def __init__(self, regexp):
self.regexp = regexp
self.m = len(regexp)
self.graph = Digraph(self.m + 1)
ops = []
for i in range(0, self.m):
lp = i
if regexp[i] == '(' or regexp[i] == '|':
ops += [i]
elif regexp[i] == ')':
or_ = ops.pop()
if regexp[or_] == '|':
lp = ops.pop()
self.graph.addEdge(lp, or_ + 1)
self.graph.addEdge(or_, i)
elif regexp[or_] == '(':
lp = or_
else:
assert False
if i < self.m - 1 and regexp[i + 1] == '*':
self.graph.addEdge(lp, i + 1)
self.graph.addEdge(i + 1, lp)
if regexp[i] == '(' or regexp[i] == '*' or regexp[i] == ')':
self.graph.addEdge(i, i + 1)
if len(ops) != 0:
raise ValueError("Invalid regular expression")
def read_graph(filename):
with open(filename, 'r') as reader:
g = Digraph(int(reader.readline()))
for line in reader:
(s, d, w) = line.split()
g.insert_arc(int(s), int(d), float(w))
return g
class NFA:
regexp = None
m = 0
def __init__(self, regexp):
self.regexp = regexp
self.m = len(regexp)
self.graph = Digraph(self.m + 1)
ops = []
for i in range(0, self.m):
lp = i
if regexp[i] == '(' or regexp[i] == '|':
ops += [i]
elif regexp[i] == ')':
or_ = ops.pop()
if regexp[or_] == '|':
lp = ops.pop()
self.graph.addEdge(lp, or_ + 1)
self.graph.addEdge(or_, i)
elif regexp[or_] == '(':
lp = or_
else:
assert False
if i < self.m - 1 and regexp[i + 1] == '*':
self.graph.addEdge(lp, i + 1)
self.graph.addEdge(i + 1, lp)
if regexp[i] == '(' or regexp[i] == '*' or regexp[i] == ')':
self.graph.addEdge(i, i + 1)
if len(ops) != 0:
raise ValueError("Invalid regular expression")
def recognizes(self, txt):
dfs = DirectedDFS(self.graph, [0])
pc = []
for v in range(0, self.graph.Vertex()):
if dfs.marked[v]:
pc += [v]
for i in range(0, len(txt)):
if txt[i] == '*' or txt[i] == '|' or txt[i] == '(' or txt[i] == ')':
raise ValueError("text contains the metacharacter %s" %
(txt[i]))
match = []
for v in pc:
if v == self.m:
continue
if self.regexp[v] == txt[i] or self.regexp[v] == '.':
match += [v + 1]
dfs = DirectedDFS(self.graph, match)
pc = []
for v in range(0, self.graph.Vertex()):
if dfs.marked[v]:
pc += [v]
if len(pc) == 0:
return False
for v in pc:
if v == self.m:
return True
return False
def addEdge(self, edge):
"""
Adds an edge to the graph
Requires: edge Edge
Ensures: edge[src] = dest and edge[dest] = src
"""
Digraph.addEdge(self, edge)
rev = Edge(edge.getDestination(), edge.getSource())
Digraph.addEdge(self, rev)
class SymbolDigraph:
def __init__(self, filename, delimiter):
self.st = dict()
self.keys = dict()
delimiter = None
myin = In(filename, delimiter)
while myin.hasNextLine():
a = myin.readLine().split(delimiter)
for i in range(0, len(a)):
if self.st.get(a[i]) == None:
self.st[a[i]] = len(self.st)
for name in self.st.keys():
self.keys[self.st[name]] = name
self.graph = Digraph(len(self.st.keys()))
myin = In(filename, '\n')
while myin.hasNextLine():
a = myin.readLine().split(' ')
v = self.st.get(a[0])
for i in range(1, len(a)):
w = self.st.get(a[i])
self.graph.addEdge(v, w)
def contains(self, s):
if self.st.get(s) == None:
return False
else:
return True
def index(self, s):
return self.st.get(s)
def indexOf(self, s):
return self.get(s)
def name(self, v):
self.validateVertex(v)
return self.keys[v]
def nameOf(self, v):
self.validateVertex(v)
return self.keys[v]
def G(self):
return self.graph
def digraph(self):
return self.graph
def validateVertex(self, v):
Ver = self.graph.Vertex()
if v < 0 or v >= Ver:
raise ("vertex %d is not between 0 and %d " % (v, Ver - 1))
def __init__(self, s):
self._ops = list()
self._re = list(self.__convert(s))
self._M = len(self._re)
