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    "info": {
        "author": "Joan Puigcerver",
        "author_email": "joapuipe@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "# prob-phoc\n\n[![Build Status](https://travis-ci.com/jpuigcerver/prob-phoc.svg?branch=master)](https://travis-ci.com/jpuigcerver/prob-phoc)\n\nPyTorch functions to compute meaningful probabilistic relevance scores from\nPHOC (Pyramid of Histograms of Characters) embeddings.\nAlthough they are called Pyramid of Histograms of Characters, in practice\nthey are a Pyramid of Bag of Characters. At the end, each word is\nrepresented by a high-dimensional binary vector.\n\nSee the [wiki](https://github.com/jpuigcerver/prob-phoc/wiki)\nfor additional details.\n\n## Usage\n\nThe library provides two functions: `cphoc` and `pphoc`, which are\nsimilar to SciPy's `cdist` and `pdist`:\n\nBoth functions can operate with PHOC embeddings in the probability space (where\neach dimension is a real number in the range [0, 1]), or in the log-probability\nspace (where each dimension is the logarithm of a probability). These are also\nsometimes refered to as the Real and Log semirings.\n\n```python\nimport torch\nfrom prob_phoc import cphoc, pphoc\n\nx = torch.Tensor(...)\ny = torch.Tensor(...)\n\n# Compute the log-relevance scores between all pairs of rows in x, y.\n# Note: x and y must have the PHOC log-probabilities.\nlogprob = cphoc(x, y)\n\n# This is equivalent to:\nlogprob = cphoc(x, y, method=\"sum_prod_log\")\n\n# If your matrices have probabilities instead of log-probabilities, use:\nprob = cphoc(x, y, method=\"sum_prob_real\")\n\n# Compute the log-relevance scores between all pairs of distinct rows in x.\n# Note: The output is a vector with N * (N - 1) / 2 elements.\nlogprob = pphoc(x)\n```\n\n## Installation\n\nThe easiest way is to install the package from PyPI:\n\n```bash\npip install prob-phoc\n```\n\nIf you want to install the latest version from the repository, clone it\nand use the setup.py script to compile and install the library.\n\n```bash\npython setup.py install\n```\n\nYou will need a C++11 compiler (tested with GCC 4.9).\nIf you want to compile with CUDA support, you will also need to install\nthe CUDA Toolkit (tested with versions 8.0, 9.0 and 10.0)\n\n## Tests and benchmarks\n\nAfter the installation, you can run the tests to ensure that everything is\nworking fine.\n\n```bash\npython -m prob_phoc.test\n```\n\nI have also some benchmarks to compare CPU vs. CUDA, for different matrix\nsizes and float precision. These take quite a long to run, so don't hold\nyour breath.\n\n```bash\npython -m prob_phoc.benchmark\n```",
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        "home_page": "https://github.com/jpuigcerver/prob_phoc",
        "keywords": "",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "prob-phoc",
        "package_url": "https://pypi.org/project/prob-phoc/",
        "platform": "",
        "project_url": "https://pypi.org/project/prob-phoc/",
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        "release_url": "https://pypi.org/project/prob-phoc/0.2.0/",
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        "summary": "Functions to compute probabilistic relevance scores from PHOC embeddings",
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