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dataset_info:
features:
- name: code
dtype: string
- name: output
list: int64
- name: n_cells
dtype: int64
splits:
- name: train
num_bytes: 15602481
num_examples: 32768
download_size: 14512543
dataset_size: 15602481
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
### dataset creation
```python
import random
from datasets import Dataset
from tqdm.auto import trange
bf = Brainfuck(n_cells = 1024, max_steps = 32768)
SIMPLE = "><+-."
def bf_program(p_continue=0.5, p_loop=0.3, max_depth=6, rng=random):
# glm5.3-flash generated function sorry for my lazy
def sequence(depth):
parts = []
while rng.random() < p_continue:
if depth > 0 and rng.random() < p_loop:
parts += ["[", *sequence(depth - 1), "]"]
else:
parts.append(rng.choice(SIMPLE))
return parts
return "".join(sequence(max_depth))
data = []
for _ in trange(32_000, miniters=100):
valid = False
output = []
while valid is False or (len(output) < 3 or len(code) > 256 or len(code)<8 or False not in [i==0 for i in output]):
code = bf_program(p_continue=0.8)
output, valid = bf.run(code)
data.append({
"code": code,
"output": output,
"n_cells": 1024
})
dataset = Dataset.from_list(data)
dataset.push_to_hub("crumb/bf_random_valid")
```
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