self._G = Digraph(self._M + 1)
for i in range(self._M):
lp = i # left parenthesis(bracket, brace), used for closure
# (), |
if self._re[i] == '(' or self._re[i] == '|':
self._ops.append(i)
elif self._re[i] == ')':
or_pos = list()
_or = self._ops.pop()
while self._re[_or] == '|':
or_pos.append(_or)
_or = self._ops.pop()
lp = _or # left parenthesis
for pos in or_pos:
self._G.add_edge(lp, pos + 1)
self._G.add_edge(pos, i)
# meta characters, support only convert meta character
# \, ., |, *, (, ), +, [, ], {, }
if i < self._M - 1 and self._re[i] == '\\':
escape = '\\.*+?|()[]{}' # '\\.|*()+[]{}'
if escape.find(self._re[i + 1]):
self._G.add_edge(i, i + 1)
else:
print("please don't use only one \\ "
"or \\(special character) like \\s,"
" which is not finish")
# closure, and look forward to check
# * closure, zero or more recognizes
if i < self._M - 1 and self._re[i + 1] == '*':
self._G.add_edge(lp, i + 1)
self._G.add_edge(i + 1, lp)
# + closure, one or more recognizes
if i < self._M - 1 and self._re[i + 1] == '+':
self._G.add_edge(i + 1, lp)
# ? closure, zero or one recognizes
if i < self._M - 1 and self._re[i + 1] == '?':
self._G.add_edge(lp, i + 1)
# keep moving
if self._re[i] == '(' or \
self._re[i] == '*' or \
self._re[i] == ')' or \
self._re[i] == '+' or \
self._re[i] == '?':
self._G.add_edge(i, i + 1)
def test_Digraph(self):
gr = Digraph(5)
gram = Digraph_AM(5)
for item in self.G:
gr.addEdge(item[0], item[1])
gram.addEdge(item[0], item[1])
self.assertTrue(gr.V == gram.V)
self.assertTrue(gr.E == gram.E)
a = gr.adjlist(1)
b = gram.adjlist(1)
self.assertTrue(a == b)
def main():
filename = raw_input('input the graph path: ')
G = Digraph(filename)
print 'The adjacent table of the input Graph is: \n'
print G
print ''
#Start_point = raw_input('input the start point: ')
DFS = Depth_First_Order(G)
print '\nThe order used for the SCC search of orininal G is:: '
print DFS.topo_list()
print '\nThe toplocical order the the graph is: '
DFS.show_topo_order()
print '----------------the reversed G------------------'
print G.reverse()
DFS = Depth_First_Order(G.reverse())
print '\nThe order used for the SCC of reversed G is: '
print DFS.topo_list()
print '\nThe toplocical order the the reversed graph is: '
DFS.show_topo_order()
def __init__(self, filename, delimiter):
self.st = dict()
self.keys = dict()
delimiter = None
myin = In(filename, delimiter)
while myin.hasNextLine():
a = myin.readLine().split(delimiter)
for i in range(0, len(a)):
if self.st.get(a[i]) == None:
self.st[a[i]] = len(self.st)
for name in self.st.keys():
self.keys[self.st[name]] = name
self.graph = Digraph(len(self.st.keys()))
myin = In(filename, '\n')
while myin.hasNextLine():
a = myin.readLine().split(' ')
v = self.st.get(a[0])
for i in range(1, len(a)):
w = self.st.get(a[i])
self.graph.addEdge(v, w)
return self.count
def processVertexEarly(self, s):
#print "Discovered vertex ", s
return
def processVertexLate(self, s):
return
def processEdge(self,s, n):
#print "Processed Edge ", s, " -> ", n
return
if __name__ == '__main__':
inputFile = sys.argv[1]
sourceId = int(sys.argv[2])
g = Digraph(inputFile)
d = DirectedDFS(g, sourceId)
for vid in g.getVertexIds():
print sourceId, "to", vid, ":",
print d.pathTo(vid)
print ""
# if d.getCount() == g.getNoOfVertices():
# print "connected"
# else:
# print "NOT connected"
def openTwoPathOpenTimes(numNodes, transaction_list, verbose):
"""Given a list of transactions which are in the form of
a list of (sender, receiver, timestamp) tuples, return the list
of times at open two-paths are created.
