semvec/pypi-packages huggingface dataset:
anubis 0.2.1
Astrophysical Nonparametrically Upgraded Bayesian Inference of Subpopulations
Requires Python: >=3.7

# ANUBIS - Astrophysical Nonparametrically Upgraded Bayesian Inference of Subpopulations

[ANUBIS](https://github.com/sterinaldi/ANUBIS) implements the inference of a multivariate probability density given a set of samples and an (incomplete) parametric model.
The Heterogeneous Mixture Model, or HMM, is a finite mixture of parametric models augmented with DPGMM to capture the eventual features missed by the parametric model.

To install this code, run `python setup.py install`. In some cases (like on clusters), it may happen that you do not have the permission to write in the default installation directory. In this case, run `python setup.py install --user`.

This code makes use of [FIGARO](https://github.com/sterinaldi/FIGARO). Be sure of having it installed and updated. **DO NOT USE** `pip install figaro`, since it installs an homonymous package.
MetaNN 0.3.2
MetaNN provides extensions of PyTorch nn.Module for meta learning
Requires Python: >=3.6

MetaNN for PyTorch Meta Learning
=====================================

.. image:: https://readthedocs.org/projects/metann/badge/?version=latest
    :target: https://metann.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

1. Introduction
____________________

In meta learner scenario, it is common use dependent variables as parameters, and back propagate the gradient of the parameters. However, parameters of PyTorch Module are designed to be leaf nodes and it is forbidden for parameters to have grad_fn. Meta learning coders are therefore forced to rewrite the basic layers to adapt the meta learning requirements.

This module provide an extension of torch.nn.Module, DependentModule that has dependent parameters, allowing the differentiable dependent parameters. It also provide the method to transform nn.Module into DependentModule, and turning all of the parameters of a nn.Module into dependent parameters.

2. Installation
__________________

.. code-block:: python

    pip install MetaNN

3. Example
___________

PyTorch suggest all parameters of a module to be independent variables. Using DependentModule arbitrary torch.nn.module can be transformed into dependent module.

.. code-block:: python

    from metann import DependentModule
    from torch import nn
    net = torch.nn.Sequential(
        nn.Linear(10, 100),
        nn.Linear(100, 5))
    net = DependentModule(net)
    print(net)

Higher-level api such as MAML class are more recommended to use.

.. code-block:: python

    from metann.meta import MAML, default_evaluator_classification as evaluator
    from torch import nn
    net = torch.nn.Sequential(
        nn.Linear(10, 100),
        nn.Linear(100, 5))
    )
    maml = MAML(net, steps_train=5, steps_eval=10, lr=0.01)
    output = maml(data_train)
    loss = evaluator(output, data_test)
    loss.backward()


4. Documents
_____________

The documents are available at ReadTheDocs.
`MetaNN <https://metann.readthedocs.io/>`__

5. License
__________

`MIT <http://opensource.org/licenses/MIT>`__

Copyright (c) 2019-present, Hanqiao Yu
streamlit-back-camera-input 0.1.1
Custom camera_input with rear camera as default, just click on video to capture!
Requires Python: >=3.10

# streamlit-back-camera-input

## Installation instructions

```sh
pip install streamlit-back-camera-input
```

## Usage instructions

```python
import streamlit as st

from streamlit_back_camera_input import back_camera_input

image = back_camera_input()

st.image(image)
```