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import autograd.numpy as np
from autograd import grad
def g(m):
return np.log(1 - np.exp(-m))
def h(m):
return -m
def ll(cascades, theta):
res = 0
for x, s in cascades:
for t in range(1, x.shape[1] + 1):
w = np.dot(x[t-1], theta)
res += g(w)[x[t]].sum() + h(w)[~x[t] & s[t]].sum()
def sample(params):
return np.random.normal(*params)
def ll_full(params, cascades):
nsamples = 50
return np.mean([ll(cascades, sample(params)) for _ in xrange(nsamples)])
grad_ll_full = grad(ll_full)
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