#!/usr/bin/env python
# coding: utf-8

# # Table of Contents
#  <p>

# In[1]:


import pymc3 as pm
import numpy as np
import theano.tensor as tt
from theano import shared

np.random.seed(42)


# In[2]:


y = shared(np.concatenate([np.random.poisson(5, size=10), np.random.poisson(9, size=10)]))
x = shared(np.random.randn(20))


# In[3]:


with pm.Model() as model:
    μ = pm.Normal('μ', 0, 1, shape=2)
    β = pm.Normal('β', 0, 1)
    lam = pm.Deterministic('lam', tt.exp(tt.outer(μ, β*x).T))

    components = pm.Poisson.dist(mu=lam, shape=(20, 2))  

    w = pm.Dirichlet('w', a=np.array([1, 1]))  # two mixture component weights.

    like = pm.Mixture('like', w=w, comp_dists=components, observed=y)


# In[4]:


with model:
    trace = pm.sample(1000)


# In[5]:


with model:
    pred = pm.sample_ppc(trace, samples=1000)


# In[6]:


pred['like'].shape