Parameters:
numNodes - number of actors (nodes)
transaction_list - list of (sender, receiver, timestamp) tuples.
Sender and receiver are intergers in 0..numNodes-1
and timestamps are numeric values.
The list must be ordered by timestamp ascending.
verbose - if True write debug output to stdout
Return value:
dict { (i, j, k) : t } where (i, j, k) is an open
directed two-path (note this means it is not part of a
transitive triad i.e. i -> k is not present, but it may be
part of a cyclic triad i.e. k -> i may be present), and t is
the time the two-path was created (open). BUT only if the
second arc in two-path has higher timestamp than the first
(i.e. we don't count a two-path that goes backward in time
along the path)
"""
G = Digraph(numNodes)
lastTime = None
pathdict = {} # dict mapping (i,j,k) two-path tuple to open time
for trans in transaction_list:
assert (trans[TSENDER] >= 0 and trans[TSENDER] < numNodes)
assert (trans[TRECEIVER] >= 0 and trans[TRECEIVER] < numNodes)
assert (lastTime is None or trans[TTIME] >= lastTime)
if verbose:
print trans[TSENDER], '->', trans[TRECEIVER], ' at time ', trans[
TTIME],
(ulist, vlist) = openTwoPaths(G, trans[TSENDER], trans[TRECEIVER])
i = trans[TSENDER]
j = trans[TRECEIVER]
if len(ulist) > 0 or len(vlist) > 0:
if verbose:
print 'opened', len(ulist) + len(vlist), 'two-paths'
for u in ulist: # u -> i -> j
path_1st_time = G.G[u][i]
path_2nd_time = trans[TTIME] # will be G.G[i][j] when inserted
if path_2nd_time > path_1st_time:
if verbose:
print ' path from', u, 'is forward in time (', path_1st_time, ',', path_2nd_time, '), including'
if not pathdict.has_key((u, i, j)):
pathdict[(u, i, j)] = path_2nd_time
else:
if verbose:
print ' two-path ', u, i, j, 'already present from time', pathdict[
(u, i, j)], ' not updating'
else:
if verbose:
print ' path from', u, 'is backwards in time (', path_1st_time, ',', path_2nd_time, '), skipping'
if len(vlist) > 0:
if verbose:
print ' ', len(vlist), ' are backward in time, skipping'
for v in vlist: # i -> j -> v
path_1st_time = trans[TTIME] # will be G.G[i][j] when inserted
path_2nd_time = G.G[j][v]
# as the transactions are ordered in time we cannot
# have this a two-path ordered in time, as the 2nd is older
assert (path_1st_time > path_2nd_time)
else:
if verbose:
print
# now check if the new arc i -> j would close any currently open
# two-paths. If so, remove those from the dictionary of open
# two paths.
for v in closedTwoPaths(G, i, j):
# For each v, i -> v -> j is now a two-path closed by i -> j
if verbose:
print ' removing ', i, v, j, ' as it is now a transitive triad'
# note (i, v, j) might exist in pathdict as it was an open two-path
# but NOT NECESSARILY as it might have been ignored as backward in time
if pathdict.has_key((i, v, j)):
pathdict.pop((i, v, j))
else:
if verbose:
print ' (did not exist in dict)'
