graphrag-ollama/lib/python3.12/site-packages/autograd-1.7.0.dist-info/METADATA

Metadata-Version: 2.3
Name: autograd
Version: 1.7.0
Summary: Efficiently computes derivatives of NumPy code.
Project-URL: Source, https://github.com/HIPS/autograd
Author-email: Dougal Maclaurin <[email protected]>, David Duvenaud <[email protected]>, Matthew Johnson <[email protected]>, Jamie Townsend <[email protected]>
Maintainer-email: Jamie Townsend <[email protected]>, Fabian Joswig <[email protected]>, Agriya Khetarpal <[email protected]>
License: The MIT License (MIT)
        
        Copyright (c) 2014 by the President and Fellows of Harvard University
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Keywords: Automatic differentiation,NumPy,Python,SciPy,backpropagation,gradients,machine learning,neural networks,optimization
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Requires-Dist: numpy
Provides-Extra: scipy
Requires-Dist: scipy; extra == 'scipy'
Description-Content-Type: text/markdown

# Autograd  [![Checks status][checks-badge]][checks-url] [![Tests status][tests-badge]][tests-url] [![Publish status][publish-badge]][publish-url] [![asv][asv-badge]](#)

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[checks-badge]: https://github.com/HIPS/autograd/actions/workflows/check.yml/badge.svg
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[publish-url]: https://github.com/HIPS/autograd/actions/workflows/publish.yml
[checks-url]: https://github.com/HIPS/autograd/actions/workflows/check.yml
[tests-url]: https://github.com/HIPS/autograd/actions/workflows/test.yml

Autograd can automatically differentiate native Python and Numpy code. It can
handle a large subset of Python's features, including loops, ifs, recursion and
closures, and it can even take derivatives of derivatives of derivatives. It
supports reverse-mode differentiation (a.k.a. backpropagation), which means it
can efficiently take gradients of scalar-valued functions with respect to
array-valued arguments, as well as forward-mode differentiation, and the two can
be composed arbitrarily. The main intended application of Autograd is
gradient-based optimization. For more information, check out the
[tutorial](docs/tutorial.md) and the [examples directory](examples/).

Example use:

```python
>>> import autograd.numpy as np  # Thinly-wrapped numpy
>>> from autograd import grad    # The only autograd function you may ever need
>>>
>>> def tanh(x):                 # Define a function
...     y = np.exp(-2.0 * x)
...     return (1.0 - y) / (1.0 + y)
...
>>> grad_tanh = grad(tanh)       # Obtain its gradient function
>>> grad_tanh(1.0)               # Evaluate the gradient at x = 1.0
0.41997434161402603
>>> (tanh(1.0001) - tanh(0.9999)) / 0.0002  # Compare to finite differences
0.41997434264973155
```

We can continue to differentiate as many times as we like, and use numpy's
vectorization of scalar-valued functions across many different input values:

```python
>>> from autograd import elementwise_grad as egrad  # for functions that vectorize over inputs
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-7, 7, 200)
>>> plt.plot(x, tanh(x),
...          x, egrad(tanh)(x),                                     # first  derivative
...          x, egrad(egrad(tanh))(x),                              # second derivative
...          x, egrad(egrad(egrad(tanh)))(x),                       # third  derivative
...          x, egrad(egrad(egrad(egrad(tanh))))(x),                # fourth derivative
...          x, egrad(egrad(egrad(egrad(egrad(tanh)))))(x),         # fifth  derivative
...          x, egrad(egrad(egrad(egrad(egrad(egrad(tanh))))))(x))  # sixth  derivative
>>> plt.show()
```

<img src="examples/tanh.png" width="600">

See the [tanh example file](examples/tanh.py) for the code.

## Documentation

You can find a tutorial [here.](docs/tutorial.md)

## End-to-end examples

* [Simple neural net](examples/neural_net.py)
* [Convolutional neural net](examples/convnet.py)
* [Recurrent neural net](examples/rnn.py)
* [LSTM](examples/lstm.py)
* [Neural Turing Machine](https://github.com/DoctorTeeth/diffmem/blob/512aadeefd6dbafc1bdd253a64b6be192a435dc3/ntm/ntm.py)
* [Backpropagating through a fluid simulation](examples/fluidsim/fluidsim.py)

<img src="examples/fluidsim/animated.gif" width="400">

* [Variational inference in Bayesian neural network](examples/bayesian_neural_net.py)
* [Gaussian process regression](examples/gaussian_process.py)
* [Sampyl, a pure Python MCMC package with HMC and NUTS](https://github.com/mcleonard/sampyl)

## How to install

Install Autograd using Pip:

```shell
pip install autograd
```

Some features require SciPy, which you can install separately or as an
optional dependency along with Autograd:

```shell
pip install "autograd[scipy]"
```

## Authors and maintainers

Autograd was written by [Dougal Maclaurin](https://dougalmaclaurin.com),
[David Duvenaud](https://www.cs.toronto.edu/~duvenaud/),
[Matt Johnson](http://people.csail.mit.edu/mattjj/),
[Jamie Townsend](https://github.com/j-towns)
and many other contributors. The package is currently being maintained by
[Agriya Khetarpal](https://github.com/agriyakhetarpal),
[Fabian Joswig](https://github.com/fjosw) and
[Jamie Townsend](https://github.com/j-towns).
Please feel free to submit any bugs or
feature requests. We'd also love to hear about your experiences with Autograd
in general. Drop us an email!

We want to thank Jasper Snoek and the rest of the HIPS group (led by Prof. Ryan
P. Adams) for helpful contributions and advice; Barak Pearlmutter for
foundational work on automatic differentiation and for guidance on our
implementation; and Analog Devices Inc. (Lyric Labs) and Samsung Advanced Institute
of Technology for their generous support.