Metadata-Version: 2.1
Name: njet
Version: 0.3.2
Summary: Lightweight automatic differentiation package for higher-order differentiation.
Home-page: UNKNOWN
Author: Malte Titze
Author-email: mtitze@users.noreply.github.com
License: GPL-3.0-or-later
Project-URL: homepage, https://njet.readthedocs.io/en/latest/index.html
Project-URL: repository, https://github.com/mtitze/njet
Description: # njet: Lightweight automatic differentiation
        
        A lightweight AD package, using forward-mode automatic differentiation, in order to determine the
        higher-order derivatives of a given function in several variables.
        
        ## Features
        
        - Higher-order (forward-mode) automatic differentiation in several variables.
        - Support for NumPy, SymPy and mpmath.
        - Differentiation of expressions containing nested higher-order derivatives.
        - Complex differentiation (Wirtinger calculus) possible.
        - Lightweight and easy to use.
        
        ## Installation
        
        Install this module with pip
        
        ```sh
        pip install njet
        ```
        
        ## Quickstart
        
        An example function we want to differentiate
        ```python
        from njet.functions import exp
        f = lambda x, y, z: exp(-0.23*x**2 - 0.33*x*y - 0.11*z**2)
        ```
        
        Generate a class to handle the derivatives of the given function (here up to order 3)
        ```python
        from njet import derive
        df = derive(f, order=3)
        ```
        
        Evaluate the derivatives at a specific point
        ```python
        df([0.4, 2.1, 1.73])
        
        {(0, 0, 0): 0.5255977986928584,
         (0, 0, 1): -0.2000425221825019,
         (1, 0, 0): -0.46094926945363685,
         (0, 1, 0): -0.06937890942745731,
         (0, 0, 2): -0.03949533176976862,
         (0, 2, 0): 0.009158016044424365,
         (1, 0, 1): 0.1754372919540542,
         (0, 1, 1): 0.026405612928090252,
         (2, 0, 0): 0.1624775219121247,
         (1, 1, 0): -0.11260197000076322,
         (2, 1, 0): 0.2827794849469999,
         (1, 1, 1): 0.04285630978229049,
         (0, 1, 2): 0.005213383793609458,
         (0, 2, 1): -0.0034855409065079135,
         (0, 3, 0): -0.0012088581178640162,
         (3, 0, 0): 0.2815805411804125,
         (2, 0, 1): -0.061838944839754675,
         (0, 0, 3): 0.10305063303187477,
         (1, 2, 0): 0.03775850015116166,
         (1, 0, 2): 0.034637405962087094}
        ```
        The indices here correspond to the powers of the variables x, y, z
        in the multivariate Taylor expansion. They can be translated to
        the tensor indices of the corresponding multilinear map using a
        built-in routine. Example:
        
        Obtain the gradient and the Hessian
        ```python
        df.grad()
        
        {(2,): -0.2000425221825019,
         (0,): -0.46094926945363685,
         (1,): -0.06937890942745731}
        ```
        
        ```python
        df.hess()
        
        {(2, 2): -0.03949533176976862,
         (1, 1): 0.009158016044424365,
         (0, 2): 0.1754372919540542,
         (1, 2): 0.026405612928090252,
         (0, 0): 0.1624775219121247,
         (0, 1): -0.11260197000076322}
        ```
        
        ## Further reading
        
        https://njet.readthedocs.io/en/latest/index.html
        
        ## License
        
        This file is part of njet.
        
        njet is free software: you can redistribute it and/or modify
        it under the terms of the GNU General Public License as published by
        the Free Software Foundation, either version 3 of the License, or
        (at your option) any later version.
        
        njet is distributed in the hope that it will be useful,
        but WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
        GNU General Public License for more details.
        
        You should have received a copy of the GNU General Public License
        along with njet. If not, see <https://www.gnu.org/licenses/>.
        
Keywords: AD jet njet automatic differentiation
Platform: UNKNOWN
Requires-Python: <3.11,>=3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