# add this new arc i -> j to the graph
# note there is a potential inconsistency here in that this
# arc might already exist in which case we update the time with
# the new (later) time, however in the pathdict dictionary
# we do not update times of open two-paths but keep the first
# opening time. Need to decide which one really is correct.
G.insertArc(trans[TSENDER], trans[TRECEIVER], trans[TTIME])
lastTime = trans[TTIME]
return pathdict
'''WinPython 3.4.4 64-bit'''
import time
from Digraph import Digraph
# read file: 14.1 seconds
start = time.time()
digraph = Digraph()
with open("SCC.txt", "rt") as f:
for line in f:
tail, head = [int(value) for value in line.split()]
digraph.add_edge(tail, head)
finish = time.time()
elapsed = 1.0 * (finish - start)
print("Time to read file: %.3f" % (elapsed,))
print("Number of graph vertices: %d" % (digraph.num_nodes(),))
print()
# calculate scc sizes: 9.100 seconds
# answer:
start = time.time()
scc_sizes = digraph.scc_sizes()
finish = time.time()
elapsed = 1.0 * (finish - start)
print("Calculation time: %.3f" % (elapsed,))
# print out answer
print("Component sizes: ", end="")
for i in range(5):
print("%d " % (scc_sizes[i],), end="")
print()
def addEdge(self, edge): Digraph.addEdge(self, edge) rev = Edge(edge.getDestination(), edge.getSource()) Digraph.addEdge(self, rev)
self.edgeTo[w] = v
def hasPathTo(self, v):
return self.marked[v]
def pathTo(self, v):
if (not self.hasPathTo(v)):
return None
else:
path = [v]
x = v
while not (x == self.s):
path.append(self.edgeTo[x])
x = self.edgeTo[x]
# path.append(v)
return list(reversed(path))
if __name__ == '__main__':
from Digraph import Digraph
from In import In
import sys
in_ = In(sys.argv[1])
G = Digraph(fileobject=in_)
bfp = BreadthFirstPaths(G, 0)
print(bfp.edgeTo)
print(bfp.marked)
print(bfp.pathTo(3))
print(bfp.distTo)
class NFA:
"""This class provides a data type for creating a
non-deterministic finite state automaton(NFA) from a regular expression
and testing whether a given string is matched by that regular expression.
"""
def __init__(self, s):
self._ops = list()
self._re = list(self.__convert(s))
self._M = len(self._re)
self._G = Digraph(self._M + 1)
for i in range(self._M):
lp = i # left parenthesis(bracket, brace), used for closure
# (), |
if self._re[i] == '(' or self._re[i] == '|':
self._ops.append(i)
elif self._re[i] == ')':
or_pos = list()
_or = self._ops.pop()
while self._re[_or] == '|':
or_pos.append(_or)
_or = self._ops.pop()
lp = _or # left parenthesis
for pos in or_pos:
self._G.add_edge(lp, pos + 1)
self._G.add_edge(pos, i)
# meta characters, support only convert meta character
# \, ., |, *, (, ), +, [, ], {, }
if i < self._M - 1 and self._re[i] == '\\':
escape = '\\.*+?|()[]{}' # '\\.|*()+[]{}'
if escape.find(self._re[i + 1]):
self._G.add_edge(i, i + 1)
else:
print("please don't use only one \\ "
"or \\(special character) like \\s,"
" which is not finish")
# closure, and look forward to check
# * closure, zero or more recognizes
if i < self._M - 1 and self._re[i + 1] == '*':
self._G.add_edge(lp, i + 1)
self._G.add_edge(i + 1, lp)
# + closure, one or more recognizes
if i < self._M - 1 and self._re[i + 1] == '+':
self._G.add_edge(i + 1, lp)
# ? closure, zero or one recognizes
if i < self._M - 1 and self._re[i + 1] == '?':
self._G.add_edge(lp, i + 1)
# keep moving
if self._re[i] == '(' or \
self._re[i] == '*' or \
self._re[i] == ')' or \
self._re[i] == '+' or \
self._re[i] == '?':
self._G.add_edge(i, i + 1)
