diff options
Diffstat (limited to 'simulation/vi.py')
| -rw-r--r-- | simulation/vi.py | 26 |
1 files changed, 16 insertions, 10 deletions
diff --git a/simulation/vi.py b/simulation/vi.py index 9604c7d..aeccb69 100644 --- a/simulation/vi.py +++ b/simulation/vi.py @@ -2,7 +2,10 @@ import time import main as mn import autograd.numpy as np from autograd import grad +import logging +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s', + level=logging.INFO) def g(m): assert (m > 0).all() @@ -47,29 +50,32 @@ def kl(params1, params0): grad_kl = grad(kl) -def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2): +def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2, n_print=10): g_mu1, g_sig1 = grad_kl((mu1, sig1), (mu0, sig0)) for t in xrange(n_e): lrt = lr(t) # learning rate mu1, sig1 = mu1 + lrt * g_mu1, sig1 + lrt * g_sig1 - for x, s in zip(*cascades): + for step, (x, s) in enumerate(zip(*cascades)): g_mu1, g_sig1 = grad_ll_full((mu1, sig1), x, s) mu1 = np.maximum(mu1 + lrt * g_mu1, 0) sig1 = np.maximum(sig1 + lrt * g_sig1, 1e-3) - res = np.sum(ll_full((mu1, sig1), x, s) for x, s in zip(*cascades)) + \ - kl((mu1, sig1), (mu0, sig0)) - print("Epoch: {}\t LB: {}\t Time: {}".format(t, res, time.time())) - print mu1 - print sig1 + res = np.sum(ll_full((mu1, sig1), x, s) for x, s in zip(*cascades))\ + + kl((mu1, sig1), (mu0, sig0)) + if step % n_print == 0: + logging.info("Epoch:{}\tStep:{}\tLB:{}\t".format(t, step, res)) + print mu1[0:2, 0:2] + print sig1[0:2, 0:2] if __name__ == '__main__': - graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]]) + #graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]]) + graph = np.random.binomial(2, p=.2, size=(10, 10)) p = 0.5 graph = np.log(1. / (1 - p * graph)) - cascades = mn.build_cascade_list(mn.simulate_cascades(1000, graph)) + print(graph[0:2, 0:2]) + cascades = mn.build_cascade_list(mn.simulate_cascades(500, graph)) mu0, sig0 = (1. + .2 * np.random.normal(size=graph.shape), 1 + .2 * np.random.normal(size=graph.shape)) mu1, sig1 = (1. + .2 * np.random.normal(size=graph.shape), 1 + .2 * np.random.normal(size=graph.shape)) - sgd(mu1, sig1, mu0, sig0, cascades, n_e=30) + sgd(mu1, sig1, mu0, sig0, cascades, n_e=30, n_print=1) |