semvec/pypi-packages huggingface dataset:
dooc 0.0.2
Digtal Organoid On Chips
Requires Python: >=3.8
Keywords: molcule, organoid, AI, deep learning, transformer, drug response, mutations

# DOoC

## Usage


### Train


```python
# Regression train
from moltx import tokenizers
from dooc import models, datasets, nets


tk = tokenizers.MoltxTokenizer.from_pretrain(models.AdaMRTokenizerConfig.Prediction)
ds = datasets.MutSmiXAttention(tokenizer=tk, device=torch.device('cpu'))
smiles = ["c1cccc1c", "CC[N+](C)(C)Cc1ccccc1Br"]
mutations = [[1, 0, 0, ...], [1, 0, 1, ...]]
# e.g.
# import random
# [random.choice([0, 1]) for _ in range(3008)]
values = [0.85, 0.78]
smiles_src, smiles_tgt, mutations_src, out = ds(smiles, mutations, values)

model = models.MutSmiXAttention()
model.load_pretrained_ckpt('/path/to/drugcell.ckpt', '/path/to/moltx.ckpt')

crt = nn.MSELoss()

optim.zero_grad()
pred = model(smiles_src, smiles_tgt, mutations_src)
loss = crt(pred, out)
loss.backward()
optim.step()

torch.save(model.state_dict(), '/path/to/mutsmixattention.ckpt')
```

### Inference

```python
from dooc import pipelines, models
# dooc
model = models.MutSmiXAttention()
model.load_ckpt('/path/to/mutsmixattention.ckpt')
pipeline = pipelines.MutSmiXAttention()
pipeline([1, 0, 0, ...], "C=CC=CC=C")
# 0.85


```
ZexusDigital 0.1
Instagram Scraping
Requires Python: >=3.6

# Instagram Scraper

This project contains a simple Python application that scrapes data from Instagram. This application retrieves posts of a specific user and displays basic information about those posts.

## Installation

To run the project, you need to have Python installed. Then, you can follow these steps to run the project:

1. Clone the repository:
```bash
git clone https://github.com/ZexusDigital/instagram-scraper.git
```

3. Navigate to the project directory:
```bash
cd instagram-scraper
```

5. Install the required dependencies:
```bash
pip install -r requirements.txt
```

6. Run the main script:
```bash
python main.py
```
torchxlstm 0.0.3
A pure pytorch implementation of xLSTM.
Requires Python: >=3
Keywords: xlstm, lstm, torch, pytorch

# xLSTM

A pure pytorch implementation of the [XLSTM paper](https://arxiv.org/abs/2405.04517).

## TODO
- Ensure correct with paper
- Create some usage examples
- Implement paralleization
- CUDA?
- Allow for different initializations according to: https://pytorch.org/docs/stable/nn.init.html
- Allow for flattening of x for greater shape conformity
- Allow for batching
- Add tests
    - https://github.com/catid/audio_prediction/tree/master
    - other classic RNN/LSTM tasks

## References
- https://arxiv.org/abs/2405.04517
- https://discuss.pytorch.org/t/causal-convolution/3456/3
- https://pytorch.org/docs/stable/generated/torch.nn.RNN.html
- https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html