Metadata-Version: 2.1
Name: zunis
Version: 0.2
Summary: Neural Importance Sampling
Home-page: https://zunis.readthedocs.io
Author: Nicolas Deutschmann
Author-email: nicolas.deutschmann@gmail.com
License: MIT
Download-URL: https://github.com/ndeutschmann/zunis
Description: ZüNIS: Normalizing flows for neural importance sampling
        ==============================
        
        ZüNIS (Zürich Neural Importance Sampling) a work-in-progress Pytorch-based library for Monte-Carlo integration
         based on Neural imporance sampling [[1]](https://arxiv.org/abs/1808.03856), developed at ETH Zürich.
        In simple terms, we use artificial intelligence to compute integrals faster.
        
        The goal is to provide a flexible library to integrate black-box functions with a level of automation comparable to the
        VEGAS Library [[2]](https://pypi.org/project/vegas/), while using state-of-the-art methods that go around
        the limitations of existing tools.
        
        ## Installation
        
        ### Using `pip`
        
        The library is available on PyPI:
        ```bash
         pip install zunis 
        ```
        
        The latest version can be installed directly from GitHub:
        ```bash
            pip install 'git+https://github.com/ndeutschmann/zunis#egg=zunis&subdirectory=zunis_lib'
        ```
        
        ### Setting up a development environment
        
        If you would like to contribute to the library, run the benchmarks or try the examples,
        the easiest is to clone this repository directly and install the extended requirements:
        ````bash
        # Clone the repository
        git clone https://github.com/ndeutschmann/zunis.git ./zunis
        # Create a virtual environment (recommended)
        python3.7 -m venv  zunis_venv
        source ./zunis_venv/bin/activate
        pip install --upgrade pip
        # Install the requirements
        cd ./zunis
        pip install -r requirements.txt
        # Run one benchmark (GPU recommended)
        cd ./experiments/benchmarks
        python benchmark_hypersphere.py
        ````
        
        ## Library usage
        
        For basic applications, the integrator is provided with default choices and can be created and used as follows:
        
        ```python
        import torch
        from zunis.integration import Integrator
        
        device = torch.device("cuda")
        
        d = 2
        
        def f(x):
            return x[:,0]**2 + x[:,1]**2
        
        integrator = Integrator(d=d,f=f,device=device)
        result, uncertainty, history = integrator.integrate()
        ```
        
        The function `f` is integrated over the `d`-dimensional unit hypercube and 
        
        * takes `torch.Tensor` batched inputs with shape `(N,d)` for arbitrary batch size `N` on `device`
        * returns `torch.Tensor` batched inputs with shape `(N,)` for arbitrary batch size `N` on `device`
        
        A more systematic documentation is under construction [here](https://zunis.readthedocs.io).
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