Package: skstan
scikit-stan===========|Build Status| |codecov|What is scikit-stan-------------------``scikit-stan`` will enable you to use various bayesian models based on``stan``\ (http://mc-stan.org) and ``pystan`` with a elegant interfacelike a ``scikit-learn`` or ``keras``.Demo----.. code:: pythonimport numpy as npfrom skstan.regression.linear_models import LogisticRegressionif __name__ == '__main__':x = np.array([[1,2,3,],[1,2,7,],[1,0,3,],[1,1,3,],[3,7,3,],])y = np.array([0,0,0,0,1])glm = LogisticRegression(shrinkage=10, chains=8)fit = glm.fit(x, y)Then we got result object ``fit``, and field ``stanfit`` is a stanfitobject of pystan... code:: pythonprint(fit.stanfit)It gives following::Inference for Stan model: anon_model_f63cd5ccdd67c22034b2490ae4c9cdd1.4 chains, each with iter=2000; warmup=1000; thin=1;post-warmup draws per chain=1000, total post-warmup draws=4000.mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhatalpha[0]   2.23    0.29   8.88 -14.39  -3.87   2.02   8.07  20.67    966    1.0alpha[1]   7.81    0.18   5.29  -1.08   4.01   7.48  11.23  19.01    880    1.0alpha[2]  -9.79    0.22   5.87 -22.91 -13.41  -9.37  -5.38  -0.17    728    1.0beta      -2.48    0.29    9.8 -22.63  -9.03   -2.3   3.99  16.91   1146    1.0yp[0]    -13.99    0.32  11.19 -40.69 -20.24 -11.35  -5.42    0.3   1259    1.0yp[1]    -53.15    1.14  32.08 -128.4 -71.99 -48.46 -29.35  -5.24    790    1.0yp[2]    -29.61     0.6  16.66 -67.24 -39.44 -27.97  -17.0  -4.37    771    1.0yp[3]     -21.8    0.44  13.17 -52.98 -29.03 -19.57 -11.91  -3.23    894    1.0yp[4]     29.51    0.69  24.68   0.58   10.3  23.36  42.72  90.17   1276    1.0lp__      -2.16    0.05   1.48  -5.93   -2.9  -1.81  -1.07  -0.32    956    1.0Samples were drawn using NUTS at Thu Apr 13 07:52:33 2017.For each parameter, n_eff is a crude measure of effective sample size,and Rhat is the potential scale reduction factor on split chains (atconvergence, Rhat=1).Result object of ``skstan`` also have prediction methods. Predictedvalues can be obtained as samples from distribution with a``predict_dist`` method, because it is bayesian model... code:: pythonyp_dist = fit.predict_dist(x)print(yp_dist)Then we got::array([[  2.63886682e-08,   5.23976746e-04,   5.54863097e-05, ...,2.46008578e-08,   3.74830192e-01,   3.45994043e-03],[  1.07746578e-22,   1.01664809e-18,   4.12813154e-26, ...,5.64992544e-19,   7.24386097e-12,   1.75795155e-23],[  8.04688037e-22,   4.44522113e-12,   1.42920488e-11, ...,7.71565191e-13,   5.13118658e-05,   4.26331280e-05],[  4.60810657e-15,   4.82743551e-08,   2.81612678e-08, ...,1.37772153e-10,   5.51614998e-03,   3.84594197e-04],[  9.99999998e-01,   1.00000000e+00,   1.00000000e+00, ...,9.99965378e-01,   1.00000000e+00,   1.00000000e+00]])So let's check the histgram of first row with ``pandas.Series``... code:: pythonimport pandas as pdpd.Series(yp_dist[0]).hist(bins=20).. figure:: image/hist.png:alt: Histgram of first rowHistgram of first rowIf you need a median of samples, you can get it with just ``predict``method.. code:: pythonyp = fit.predict(x)print(yp)gives::array([  1.17280235e-05,   9.01419773e-22,   7.16023732e-13,3.18368664e-09,   1.00000000e+00])How to install--------------Install~~~~~~~.. code:: shgit clone https://github.com/BayesianFreaks/scikit-stancd scikit-stanpython3 setup.py installUninstall~~~~~~~~~.. code:: shpip3 uninstall scikit-stanUsing python2?==============Are you joking?We can't touch you because we are living in the future from you, andyou're living in past ages. Please say hello to Nobunaga Oda.We will always use newest features of the latest version of python, soyou should use the latest version of python.Models======Ready-----Regression Models~~~~~~~~~~~~~~~~~-  Linear Regrassion-  Poisson Regression-  Logistic RegressionNext Steps----------Regression Models~~~~~~~~~~~~~~~~~-  Gamma Regression-  GLMM-  etc...Time Series Models~~~~~~~~~~~~~~~~~~-  AR Model-  MA Model-  ARMA Model-  ARIMA Model-  ARCH Model-  GARCH Model-  TAR Model-  State Space Model-  or Some Dynamic Regression Models-  etc...Clustering Model~~~~~~~~~~~~~~~~-  Gaussian Mixture Model-  Latent Dirichlet Allocation-  etc...Particular Application~~~~~~~~~~~~~~~~~~~~~~-  Modeling about online-advertisement-  Decompose time series data-  Empirical Bayesian Estimation.. |Build Status| image:: https://travis-ci.org/BayesianFreaks/scikit-stan.svg?branch=master:target: https://travis-ci.org/BayesianFreaks/scikit-stan.. |codecov| image:: https://codecov.io/gh/BayesianFreaks/scikit-stan/branch/master/graph/badge.svg:target: https://codecov.io/gh/BayesianFreaks/scikit-stan