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        "author": "Jonas Rothfuss, Fabio Ferreira",
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        "description": "[![Build Status](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation.svg?branch=master)](https://travis-ci.org/freelunchtheorem/Conditional_Density_Estimation) [![Downloads](https://pepy.tech/badge/cde)](https://pepy.tech/project/cde)\n\n# Conditional Density Estimation (CDE)\n\n## Description\nImplementations of various methods for conditional density estimation\n\n* **Parametric neural network based methods**\n    * Mixture Density Network (MDN)\n    * Kernel Mixture Network (KMN)\n    * Normalizing Flows (NF)\n* **Nonparametric methods**\n    * Conditional Kernel Density Estimation (CKDE)\n    * Neighborhood Kernel Density Estimation (NKDE)\n* **Semiparametric methods**\n    * Least Squares Conditional Density Estimation (LSKDE)\n    \nBeyond estimating conditional probability densities, the package features extensive functionality for computing:\n* **Centered moments:** mean, covariance, skewness and kurtosis\n* **Statistical divergences:** KL-divergence, JS-divergence, Hellinger distance\n* **Percentiles and expected shortfall**\n\n## Installation\n\nTo use the library, you can directly use the python package index:\n```bash\npip install cde\n```\nor clone the GitHub repository and run \n```bash\npip install .\n``` \nNote that the package only supports tensorflow versions between 1.4 and 1.7.\n## Documentation and paper\nSee the documentation [here](https://freelunchtheorem.github.io/Conditional_Density_Estimation). A paper on best practices and benchmarks on conditional density estimation with neural networks that makes extensive use of this library can be found [here](https://arxiv.org/abs/1903.00954).\n\n## Usage\nThe following code snipped holds an easy example that demonstrates how to use the cde package.\n```python\nfrom cde.density_simulation import SkewNormal\nfrom cde.density_estimator import KernelMixtureNetwork\nimport numpy as np\n\n\"\"\" simulate some data \"\"\"\ndensity_simulator = SkewNormal(random_seed=22)\nX, Y = density_simulator.simulate(n_samples=3000)\n\n\"\"\" fit density model \"\"\"\nmodel = KernelMixtureNetwork(\"KDE_demo\", ndim_x=1, ndim_y=1, n_centers=50,\n                             x_noise_std=0.2, y_noise_std=0.1, random_seed=22)\nmodel.fit(X, Y)\n\n\"\"\" query the conditional pdf and cdf \"\"\"\nx_cond = np.zeros((1, 1))\ny_query = np.ones((1, 1)) * 0.1\nprob = model.pdf(x_cond, y_query)\ncum_prob = model.cdf(x_cond, y_query)\n\n\"\"\" compute conditional moments & VaR  \"\"\"\nmean = model.mean_(x_cond)[0][0]\nstd = model.std_(x_cond)[0][0]\nskewness = model.skewness(x_cond)[0]\n```\n## Citing\nIf you use CDE in your research, you can cite it as follows:\n\n```\n@article{rothfuss2019conditional,\n  title={Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks},\n  author={Rothfuss, Jonas and Ferreira, Fabio and Walther, Simon and Ulrich, Maxim},\n  journal={arXiv:1903.00954},\n  year={2019}\n}\n\n```\n\n## Todo\n- creating a branch just for our conditional estimators + python package",
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