semvec/pypi-packages huggingface dataset:
paddle-spline-conv 0.0.2
SplineConv module for paddle.
Requires Python: ~=3.6

# SplineConv implementation for Paddle
This module implements the `SplineConv` operators from

> Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich M眉ller: SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels (CVPR 2018).

It is in early development, and may have problems. Feel free to open an issue if you find one.

## Requirements
It only needs `paddle`. It is tested on `paddle >= 2.1.0, <= 2.2.0rc1`, but should work for any recent paddle versions.

For development -- since we run tests against `torch-spline-conv`, you will need that.

## Installation
`pip install paddle-spline-conv`

## Usage
Here are some basic usage descriptions. See docstring in code for more detailed descriptions, types and shapes of parameters.

Currently only degree-1 splines are supported. But the basic operators have been ready, and adding more shouldn't be very hard. You are welcome to contribute for higher degree splines!

```python
import paddle_spline_conv

# Stacked SplineConv layers implemented in SConv
g = paddle_spline_conv.nn.GraphData(x, edge_index, edge_attr)
# Input n_features and output n_features
sconv = paddle_spline_conv.nn.SConv(10, 40)
sconv(g)

# Standalone SplineConv layer
paddle_spline_conv.nn.SplineConv(
    in_channels: int,
    out_channels: int,
    dim: int,
    kernel_size: int,
    is_open_spline: bool = True,
    degree: int = 1,
    aggr: str = 'mean',
    root_weight: bool = True,
    bias: bool = True
)

# Standalone SplineConv functional API
paddle_spline_conv.functional.spline_conv(
    x: paddle.Tensor, edge_index: paddle.Tensor,
    pseudo: paddle.Tensor, weight: paddle.Tensor,
    kernel_size: paddle.Tensor, is_open_spline: paddle.Tensor,
    degree: int = 1, aggr: str = 'mean',
    root_weight: Optional[paddle.Tensor] = None,
    bias: Optional[paddle.Tensor] = None
)

# SplineConv-specific operators
paddle_spline_conv.ops.spline_basis
paddle_spline_conv.ops.spline_weighting
paddle_spline_conv.ops.basis_kernel_1d
```
fcmpy 0.0.21
Fuzzy Cognitive Maps for Behavior Change Interventions and Evaluation
Requires Python: >=3.8.1

# FCMpy: A package for Constructing and Analysing Fuzzy Cognitive Maps in Python.
<div align = justify>

The fcmpy is Python package for automatically generating causal weights for fuzzy cognitive maps based on qualitative inputs (by using fuzzy logic), optimizing the FCM connection matrix via Machine Learning Algorithms and testing <em>what-if</em> scenarios. The package includes the following submodules:

* ExpertFcm
* Simulation
* Intervention
* ML

## Installation
FCMpy requires python >=3.8.1 and depends on:

* pandas>=1.0.3
* numpy>=numpy==1.18.2
* scikit-fuzzy>=0.4.2
* tqdm>=4.50.2
* openpyxl

The latest version can be installed by:

```
pip install fcmpy
```

Alternatively, you can install it from source or develop this package, you can fork and clone this repository then install FCMpy by running:

```
py -m pip install --user --upgrade setuptools wheel
py setup.py sdist bdist_wheel
py -m pip install install e . 
```

You can run the unittest for the package as follows:

```
py -m unittest discover unittests
```

## Documentation and tutorials
<a href="https://maxiuw.github.io/fcmpyhtml"> Tutorial and documentation </a> Learn how to use our library by following available tutorials and documentation!<br>

## License

Please read LICENSE.txt in this directory.

</div>