def model_gen():
variables = []
intercept = pymc.Normal("intercept", mu=0, tau=50**-2)
sd = pymc.Gamma("sd", alpha=3, beta=2.0)
responses = pymc.Normal("responses",
mu=zeros((nPredictors, 1)),
tau=ones((nPredictors, 1)) * 5**-2)
variables.append(intercept)
variables.append(responses)
variables.append(sd)
obsMeans = intercept + pymc.sum(responses * observedPredictors, axis=0)
obs = pymc.Normal("obs",
mu=obsMeans,
tau=sd**-2,
observed=True,
value=data)
variables.append(obs)
return variables
def poisson_regression(targets, predictors, iters=2000):
""" Return the posterior of a Bayesian Poisson regression model.
This function takes the targets and predictors and builds a Poisson
regression model. The predictor coefficients are found by sampling
from the posterior using PyMC (NUTS in particular).
The posterior is returned as an MxN array, where M is the number of
samples and N is the number of predictors. The first column is the
coefficient for the first predictor and so on.
Requires PyMC3
"""
with pm.Model() as poisson_model:
# priors for coefficients
coeffs = pm.Uniform('coeffs', -10, 10, shape=(1, predictors.shape[1]))
p = t.exp(pm.sum(coeffs*predictors.values, 1))
obs = pm.Poisson('obs', p, observed=targets)
start = pm.find_MAP()
step = pm.NUTS(scaling=start)
poisson_trace = pm.sample(iters, step, start=start, progressbar=False)
return poisson_trace['coeffs'].squeeze()
def model_gen():
variables = []
intercept = pymc.Normal("intercept",mu = 0, tau = 50**-2)
sd = pymc.Gamma("sd", alpha = 3 , beta = 2.0)
responses = pymc.Normal("responses", mu = zeros((nPredictors,1)), tau = ones((nPredictors,1)) * 5**-2 )
variables.append(intercept)
variables.append(responses)
variables.append (sd)
obsMeans = intercept + pymc.sum(responses * observedPredictors, axis = 0)
obs = pymc.Normal ("obs", mu = obsMeans, tau = sd**-2, observed = True, value = data)
variables.append(obs)
return variables
'''
Created on Jan 20, 2010
@author: johnsalvatier
'''
from numpy import *
from pymc import *
import pymc
a = ones((3,4))
j= NumpyDeterministics.sum_jacobian('a', a = a, axis = 0)
print j * 1
x = Normal('z', mu = ones((3,4)), tau = ones((3,4)))
y = pymc.sum(x, axis = 1)
print y
'''
Created on Jan 20, 2010
@author: johnsalvatier
'''
from numpy import *
from pymc import *
import pymc
a = ones((3, 4))
j = NumpyDeterministics.sum_jacobian('a', a=a, axis=0)
print j * 1
x = Normal('z', mu=ones((3, 4)), tau=ones((3, 4)))
y = pymc.sum(x, axis=1)
print y
