test_sample_float32.py

import pymc3 as mc
import theano.tensor as T

## generate some data

nb = 6
nx = 200

b_true_mean = np.random.normal(size=nb) * 5
b_true_cov = np.random.normal(size=(nb,nb))
# make symmetric and positive semidefinite
b_true_cov = np.dot(b_true_cov, b_true_cov.T)

x = np.random.multivariate_normal(b_true_mean, b_true_cov, size=nx)
y = np.dot(b_true_mean, x.T) + np.random.normal(size=nx)

## generate model

model = mc.Model()
with model:
    sigma = mc.HalfCauchy('sigma', beta=10, testval=np.asarray(2, dtype='float32'), dtype='float32')
    intercept = mc.Normal('intercept', 0, sd=10, testval=np.asarray(2, dtype='float32'), dtype='float32')
    b_model = mc.Normal('b_model', 0, sd=20, shape=(nb,), testval=np.zeros((nb,), dtype='float32'), dtype='float32')
    
    likelihood = mc.Normal('y', mu=intercept + T.dot(b_model, x.T), sd=sigma,
        observed=y.astype('float32'), dtype='float32')
    
    step = mc.Metropolis()
    trace = mc.sample(50000, step=step)

does a theano flag have to be set somewhere?