Archive old code

1 files changed, 0 insertions, 118 deletions

diff --git a/experiments/main.py b/experiments/main.py
deleted file mode 100644
index 7556dac..0000000
--- a/experiments/main.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import networkx as nx
-import sys
-from scipy.sparse import dok_matrix
-import numpy as np
-from collections import Counter
-from math import log
-from scipy.optimize import minimize, basinhopping
-
-
-def weight(delta, dist, alpha):
-    r = 1. / ((1. + (delta / alpha)) ** 1.01) * dist # * (0.01 / alpha)
-    return r
-
-
-def construct_matrix(filename):
-    delta_matrix = dok_matrix((8238, 8238), dtype=np.float64)
-    dist_matrix = dok_matrix((8238, 8238), dtype=np.float64)
-    labels = {}
-    idx = 0
-    with open(filename) as fh:
-        fh.readline()
-        for line in fh:
-            values = line.strip().split(",")
-            i, j, dist, t1, t2 = map(int, values[1:])
-            if i not in labels:
-                labels[i] = idx
-                idx += 1
-            if j not in labels:
-                labels[j] = idx
-                idx += 1
-            delta = float(t2 - t1)
-            dist = 0 if dist >= 4 else 0.15 * (0.57 ** (dist - 1))
-            delta_matrix[labels[i], labels[j]] = delta
-            dist_matrix[labels[i], labels[j]] = dist
-    return delta_matrix, dist_matrix
-
-
-def construct_graph(m, threshold=0.5):
-    g = nx.DiGraph(weight=0.)
-    for ((i, j), w) in m.iteritems():
-        g.add_node(i)
-        g.add_node(j)
-        if w >= threshold:
-            g.add_edge(i, j, weight=w)
-    return g
-
-
-def construct_graph2(m, beta):
-    g = nx.DiGraph(weight=0.)
-    for (i, j), w in m.iteritems():
-        g.add_node(i)
-        g.add_node(j)
-    md = m.toarray()
-    am = md.argmax(axis=0)
-    ma = md[am, np.arange(md.shape[1])]
-    threshold = beta / (1. - beta)
-    edges = ma[ma >= threshold]
-    md[am, np.arange(md.shape[1])] = 0
-    for j, i in enumerate(am):
-        if ma[j] >= beta:
-            g.add_edge(i, j)
-    return g, (np.log(edges).sum() + np.log(1 - ma[ma < threshold]).sum()
-               + np.log(1 - md).sum())
-
-
-def log_likelihood(delta_matrix, dist_matrix, alpha=60., beta=1.):
-    m = dok_matrix((8238, 8238), dtype=np.float64)
-    for ((i, j), delta) in delta_matrix.iteritems():
-        m[i, j] = weight(delta, dist_matrix[i, j], alpha)
-    g = construct_graph(m)
-    m = np.array([w for _, w in m.items()])
-    n, e = g.number_of_nodes(), g.number_of_edges()
-    c = nx.number_weakly_connected_components(g)
-    l = (np.sum(np.log(1 - m[m < 0.5])) + np.sum(np.log(m[m >= 0.5])))
-    l = l + c * log(beta)
-    cnt = Counter()
-    print "Nodes:{0}, Edges:{1}, Cascades:{2}, Alpha:{4}, Beta:{5}, "\
-        "LL:{3}".format(n, e, c, l, alpha, beta)
-    for c in nx.weakly_connected_components(g):
-        cnt[len(c)] += 1
-    print cnt.most_common()
-    return l
-
-
-def log_likelihood2((alpha, beta), delta_matrix, dist_matrix):
-    m = dok_matrix((8238, 8238), dtype=np.float64)
-    for ((i, j), delta) in delta_matrix.iteritems():
-        m[i, j] = weight(delta, dist_matrix[i, j], alpha)
-    g, l = construct_graph2(m, beta)
-    n, e = g.number_of_nodes(), g.number_of_edges()
-    c = nx.number_weakly_connected_components(g)
-    l = (l + c * log(beta) + (123506 - c) * log(1 - beta)
-         + log(0.15) * 36186 + log(0.15 * 0.57) * 41711
-         + log(0.15 * 0.57 * 0.57) * 23264)
-    # cnt = Counter()
-    # print "Nodes:{0}, Edges:{1}, Cascades:{2}, Alpha:{4}, Beta:{5}, "\
-    #     "LL:{3}".format(n, e, c, l, alpha, beta)
-    # for c in nx.weakly_connected_components(g):
-    #     cnt[len(c)] += 1
-    # print cnt.most_common()
-    #print beta, alpha, l
-    return -l
-
-
-def accept_test(**kwargs):
-    bounds = [(1, 100), (0.01, 0.5)]
-    x = kwargs["x_new"]
-    l = [bounds[i][0] <= e <= bounds[i][1] for i, e in enumerate(x)]
-    print x, l
-
-
-if __name__ == "__main__":
-    delta, dist = construct_matrix(sys.argv[1])
-    basinhopping(log_likelihood2, [10, 0.1],
-                 minimizer_kwargs={"args": (delta, dist),
-                                   "bounds": [(1, 100), (0.01, 0.5)]},
-                 accept_test=accept_test,
-                 disp=True)