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import main as mn
import theano
from theano import tensor as tsr
import theano.tensor.shared_randomstreams
import numpy as np
n_cascades = 1000
n_nodes = 4
n_samples = 100
srng = tsr.shared_randomstreams.RandomStreams(seed=123)
lr = 5*1e-3
n_epochs = 20
###############Variational Inference####################
# Declare Theano variables
mu = theano.shared(.5 + .2 * np.random.normal(size=(1, n_nodes, n_nodes)),
name="mu", broadcastable=(True, False, False))
sig = theano.shared(.1 + .04 * np.random.normal(size=(1, n_nodes, n_nodes)),
name="sig", broadcastable=(True, False, False))
mu0 = theano.shared(.5 + .2 * np.random.normal(size=(1, n_nodes, n_nodes)),
name="mu", broadcastable=(True, False, False))
sig0 = theano.shared(.1 + .04 * np.random.normal(size=(1, n_nodes, n_nodes)),
name="sig", broadcastable=(True, False, False))
x = tsr.matrix(name='x', dtype='int8')
s = tsr.matrix(name='s', dtype='int8')
# Construct Theano graph
theta = srng.normal((n_samples, n_nodes, n_nodes)) * sig + mu
y = tsr.maximum(tsr.dot(x, theta), 1e-3)
infect = tsr.log(1. - tsr.exp(-y[0:-1])).dimshuffle(1, 0, 2)
lkl_pos = tsr.sum(infect * (x[1:] & s[1:])) / n_samples
lkl_neg = tsr.sum(-y[0:-1].dimshuffle(1, 0, 2) * (~x[1:] & s[1:])) / n_samples
lkl = lkl_pos + lkl_neg
kl = tsr.sum(tsr.log(sig / sig0) + (sig0**2 + (mu0 - mu)**2)/(2*sig)**2)
res = lkl + kl
gmu, gsig = theano.gradient.grad(lkl, [mu, sig])
gmukl, gsigkl = theano.grad(kl, [mu, sig])
# Compile into functions
loglkl_full = theano.function([x, s], lkl)
train = theano.function(inputs=[x, s], outputs=res,
updates=((mu, tsr.clip(mu + lr * gmu, 0, 1)),
(sig, tsr.clip(sig + lr * gsig, 1e-3, 1))))
train_kl = theano.function(inputs=[], outputs=[],
updates=((mu, tsr.clip(mu + lr * gmukl, 0, 1)),
(sig, tsr.clip(sig + lr * gsigkl, 1e-3, 1))))
###############Maximum Likelihood#####################
x = tsr.matrix(name='x', dtype='int8')
s = tsr.matrix(name='s', dtype='int8')
params = theano.shared(.5 + .01*np.random.normal(size=(n_nodes, n_nodes)),
name='params')
y = tsr.maximum(tsr.dot(x, params), 1e-5)
infect = tsr.log(1. - tsr.exp(-y[0:-1]))
lkl_pos = tsr.sum(infect * (x[1:] & s[1:]))
lkl_neg = tsr.sum(-y[0:-1] * (~x[1:] & s[1:]))
lkl_mle = lkl_pos + lkl_neg
gparams = theano.gradient.grad(lkl_mle, params)
train_mle = theano.function(inputs=[x, s], outputs=lkl_mle, updates=[(params,
tsr.clip(params + lr * gparams, 0, 1))])
if __name__ == "__main__":
graph = .5 * np.random.binomial(2, p=.5, size=(n_nodes, n_nodes))
for k in range(len(graph)):
graph[k, k] = 0
p = 0.5
graph = np.log(1. / (1 - p * graph))
cascades = mn.build_cascade_list(mn.simulate_cascades(n_cascades, graph),
collapse=True)
x_obs, s_obs = cascades[0], cascades[1]
#mle
lkl_plot = []
if 0:
for i in range(n_epochs):
for xt, st in zip(x_obs, s_obs):
lkl = train_mle(xt, st)
lkl_plot.append(lkl)
print(graph)
w = params.get_value()
for k in range(len(w)):
w[k, k] = 0
print(w)
import matplotlib.pyplot as plt
plt.plot(lkl_plot)
plt.show()
#variational inference
if 1:
for i in range(n_epochs):
train_kl()
for k in xrange(len(x_obs)/100):
cost = train(x_obs[k*100:(k+1)*100], s_obs[k*100:(k+1)*100])
print(cost)
print(graph)
print(mu.get_value())
print(sig.get_value())
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