AttributeError: GaussianMixture instance has no attribute 'loglike' - python-2.7

Question

I'm trying to implement Gaussian Mixture Model with Expectation–Maximization algorithm and I get this error.
This is the Gaussian Mixture model that I used:
class GaussianMixture:
"Model mixture of two univariate Gaussians and their EM estimation"
def __init__(self, data, mu_min=min(data), mu_max=max(data), sigma_min=.1, sigma_max=1, mix=.5):
self.data = data
#init with multiple gaussians
self.one = Gaussian(uniform(mu_min, mu_max),
uniform(sigma_min, sigma_max))
self.two = Gaussian(uniform(mu_min, mu_max),
uniform(sigma_min, sigma_max))
#as well as how much to mix them
self.mix = mix
def Estep(self):
"Perform an E(stimation)-step, freshening up self.loglike in the process"
# compute weights
self.loglike = 0. # = log(p = 1)
for datum in self.data:
# unnormalized weights
wp1 = self.one.pdf(datum) * self.mix
wp2 = self.two.pdf(datum) * (1. - self.mix)
# compute denominator
den = wp1 + wp2
# normalize
wp1 /= den
wp2 /= den
# add into loglike
self.loglike += log(wp1 + wp2)
# yield weight tuple
yield (wp1, wp2)
def Mstep(self, weights):
"Perform an M(aximization)-step"
# compute denominators
(left, rigt) = zip(*weights)
one_den = sum(left)
two_den = sum(rigt)
# compute new means
self.one.mu = sum(w * d / one_den for (w, d) in zip(left, data))
self.two.mu = sum(w * d / two_den for (w, d) in zip(rigt, data))
# compute new sigmas
self.one.sigma = sqrt(sum(w * ((d - self.one.mu) ** 2)
for (w, d) in zip(left, data)) / one_den)
self.two.sigma = sqrt(sum(w * ((d - self.two.mu) ** 2)
for (w, d) in zip(rigt, data)) / two_den)
# compute new mix
self.mix = one_den / len(data)
def iterate(self, N=1, verbose=False):
"Perform N iterations, then compute log-likelihood"
def pdf(self, x):
return (self.mix)*self.one.pdf(x) + (1-self.mix)*self.two.pdf(x)
def __repr__(self):
return 'GaussianMixture({0}, {1}, mix={2.03})'.format(self.one,
self.two,
self.mix)
def __str__(self):
return 'Mixture: {0}, {1}, mix={2:.03})'.format(self.one,
self.two,
self.mix)
And then , while training I get that error in the conditional statement.
# Check out the fitting process
n_iterations = 5
best_mix = None
best_loglike = float('-inf')
mix = GaussianMixture(data)
for _ in range(n_iterations):
try:
#train!
mix.iterate(verbose=True)
if mix.loglike > best_loglike:
best_loglike = mix.loglike
best_mix = mix
except (ZeroDivisionError, ValueError, RuntimeWarning): # Catch division errors from bad starts, and just throw them out...
pass
Any ideas why I have the following error?
AttributeError: GaussianMixture instance has no attribute 'loglike'

Answer 1

The loglike attribute is only created when you call the Estep method.
def Estep(self):
"Perform an E(stimation)-step, freshening up self.loglike in the process"
# compute weights
self.loglike = 0. # = log(p = 1)
You didn't call Estep between creating the GaussianMixture instance and mix.loglike:
mix = GaussianMixture(data)
for _ in range(n_iterations):
try:
#train!
mix.iterate(verbose=True)
if mix.loglike > best_loglike:
And the iterate method is empty (It looks like you forgot some code here).
def iterate(self, N=1, verbose=False):
"Perform N iterations, then compute log-likelihood"
So, there'll be no loglike attribute set on the mix instance by the time you do if mix.loglike. Hence, the AttributeError.
You need to do one of the following:
Call the Estep method (since you set self.loglike there)
Define a loglike attribute in __init__