diff options
Diffstat (limited to 'simulation')
| -rw-r--r-- | simulation/vi.py | 38 |
1 files changed, 23 insertions, 15 deletions
diff --git a/simulation/vi.py b/simulation/vi.py index 3ba6b96..9604c7d 100644 --- a/simulation/vi.py +++ b/simulation/vi.py @@ -5,6 +5,7 @@ from autograd import grad def g(m): + assert (m > 0).all() return np.log(1 - np.exp(-m)) @@ -18,13 +19,16 @@ def ll(x, s, theta): s : susceptible """ res = 0 - for t in range(1, x.shape[1] + 1): + for t in range(1, x.shape[0]): w = np.dot(x[t-1], theta) res += g(w)[x[t]].sum() + h(w)[~x[t] & s[t]].sum() + return res def sample(params): - return np.random.normal(*params) + mu, v = params + size = mu.shape + return np.maximum(np.random.normal(size=size) * v + mu, 1e-3) def ll_full(params, x, s, nsamples=50): @@ -43,25 +47,29 @@ def kl(params1, params0): grad_kl = grad(kl) -def sgd(mu1, sig1, mu0, sig0, cascades, n_e, lr=lambda t: 1e-3): +def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2): 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 cascades: + for x, s in zip(*cascades): g_mu1, g_sig1 = grad_ll_full((mu1, sig1), x, s) - mu1, sig1 = mu1 + lrt * g_mu1, sig1 + lrt * g_sig1 - res = np.sum(ll_full((mu1, sig1), x, s) for x, s in cascades) +\ - kl((mu1, sig1), (mu0, sig0)) + 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 -if __name__=='__main__': - g = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]]) +if __name__ == '__main__': + graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]]) p = 0.5 - g = np.log(1. / (1 - p * g)) - cascades = mn.build_cascade_list(mn.simulate_cascades(100, g)) - params0 = (1. + .2 * np.random.normal(len(g)), \ - 1 + .2 * np.random.normal(len(g))) - params1 = (1. + .2 * np.random.normal(len(g)), \ - 1 + .2 * np.random.normal(len(g))) + graph = np.log(1. / (1 - p * graph)) + cascades = mn.build_cascade_list(mn.simulate_cascades(1000, 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) |