def marginal_loss(marginals, workload, cache):
answers = []
for proj, W in workload:
for cl in marginals:
if set(proj) <= set(cl):
mu = marginals[cl].project(proj)
x = mu.values.flatten()
answers.append(W.dot(x))
break
total = x.sum()
answers = np.concatenate(answers) / total
gradient = grad(log_likelihood, argnum=0)
loss = log_likelihood(answers, cache)
danswers = gradient(answers, cache)
i = 0
gradients = {cl: Factor.zeros(marginals[cl].domain) for cl in marginals}
for proj, W in workload:
for cl in marginals:
if set(proj) <= set(cl):
m = W.shape[0]
dmu = W.T.dot(danswers[i:i + m]) / total
dom = gradients[cl].domain.project(proj)
gradients[cl] += Factor(dom, dmu)
i += m
break
print(loss)
return loss, graphical_model.CliqueVector(gradients)
def multWeightsFast(self, measurements, total):
domain = self.domain
groups, projections = _cluster(measurements)
factors = []
for group, proj in zip(groups, projections):
dom = self.domain.project(proj)
fact = Factor.uniform(dom)
for i in range(self.iters):
update = Factor.zeros(dom)
for Q, y, noise_scale, p in group:
dom2 = dom.project(p)
hatx = fact.project(p).values.flatten()*total
error = y - Q.dot(hatx)
update += Factor(dom2, Q.T.dot(error).reshape(dom2.shape))
fact *= np.exp(update / (2*total))
fact /= fact.sum()
factors.append(fact)
self.model = ProductDist(factors, self.domain, total)