5): 1} | |
| ``` | |
| Reasoning: | |
| The output seems to follow a pattern where the input is rotated by 90 degrees clockwise, and the number of pixels in each layer is redistributed to form a new arrangement. The shapes and their connectivity remain the same, but their positions change due to rotation. | |
| --- | |
| Almost correct. Got 2 pixels wrong. And it's GPT3.5. | |