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    "info": {
        "author": "Fomoro AI",
        "author_email": "team@fomoro.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
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        "description": "# pyoneer\n\nTensor utilities, reinforcement learning, and more! Designed to make research easier with low-level abstractions for common operations.\n\n## Usage\n\nFor the top-level utilities, import like so:\n\n    import pyoneer as pynr\n    pynr.math.rescale(...)\n\nFor the larger sub-modules, such as reinforcement learning, we recommend:\n\n    import pyoneer.rl as pyrl\n    loss_fn = pyrl.losses.PolicyGradient(...)\n\nIn general, the Pyoneer API tries to adhere to the TensorFlow 2.0 API.\n\n### Examples\n\n- [Proximal Policy Optimization with Pyoneer and TF 2.0](https://github.com/fomorians/ppo)\n\n## API\n\n### Activations ([`pynr.activations`](pyoneer/activations))\n\n- `pynr.activations.swish`\n\n### Debugging ([`pynr.debugging`](pyoneer/debugging))\n\n- `pynr.debugging.Stopwatch`\n\n### Distributions ([`pynr.distributions`](pyoneer/distributions))\n\n- `pynr.distributions.MultiCategorical`\n\n### Initializers ([`pynr.initializers`](pyoneer/initializers))\n\n- `pynr.initializers.SoftplusInverse`\n\n### Layers ([`pynr.layers`](pyoneer/layers))\n\n- `pynr.layers.Swish`\n- `pynr.layers.OneHotEncoder`\n- `pynr.layers.AngleEncoder`\n- `pynr.layers.Nest`\n\n### Tensor Manipulation ([`pynr.manip`](pyoneer/manip))\n\n- `pynr.manip.flatten`\n- `pynr.manip.batched_index`\n- `pynr.manip.pad_or_truncate`\n- `pynr.manip.shift`\n\n### Math ([`pynr.math`](pyoneer/math))\n\n- `pynr.math.to_radians`\n- `pynr.math.to_degrees`\n- `pynr.math.to_cartesian`\n- `pynr.math.to_polar`\n- `pynr.math.RADIANS_TO_DEGREES`\n- `pynr.math.DEGREES_TO_RADIANS`\n- `pynr.math.isclose`\n- `pynr.math.safe_divide`\n- `pynr.math.rescale`\n- `pynr.math.normalize`\n- `pynr.math.denormalize`\n\n### Metrics ([`pynr.metrics`](pyoneer/metrics))\n\n- `pynr.metrics.mape`\n- `pynr.metrics.smape`\n- `pynr.metrics.MAPE`\n- `pynr.metrics.SMAPE`\n\n### Moments ([`pynr.moments`](pyoneer/moments))\n\n- `pynr.moments.range_moments`\n- `pynr.moments.StaticMoments`\n- `pynr.moments.StreamingMoments`\n- `pynr.moments.ExponentialMovingMoments`\n\n### Learning Rate Schedules ([`pynr.schedules`](pyoneer/schedules))\n\n- `pynr.schedules.CyclicSchedule`\n\n### Reinforcement Learning ([`pynr.rl`](pyoneer/rl))\n\nUtilities for reinforcement learning.\n\n#### Losses ([`pynr.rl.losses`](pyoneer/rl/losses))\n\n- `pynr.rl.losses.policy_gradient`\n- `pynr.rl.losses.policy_entropy`\n- `pynr.rl.losses.clipped_policy_gradient`\n- `pynr.rl.losses.PolicyGradient`\n- `pynr.rl.losses.PolicyEntropy`\n- `pynr.rl.losses.ClippedPolicyGradient`\n\n#### Targets ([`pynr.rl.targets`](pyoneer/rl/targets))\n\n- `pynr.rl.targets.DiscountedReturns`\n- `pynr.rl.targets.GeneralizedAdvantages`\n\n#### Strategies ([`pynr.rl.strategies`](pyoneer/rl/strategies))\n\n- `pynr.rl.strategies.EpsilonGreedy`\n- `pynr.rl.strategies.Mode`\n- `pynr.rl.strategies.Sample`\n\n#### Wrappers ([`pynr.rl.wrappers`](pyoneer/rl/wrappers))\n\n- `pynr.rl.wrappers.ObservationCoordinates`\n- `pynr.rl.wrappers.ObservationNormalization`\n- `pynr.rl.wrappers.Batch`\n- `pynr.rl.wrappers.Process`\n\n## Installation\n\nThere are a few options for installation:\n\n1. (Recommended) Install with `pipenv`:\n\n        pipenv install fomoro-pyoneer\n\n2. Install locally for development with `pipenv`:\n\n        git clone https://github.com/fomorians/pyoneer.git\n        cd pyoneer\n        pipenv install\n        pipenv shell\n\n## Testing\n\nThere are a few options for testing:\n\n1. Run all tests:\n\n        python -m unittest discover -bfp '*_test.py'\n\n2. Run specific tests:\n\n        python -m pyoneer.math.logical_ops_test\n\n## Contributing\n\nFile an issue following the `ISSUE_TEMPLATE`. If the issue discussion warrants implementation, then submit a pull request from a branch describing the feature. This will eventually get merged into `master` after a few rounds of code review.",
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        "name": "fomoro-pyoneer",
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