semvec/pypi-packages huggingface dataset:
sabi-hello-world 0.2.0
test
License: MIT

sabi-hello-world
================

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test

Usage
-----

Installation
------------

Requirements
^^^^^^^^^^^^

Compatibility
-------------

Licence
-------

Authors
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`sabi-hello-world` was written by `sabih hasan <sabih.phi@gmail.com>`_.
frozenSpider 0.0.6
Library to implement and visualise ML Algorithms
License: MIT
Keywords: ml algorithms

This is a library that does all the heavy lifting for the basic ml algorithms such as regression, knn, svm, etc. But, currently this has only linear regression. The speciality about this library is you can plot the outcomes on graph in a single line using class included in the library. Which is fed with default parameters. Which you can change according to your requirements.It uses the following libraries 1) Numpy 2) Matplotlib 3) Random

Change Log 0.0.1 (10/01/2021 )- First Release
nads 0.0.4
Neural Anisotropy Directions Toolbox
License: MIT
Keywords: artificial intelligence,inductive bias,deep learning

# Neural Anisotropy Directions

This package is a rearrangement of some bits and pieces adapted from
the [original repository](https://github.com/LTS4/neural-anisotropy-directions)
by [@gortizji](https://github.com/gortizji) et al., plus some additional object-oriented implementation to make the code
as self-contained and easy to follow as possible.  
You can find the paper "Neural Anisotropy Directions" paper [here](https://arxiv.org/abs/2006.09717).

## Installation

This package relies on `torch>=1.9`, but everything other than the Fourier based operations should work just fine for
lower versions of python.

To install the package, enter the following command in your command-line interface:

```shell
pip install nads
```

## Usage

Assuming that you have a model class like `Model` and some initialization parameters
like `arg1=value1, arg2=value2, ...`
to compute the NADs for this architecture using the _Gradient Covariance_ method described in the paper, you can do as
follows:

```python
...
from nads.compute import GradientCovariance

compute = GradientCovariance(
    eval_point=torch.rand(...),  # some arbitrary input point to feed to the network
    model_cls=Model,  # your Model class
    model_params=dict(arg1=value1, arg2=value2, ...),  # initialization parameters for Model architecture
    device='cpu',  # which hardware do you want the computations to take place on
    force_eval=True,  # whether to force the model to eval state by doing model.eval() after each model initialization
)
nads = compute.nads(
    num_samples=2048, # number of MCMC samples to make for nads calculation
)
...
```

The resulting object has a bunch of useful properties such as saving (`.save(path)`), visualization of eigenvalues'
 spectrum (`.visualize_spectrum()`) and nads themselves (`.visualize_nads()`). You can slice it just like any tensor, 
and it will give you the sliced and accordingly. By calling the `.to(device)` method, you can move its tensors to your  
hardware of choice. You can also use the `.load(path)` to load up a previously saved NADs object.
For more information regarding each method, consult their docstrings.

The `data` module also contains a bunch of helpful data utils, such as the `DirectionalLinearDataset` class, which creates a
linearly separable dataset just as described in the paper and the `create_rfft2_direction` function that can be used to
create the desired canonical direction in the rfft2 vector space. 

## Todo
- [ ] Add arbitrary dataset poisoning functionality
- [ ] Add qualitative metrics for nads like using KLD or similar methods for measuring how uniformly distributed the  
eigenvalue spectrum is
- [ ] Add NADs computation for a grid of model parameters functionality
- [ ] Add grid-search functionality to reach the most uniformly distributed model architecture described by a set of
model parameters and a model class