def recognizes(self, txt):
pc = [0]
# 0 is source, the state in start
dfs = DirectedDFS(self._G, pc)
pc.clear()
# initialize the states collection, which the first state can arrived
for v in range(self._G.V):
if dfs.marked(v):
pc.append(v)
# calculate all of NFA states that txt[i+1] can arrived
for i in range(len(txt)):
recognizes = list()
# calculate arrived states after recognizes
for v in pc:
if v < self._M:
if self._re[v] == txt[i] or self._re[v] == '.':
recognizes.append(v + 1)
pc.clear()
# calculate states, which epsilon transform can arrived after recognizes
dfs = DirectedDFS(self._G, recognizes)
for v in range(self._G.V):
if dfs.marked(v):
pc.append(v)
for v in pc:
if v == self._M:
return True
return False
def __convert(self, s):
"""using convert to straight implement some pattern
like using (A|B|C) to implement [ABC]
and AAAA* to A{3,}
"""
seq = deque()
i = 0
length = len(s)
while i < length:
if s[i] == '\\':
seq.append(s[i]) # add '\'
seq.append(s[i + 1]) # add the character to convert
i += 1
elif s[i] == '[': # [ABC] -> (A|B|C)
seq.append('(')
i += 1
while s[i] != ']':
seq.append(s[i])
seq.append('|')
i += 1
seq.pop()
seq.append(')')
elif s[i] == '{': # A{3}->AAA, A{3,5}->AAAA?A?, A{3,}->AAAA*
in_brace = ''
num1, num2 = 0, 0
multiple, _range, more = False, False, False # {3},{3,5},{3,}
# get content in brace
i += 1
while s[i] != '}':
in_brace += s[i]
i += 1
# get the type of range
if ',' in in_brace:
nums = in_brace.split(',')
num1 = int(nums[0])
if nums[1] == '':
more = True
elif nums[1] != '':
_range = True
num2 = int(nums[1])
else:
multiple = True
num1 = int(in_brace)
# get the basic unit used for multiple
unit = list()
if seq[-1] == ')':
unit.append(seq.pop()) # add ')'
if seq[-1] == '\\':
unit.append(seq.pop()) # add '\'
else:
lp_count = 0
rp_count = 1
while lp_count != rp_count:
if seq[-1] == ')':
rp_count += 1
elif seq[-1] == '(':
lp_count += 1
unit.append(seq.pop())
else:
unit.append(seq.pop())
# add multiple unit to seq
def seq_add_unit(_seq, _unit):
for k in range(len(_unit) - 1, -1, -1):
_seq.append(_unit[k])
while num1 > 0:
seq_add_unit(seq, unit)
num1 -= 1
if multiple:
pass # no-statement
elif _range:
times = num2 - num1
while times > 0:
seq_add_unit(seq, unit)
seq.append('?')
times -= 1
elif more:
seq_add_unit(seq, unit)
seq.append('*')
else:
seq.append(s[i])
i += 1
# generator result
result = ''
for ch in seq:
result += ch
return result
def run_on_network_attr(edgelist_filename, param_func_list, labels,
outcome_bin_filename,
binattr_filename=None,
contattr_filename=None,
catattr_filename=None,
EEiterations = 50000,
run = None,
learningRate = 0.01,
sampler_func = basicALAAMsampler,
zone_filename= None,
directed = False):
"""Run on specified network with binary and/or continuous
and categorical attributes.
Parameters:
edgelist_filename - filename of Pajek format edgelist
param_func_list - list of change statistic functions corresponding
to parameters to estimate
labels - list of strings corresponding to param_func_list
to label output (header line)
outcome_bin_filename - filename of binary attribute (node per line)
of outcome variable for ALAAM
binattr_filename - filename of binary attributes (node per line)
Default None, in which case no binary attr.
contattr_filename - filename of continuous attributes (node per line)
Default None, in which case no continuous attr.
catattr_filename - filename of categorical attributes (node per line)
Default None, in which case no categorical attr.
