Metadata-Version: 2.1
Name: fomoro-pyoneer
Version: 0.2.0
Summary: Tensor utilities, reinforcement learning, and more!
Home-page: https://github.com/fomorians/pyoneer
Author: Fomoro AI
Author-email: team@fomoro.com
License: Apache 2.0
Download-URL: https://github.com/fomorians/pyoneer/archive/v0.2.tar.gz
Description: # pyoneer
        
        Tensor utilities, reinforcement learning, and more! Designed to make research easier with low-level abstractions for common operations.
        
        ## Usage
        
        For the top-level utilities, import like so:
        
            import pyoneer as pynr
            pynr.math.rescale(...)
        
        For the larger sub-modules, such as reinforcement learning, we recommend:
        
            import pyoneer.rl as pyrl
            loss_fn = pyrl.losses.PolicyGradient(...)
        
        In general, the Pyoneer API tries to adhere to the TensorFlow 2.0 API.
        
        ### Examples
        
        - [TF 2.0 Proximal Policy Optimization](https://github.com/fomorians/ppo)
        
        ## API
        
        ### Activations ([`pynr.activations`](pyoneer/activations))
        
        - `pynr.activations.swish`
        
        ### Debugging ([`pynr.debugging`](pyoneer/debugging))
        
        - `pynr.debugging.Stopwatch`
        
        ### Distributions ([`pynr.distributions`](pyoneer/distributions))
        
        - `pynr.distributions.MultiCategorical`
        
        ### Initializers ([`pynr.initializers`](pyoneer/initializers))
        
        - `pynr.initializers.SoftplusInverse`
        
        ### Layers ([`pynr.layers`](pyoneer/layers))
        
        - `pynr.layers.Swish`
        - `pynr.layers.OneHotEncoder`
        - `pynr.layers.AngleEncoder`
        
        ### Tensor Manipulation ([`pynr.manip`](pyoneer/manip))
        
        - `pynr.manip.flatten`
        - `pynr.manip.batched_index`
        - `pynr.manip.pad_or_truncate`
        - `pynr.manip.shift`
        
        ### Math ([`pynr.math`](pyoneer/math))
        
        - `pynr.math.to_radians`
        - `pynr.math.to_degrees`
        - `pynr.math.to_cartesian`
        - `pynr.math.to_polar`
        - `pynr.math.RADIANS_TO_DEGREES`
        - `pynr.math.DEGREES_TO_RADIANS`
        - `pynr.math.isclose`
        - `pynr.math.safe_divide`
        - `pynr.math.rescale`
        - `pynr.math.normalize`
        - `pynr.math.denormalize`
        
        ### Metrics ([`pynr.metrics`](pyoneer/metrics))
        
        - `pynr.metrics.mape`
        - `pynr.metrics.smape`
        - `pynr.metrics.MAPE`
        - `pynr.metrics.SMAPE`
        
        ### Moments ([`pynr.moments`](pyoneer/moments))
        
        - `pynr.moments.range_moments`
        - `pynr.moments.StaticMoments`
        - `pynr.moments.StreamingMoments`
        - `pynr.moments.ExponentialMovingMoments`
        
        ### Learning Rate Schedules ([`pynr.schedules`](pyoneer/schedules))
        
        - `pynr.schedules.CyclicSchedule`
        
        ### Reinforcement Learning ([`pynr.rl`](pyoneer/rl))
        
        Utilities for reinforcement learning.
        
        #### Losses ([`pynr.rl.losses`](pyoneer/rl/losses))
        
        - `pynr.rl.losses.policy_gradient`
        - `pynr.rl.losses.policy_entropy`
        - `pynr.rl.losses.clipped_policy_gradient`
        - `pynr.rl.losses.PolicyGradient`
        - `pynr.rl.losses.PolicyEntropy`
        - `pynr.rl.losses.ClippedPolicyGradient`
        
        #### Targets ([`pynr.rl.targets`](pyoneer/rl/targets))
        
        - `pynr.rl.targets.DiscountedReturns`
        - `pynr.rl.targets.GeneralizedAdvantages`
        
        #### Strategies ([`pynr.rl.strategies`](pyoneer/rl/strategies))
        
        - `pynr.rl.strategies.EpsilonGreedy`
        - `pynr.rl.strategies.Mode`
        - `pynr.rl.strategies.Sample`
        
        #### Wrappers ([`pynr.rl.wrappers`](pyoneer/rl/wrappers))
        
        - `pynr.rl.wrappers.ObservationCoordinates`
        - `pynr.rl.wrappers.ObservationNormalization`
        - `pynr.rl.wrappers.Batch`
        - `pynr.rl.wrappers.BatchProcess`
        - `pynr.rl.wrappers.Process`
        
        ## Installation
        
        There are a few options for installation:
        
        1. (Recommended) Install with `pipenv`:
        
                pipenv install fomoro-pyoneer
        
        2. Install locally for development with `pipenv`:
        
                git clone https://github.com/fomorians/pyoneer.git
                cd pyoneer
                pipenv install
                pipenv shell
        
        ## Testing
        
        There are a few options for testing:
        
        1. Run all tests:
        
                python -m unittest discover -bfp '*_test.py'
        
        2. Run specific tests:
        
                python -m pyoneer.math.logical_ops_test
        
        ## Contributing
        
        File 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.
Keywords: tensorflow,machine learning,reinforcement learning,eager execution,deep learning
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Description-Content-Type: text/markdown
Provides-Extra: dev
