Hello!

I am rewrite a following mixture model:

```
model = pm.Model()
with model:
# cluster sizes
p = pm.Dirichlet('p', a=np.array([1., 1., 1.]), shape=k)
# ensure all clusters have some points
p_min_potential = pm.Potential('p_min_potential', tt.switch(tt.min(p) < .1, -np.inf, 0))
# cluster centers
means = pm.Normal('means', mu=[0, 0, 0], sd=15, shape=k)
# break symmetry
order_means_potential = pm.Potential('order_means_potential',
tt.switch(means[1]-means[0] < 0, -np.inf, 0)
+ tt.switch(means[2]-means[1] < 0, -np.inf, 0))
# measurement error
sd = pm.Uniform('sd', lower=0, upper=20)
# latent cluster of each observation
category = pm.Categorical('category',
p=p,
shape=ndata)
# likelihood for each observed value
points = pm.Normal('obs',
mu=means[category],
sd=sd,
observed=data)
```

I want to rewrite it to a version that doesnâ€™t involve categorical random variable, i.e. using normal mixture.

```
with pm.Model() as model2:
# cluster sizes
p = pm.Dirichlet('p', a=np.array([1., 1., 1.]), shape=k)
# cluster centers
means = pm.Normal('means', mu=[0, 0, 0], sd=15, shape=k)
# measurement error
sd = pm.Uniform('sd', lower=0, upper=20)
points = pm.NormalMixture('mix', w=p, mu=means, sd=sd, observed=data, comp_shape=k, shape=ndata)
```

I keep getting following errors

`static_cast<int>( ). 6 errors generated.. ", '[Elemwise{sub,no_inplace}(<TensorType(float64, col)>, <TensorType(float64, row)>)]')`

By the way, the data used are:

```
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('paper')
sns.set_style('darkgrid')
import pymc3 as pm
import theano.tensor as tt
# simulate data
np.random.seed(12345)
k = 3
ndata = 500
spread = 5
centers = np.array([-spread, 0, spread])
# simulate data from mixture distribution
v = np.random.randint(0, k, ndata)
data = centers[v] + np.random.randn(ndata)
```