diff options
| -rw-r--r-- | hawkes_experiments/cause.py | 24 | ||||
| -rw-r--r-- | hawkes_experiments/main.py | 34 | ||||
| -rw-r--r-- | hawkes_experiments/refine.py | 6 |
3 files changed, 32 insertions, 32 deletions
diff --git a/hawkes_experiments/cause.py b/hawkes_experiments/cause.py index 800f699..711e34a 100644 --- a/hawkes_experiments/cause.py +++ b/hawkes_experiments/cause.py @@ -30,12 +30,13 @@ def cause(lamb, alpha, mu): fatal_droots += 1 continue background_rate = lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36)) - parents = sorted([(n2, t2, alpha / d * mu * exp(-mu * (t1 - t2)), d) + parents = sorted([(n2, t2, alpha / d ** 2 * mu * exp(-mu * (t1 - t2)), d) for (n2, t2, d) in s], reverse=True, key=lambda x: x[2]) parent_rate = sum(e[2] for e in parents) - # if sum(e[2] for e in prl[:1]) > br: - # G.add_edge((n1, t1), tuple(prl[0][:2])) + # if parents[0][2] > background_rate: + # G.add_edge(tuple(parents[0][:2]), (n1, t1), + # weight=parents[0][3]) if background_rate > parent_rate: roots += 1 if (n1, t1) in fatals: @@ -56,9 +57,9 @@ def analyze_graph(G): print "cascades: {0}, min: {1}, max: {2}, mean: {3}, median: {4}".format( len(csizes), np.min(csizes), np.max(csizes), np.mean(csizes), np.median(csizes)) - # counts = Counter(l) - # w = writer(open("components_dist.csv", "w")) - # w.writerows(counts.most_common()) + counts = Counter(csizes) + w = writer(open("components_dist.csv", "w")) + w.writerows(counts.most_common()) edges = list(G.edges_iter(data=True)) print "edges: {0}".format(len(edges)) times = [e[1][1] - e[0][1] for e in edges] @@ -68,14 +69,15 @@ def analyze_graph(G): print "distances, min: {0}, max: {1}, mean: {2}, median: {3}".format( np.min(distances), np.max(distances), np.mean(distances), np.median(distances)) - # e = writer(open("edges.csv", "w")) - # e.writerows(edges) + e = writer(open("edges.csv", "w")) + e.writerows((e[0][0], e[0][1], e[1][0], e[1][1], e[2]["weight"]) + for e in edges) if __name__ == "__main__": - nodes, edges, events, event_edges = load(open("data-dist1.pickle", "rb")) - lamb, alpha, mu = 1.86602117779e-05, 0.0433473674726, 0.00109325510695 - # lamb, alpha, mu = 1.87717287808e-05, 5.12006113875e+14, 4.20918377797e-20 + nodes, edges, events, event_edges = load(open("data-all.pickle", "rb")) + lamb, alpha, mu = 1.18909761267e-05, 0.00781529533133, 0.00373882477787 + print "mu: {0}, alpha: {1}, beta: {2}".format(lamb, alpha, mu) (droots, roots, infections, fatal_droots, fatal_roots, fatal_infections, G) = cause(lamb, alpha, mu) r = "events: {0}, droots: {1}, roots: {2}, infections: {3}, "\ diff --git a/hawkes_experiments/main.py b/hawkes_experiments/main.py index 8f30dcf..9ed4205 100644 --- a/hawkes_experiments/main.py +++ b/hawkes_experiments/main.py @@ -43,19 +43,18 @@ def approx(x): def ll(lamb, alpha, mu): r1 = sum(log(lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36)) - + sum(alpha / d * mu * exp(-mu * (t1 - t2)) + + sum(alpha / d ** 2* mu * exp(-mu * (t1 - t2)) for (n2, t2, d) in s)) for ((n1, t1), s) in event_edges.iteritems()) - r2 = sum(sum(alpha / d * approx(mu * (nodes[n2][0] - t1)) + r2 = sum(sum(alpha / d ** 2 * approx(mu * (nodes[n2][0] - t1)) for n2, d in edges[n1].iteritems() if nodes[n2][0] > t1) for (n1, t1) in iter_events(events)) r3 = lamb * sum(node[1] for node in nodes.itervalues()) - print r1, r2, r3 return -(r1 - r2 - r3) -def sa(x, y, z, sigma=0.5, niter=1000, fc=None): +def sa(x, y, z, sigma=0.5, niter=70, fc=None): T = 0.1 e = 1.1 if fc: @@ -82,7 +81,7 @@ def sa(x, y, z, sigma=0.5, niter=1000, fc=None): return y, z, fo -def optimize_with_sa(x, y, z, niter=10): +def optimize_with_sa(x, y, z, niter=4): def f(x): return ll(x, y, z) @@ -108,8 +107,8 @@ def optimize_with_gss(x, y, z, niter=5): return ll(x, y, z) for _ in xrange(niter): - y, fc = gss(g, 0, 1e50, tol=1e-10) - z, fc = gss(h, 0, 1e50, tol=1e-10) + y, fc = gss(g, 0, 1, tol=1e-10) + z, fc = gss(h, 0, 1, tol=1e-10) x, fc = gss(f, 0, 1e-3, tol=1e-10) print x, y, z, fc sys.stdout.flush() @@ -121,7 +120,7 @@ def coarse_search(): v = map(float, line.strip().split()) d[tuple(v[:3])] = v[3] p = Pool(5) - lamb = [20. ** i for i in range(-10, 0)] + lamb = [5e-6, 1e-5, 1.5e-5, 2e-5, 2.5e-5] alpha = [20. ** i for i in range(-15, 15)] mu = [20. ** i for i in range(-15, 15)] @@ -136,16 +135,15 @@ def coarse_search(): if __name__ == "__main__": - nodes, edges, events, event_edges = load(open("data-dist1.pickle", "rb")) - x = 1.25e-5 - y = 1.2e16 - z = 1.5e-20 - sa(x, y, z) - - # with open(sys.argv[1]) as fh: - # l = [map(float, line.strip().split()[:3]) for line in fh] - # for e in l: - # optimize_with_gss(*e) + nodes, edges, events, event_edges = load(open("data-all.pickle", "rb")) + x = 1.875e-5 + y = 6.1e14 + z = 3e-20 + # optimize_with_sa(1.25e-05, 2.048e+13, 9.1552734375e-20) + with open(sys.argv[1]) as fh: + l = [map(float, line.strip().split()[:3]) for line in fh] + for e in l: + optimize_with_gss(*e) # print ll(x, y, z) # coarse_search() diff --git a/hawkes_experiments/refine.py b/hawkes_experiments/refine.py index 02bbe07..6acb382 100644 --- a/hawkes_experiments/refine.py +++ b/hawkes_experiments/refine.py @@ -24,10 +24,10 @@ def approx(x): def ll(lamb, alpha, mu): r1 = sum(log(lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36)) - + sum(alpha / d * mu * exp(-mu * (t1 - t2)) + + sum(alpha / d ** 2 * mu * exp(-mu * (t1 - t2)) for (n2, t2, d) in s)) for ((n1, t1), s) in event_edges.iteritems()) - r2 = sum(sum(alpha / d * approx(mu * (nodes[n2][0] - t1)) + r2 = sum(sum(alpha / d ** 2 * approx(mu * (nodes[n2][0] - t1)) for n2, d in edges[n1].iteritems() if nodes[n2][0] > t1) for (n1, t1) in iter_events(events)) @@ -45,7 +45,7 @@ def get_values(): for line in open("refine.txt"): v = map(float, line.strip().split()) d[tuple(v[:3])] = v[3] - for a, _ in l[:100]: + for a, _ in l[:20]: t = [1. / i for i in range(2, 4)] + [float(i) for i in range(1, 4)] for b in product(t, repeat=3): l, al, m = inprod(a, b) |