semvec/pypi-packages huggingface dataset:
tinywin 0.2.5
A tiny Curses window management library.

# tinywin
<p align="center">
  <img src="https://raw.githubusercontent.com/knicholson32/tinywin/master/images/tinywin.png" width="400">
  <h4 align="center">A tiny Curses window library</h4>
</p>

[![PyPI](https://img.shields.io/pypi/v/tinywin)](https://pypi.org/project/tinywin/) [![PyPI - Format](https://img.shields.io/pypi/format/tinywin)](https://pypi.org/project/tinywin/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tinywin)](https://pypi.org/project/tinywin/) [![GitHub](https://img.shields.io/github/license/knicholson32/tinywin)](https://github.com/knicholson32/tinywin/blob/master/LICENSE)
deepforecast 0.0.4
An easy-to-use deep model for time series forecast
Requires Python: >=3.6

# **DeepForecast**

An Easy-to-use Deep Model for Time Series Forecast

## Methods

- STTF
- Seq2Seq

## How to install

```pip install deepforecast```

## How to use

Use STTF as an exapmle:
```python
from tensorflow.keras.utils import plot_model
from deepforecast.features import SparseColumn, SequenceColumn
from deepforecast.models import STTF

attr_feats = ["age", "user", "platform"]
sequence_feats = ["history", "future"]

attr_columns = []
for feat in attr_feats:
    col = SparseColumn(name=feat, vocab_size=10, embed_dim=8)
    attr_columns.append(col)

sequence_columns = []
hist_col = SequenceColumn(name="history", num_seq=5, seq_steps=28, dim=1)
sequence_columns.append(hist_col)
fut_col = SequenceColumn(name="future", num_seq=4, seq_steps=7, dim=1)
sequence_columns.append(fut_col)

model = STTF(attr_columns, sequence_columns, attr_attention_embed_dim=12)
model.summary()
plot_model(model, show_shapes=True)
model.compile(optimizer="rmsprop",
              loss=["mse", "mse"],
              loss_weights=[0.2, 0.8],
              metrics=["mse"])
```