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    "info": {
        "author": "Guillaume Baudart, Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar",
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        "description": "[![Build Status](https://travis-ci.org/IBM/yaps.svg?branch=master)](https://travis-ci.org/IBM/yaps) [![PyPI version](https://badge.fury.io/py/yaps.svg)](https://badge.fury.io/py/yaps) [![Documentation Status](https://readthedocs.org/projects/yaps/badge/?version=latest)](https://yaps.readthedocs.io/en/latest/?badge=latest)\n\n# YAPS\n\nYaps is a new surface language for [Stan](http://mc-stan.org/). It lets\nusers write Stan programs using Python syntax. For example, consider the\nfollowing Stan program, which models tosses `x` of a coin with bias `theta`:\n```stan\ndata {\n  int<lower=0,upper=1> x[10];\n}\nparameters {\n  real<lower=0,upper=1> theta;\n}\nmodel {\n  theta ~ uniform(0,1);\n  for (i in 1:10)\n    x[i] ~ bernoulli(theta);\n}\n```\nIt can be rewritten in Python has follows:\n```python\nimport yaps\nfrom yaps.lib import int, real, uniform, bernoulli\n\n@yaps.model\ndef coin(x: int(lower=0, upper=1)[10]):\n    theta: real(lower=0, upper=1) <~ uniform(0, 1)\n    for i in range(1,11):\n        x[i] <~ bernoulli(theta)\n```\n\nThe `@yaps.model` decorator indicates that the function following it\nis a Stan program.  While being syntactically Python, it is\nsemantically reinterpreted as Stan.\n\nThe argument of the function corresponds to the `data` block. The\ntype of the data must be declared. Here, you can see that `x` is an\narray of 10 integers between `0` and `1` (`int(lower=0, upper=1)[10]`).\n\nParameters are declared as variables with their type in the body of\nthe function. Their prior can be defined using the sampling operator\n`<~` (or `is`).\n\nThe body of the function corresponds to the Stan model. Python syntax\nis used for the imperative constructs of the model, like the `for`\nloop in the example. The operator `<~` is used to represent sampling\nand `x.T[a,b]` for truncated distribution.\n\nNote that Stan array are 1-based. The range of the loop is thus `range(1, 11)`,\nthat is 1,2, ... 10.\n\nOther Stan blocks can be introduced using the `with` syntax of Python.\nFor example, the previous program could also be written as follows:\n```python\n@yaps.model\ndef coin(x: int(lower=0, upper=1)[10]):\n    with parameters:\n        theta: real(lower=0, upper=1)\n    with model:\n        theta <~ uniform(0, 1)\n        for i in range(1,11):\n            x[i] <~ bernoulli(theta)\n```\n\nThe corresponding Stan program can be displayed using the `print` function:\n```python\nprint(coin)\n```\n\nFinally, it is possible to launch Bayesian inference on the defined model applied to some data.\nThe communication with the Stan inference engine is based on on [PyCmdStan](https://pycmdstan.readthedocs.io/en/latest/).\n```python\nflips = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])\nconstrained_coin = coin(x=flips)\nconstrained_coin.sample(data=constrained_coin.data)\n```\nNote that arrays must be cast into numpy arrays (see pycmdstan documentation).\n\nAfter the inference the attribute `posterior` of the constrained model is an object with fields for the latent model parameters:\n```python\ntheta_mean = constrained_coin.posterior.theta.mean()\nprint(\"mean of theta: {:.3f}\".format(theta_mean))\n```\n\nYaps provides a lighter syntax to Stan programs. Since Yaps uses Python syntax, users can take advantage of Python tooling\nfor syntax highlighting, indentation, error reporting, ...\n\n## Install\n\nYaps depends on the following python packages:\n- astor\n- graphviz\n- antlr4-python3-runtime\n- pycmdstan\n\nTo install Yaps and all its dependencies run:\n```\npip install yaps\n```\n\nTo install from source, first clone the repo, then:\n```\npip install .\n```\n\nBy default, communication with the Stan inference engine is based on [PyCmdStan](https://pycmdstan.readthedocs.io/en/latest/). To run inference, you first need to install [CmdStan](http://mc-stan.org/users/interfaces/cmdstan) and set the CMDSTAN environment variable to point to your CmdStan directory.\n\n```\nexport CMDSTAN=/path/to/cmdstan\n```\n\n## Tools\n\nWe provide a tool to compile Stan files to Yaps syntax.\nFor instance, if `path/to/coin.stan` contain the Stan model presented at the beginning, then:\n```\nstan2yaps path/to/coin.stan\n```\noutputs:\n```\n# -------------\n# tests/stan/coin.stan\n# -------------\n@yaps.model\ndef stan_model(x: int(lower=0, upper=1)[10]):\n    theta: real\n    theta is uniform(0.0, 1.0)\n    for i in range(1, 10 + 1):\n        x[(i),] is bernoulli(theta)\n    print(x)\n```\n\nCompilers from Yaps to Stan and from Stan to Yaps can also be invoked programmatically using the following functions:\n```python\nyaps.from_stan(code_string=None, code_file=None)  # Compile a Stan model to Yaps\nyaps.to_stan(code_string=None, code_file=None)    # Compile a Yaps model to Stan\n```\n\n## Documentation\n\nThe full documentation is available at https://yaps.readthedocs.io. You can find more details in the following [article](https://arxiv.org/abs/1812.04125):\n```\n@article{2018-yaps-stan,\n  author = {Baudart, Guillaume and Hirzel, Martin and Kate, Kiran and Mandel, Louis and Shinnar, Avraham},\n  title = \"{Yaps: Python Frontend to Stan}\",\n  journal = {arXiv e-prints},\n  year = 2018,\n  month = Dec,\n  url = {https://arxiv.org/abs/1812.04125},\n}\n```\n\n\n## License\n\nYaps is distributed under the terms of the Apache 2.0 License, see\n[LICENSE.txt](LICENSE.txt)\n\n\n\n## Contributions\n\nYaps is still at an early phase of development and we welcome\ncontributions. Contributors are expected to submit a 'Developer's\nCertificate of Origin', which can be found in [DCO1.1.txt](DCO1.1.txt).\n\n\n\n",
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