semvec/pypi-packages huggingface dataset:
ducktype-mypy-plugin 0.2.dev1
Duck-typing plugin for mypy.
License: MIT
Requires Python: >=3.9
Keywords: mypy, typing

# pyvista-mypy-plugin
Mypy plugin for the PyVista project.

This plugin is primarily used for type-promotion between related classes that do not
inherit from each other. For example, the abstract class `pyvista.DataSet` does not
inherit from `vtk.vtkDataSet`, and will generate type errors wherever a `vtk.vtkDataSet`
is expected. Without this plugin, the following is a type error:

``` python
from pyvista import DataSet
from vtkmodules.vtkCommonDataModel import vtkDataSet

x: vtkDataSet
x = DataSet()  # error: Incompatible types in assignment (expression has type "DataSet", variable has type "vtkDataSet")'
```

(Note that the example above is not valid at runtime since `DataSet` is an abstract
class and cannot be instantiated.)

With this plugin, `DataSet` is promoted as type `vtkDataSet`, allowing `DataSet`
type to be used wherever `vtkDataSet` is used.

## Installation

Dependencies:
- [mypy](https://github.com/python/mypy)
- [pyvista](https://github.com/pyvista/pyvista)
- [vtk](https://pypi.org/project/vtk/)

Install it with:

``` bash
python -m pip install pyvista-mypy-plugin
```

Alternatively, add `pyvista-mypy-plugin` as a project dependency wherever `mypy` is
used, e.g. as an optional dev requirement in `pyproject.toml`:

``` toml
[project.optional-dependencies]
dev = ["mypy", "pyvista-mypy-plugin"]
```

## Usage

To enable the plugin, it must be added to your project's mypy configuration file.
E.g. add the following to `pyproject.toml`:

``` toml
[tool.mypy]
plugins = [
  'pyvista_mypy_plugin',
]
```

## Testing

First, install `pyvista-mypy-plugin` with dev requirements:
``` bash
python -m pip install pyvista-mypy-plugin[dev]
```

To run the tests, execute:
``` bash
pytest
```
NeuralProcesses 0.2.7
A framework for composing Neural Processes in Python
License: MIT
Requires Python: >=3.8

# [Neural Processes](http://github.com/wesselb/neuralprocesses)

[![CI](https://github.com/wesselb/neuralprocesses/workflows/CI/badge.svg)](https://github.com/wesselb/neuralprocesses/actions?query=workflow%3ACI)
[![Coverage Status](https://coveralls.io/repos/github/wesselb/neuralprocesses/badge.svg?branch=main)](https://coveralls.io/github/wesselb/neuralprocesses?branch=master)
[![Latest Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://wesselb.github.io/neuralprocesses)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A framework for composing Neural Processes in Python.

## Installation

```
pip install neuralprocesses tensorflow tensorflow-probability  # For use with TensorFlow
pip install neuralprocesses torch                              # For use with PyTorch
```

If something is not working or unclear, please feel free to open an issue.

## Documentation

See [here](https://wesselb.github.io/neuralprocesses).

## TL;DR! Just Get me Started!

Here you go:

```python
import torch

import neuralprocesses.torch as nps

# Construct a ConvCNP.
convcnp = nps.construct_convgnp(dim_x=1, dim_y=2, likelihood="het")

# Construct optimiser.
opt = torch.optim.Adam(convcnp.parameters(), 1e-3)

# Training: optimise the model for 32 batches.
for _ in range(32):
    # Sample a batch of new context and target sets. Replace this with your data. The
    # shapes are `(batch_size, dimensionality, num_data)`.
    xc = torch.randn(16, 1, 10)  # Context inputs
    yc = torch.randn(16, 2, 10)  # Context outputs
    xt = torch.randn(16, 1, 15)  # Target inputs
    yt = torch.randn(16, 2, 15)  # Target output

    # Compute the loss and update the model parameters.
    loss = -torch.mean(nps.loglik(convcnp, xc, yc, xt, yt, normalise=True))
    opt.zero_grad(set_to_none=True)
    loss.backward()
    opt.step()

# Testing: make some predictions.
mean, var, noiseless_samples, noisy_samples = nps.predict(
    convcnp,
    torch.randn(16, 1, 10),  # Context inputs
    torch.randn(16, 2, 10),  # Context outputs
    torch.randn(16, 1, 15),  # Target inputs
)
```
wbml 0.4.2
Machine learning stuff
License: MIT
Requires Python: >=3.6

# [WBML](https://github.com/wesselb/wbml)

[![CI](https://github.com/wesselb/wbml/workflows/CI/badge.svg?branch=master)](https://github.com/wesselb/wbml/actions?query=workflow%3ACI)
[![Coverage Status](https://coveralls.io/repos/github/wesselb/wbml/badge.svg?branch=master&service=github)](https://coveralls.io/github/wesselb/wbml?branch=master)
[![Latest Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://wesselb.github.io/wbml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Machine learning stuff

## Requirements and Installation

See [the instructions here](https://gist.github.com/wesselb/4b44bf87f3789425f96e26c4308d0adc).
Then simply

```
pip install wbml
```