EEiterations - Number of iterations of the EE algorithm.
Default 50000.
run - run number for parallel runs, used as suffix on
output filenames. Default None
in which case no suffix added to output files.
learningRate - learning rate (step size multiplier, a)
defult 0.01
sampler_func - ALAAM sampler function with signature
(G, A, changestats_func_list, theta, performMove,
sampler_m); see basicALAAMsampler.py
default basicALAAMsampler
zone_filename - filename of snowball sampling zone file
(header line 'zone' then zone number for nodes,
one per line)
Default None, in which case no snowball zones.
If not None then the sampler_func should take
account of snowball sample zones i.e.
conditionalALAAMsampler()
directed - Default False.
True for directed network else undirected.
Write output to ifd_theta_values_<basename>_<run>.txt and
ifd_dzA_values_<basename>_<run>.txt
where <basename> is the baesname of edgelist filename e..g
if edgelist_filename is edges.txt then ifd_theta_values_edges_0.txt
and ifd_dzA_values_edges_0.txt etc.
WARNING: these files are overwritten.
"""
assert(len(param_func_list) == len(labels))
basename = os.path.splitext(os.path.basename(edgelist_filename))[0]
THETA_OUTFILENAME = THETA_PREFIX + basename
DZA_OUTFILENAME = DZA_PREFIX + basename
if run is not None:
THETA_OUTFILENAME += '_' + str(run)
DZA_OUTFILENAME += '_' + str(run)
THETA_OUTFILENAME += os.extsep + 'txt'
DZA_OUTFILENAME += os.extsep + 'txt'
if directed:
G = Digraph(edgelist_filename, binattr_filename, contattr_filename,
catattr_filename, zone_filename)
else:
G = Graph(edgelist_filename, binattr_filename, contattr_filename,
catattr_filename, zone_filename)
G.printSummary()
outcome_binvar = list(map(int_or_na, open(outcome_bin_filename).read().split()[1:]))
assert(len(outcome_binvar) == G.numNodes())
A = outcome_binvar
print('positive outcome attribute = ', (float(A.count(1))/len(A))*100.0, '%')
assert( all([x in [0,1,NA_VALUE] for x in A]) )
if NA_VALUE in A:
print('Warning: outcome variable has', A.count(NA_VALUE), 'NA values')
A = np.array(A) # convert list to numpy vector
# steps of Alg 1
M1 = 100
#OLD: Mouter = 500 # outer iterations of Algorithm EE
#OLD: Msteps = 100 # multiplier for number of inner steps of Algorithm EE
#OLD: print 'M1 = ', M1, ' Mouter = ', Mouter, ' Msteps = ', Msteps
print('M1 = ', M1, ' EEiterations = ', EEiterations, end=' ')
print('learningRate = ', learningRate, end=' ')
theta_outfile = open(THETA_OUTFILENAME, 'w',1) # 1 means line buffering
theta_outfile.write('t ' + ' '.join(labels) + ' ' + 'AcceptanceRate' + '\n')
print('Running Algorithm S...', end=' ')
start = time.time()
(theta, Dmean) = algorithm_S(G, A, param_func_list, M1, theta_outfile,
sampler_func)
print(time.time() - start, 's')
print('after Algorithm S:')
print('theta = ', theta)
print('Dmean = ', Dmean)
dzA_outfile = open(DZA_OUTFILENAME, 'w',1)
dzA_outfile.write('t ' + ' '.join(labels) + '\n')
print('Running Algorithm EE...', end=' ')
start = time.time()
#OLD: theta = algorithm_EE(G, A, param_func_list, theta, Dmean,
#OLD: Mouter, Msteps, theta_outfile, dzA_outfile)
theta = algorithm_EE(G, A, param_func_list, theta,
EEiterations, theta_outfile, dzA_outfile, learningRate,
sampler_func)
print(time.time() - start, 's')
theta_outfile.close()
dzA_outfile.close()
print('at end theta = ', theta)
from DepthFirstOrder import DepthFirstOrder from DigraphCycle import DirectedCycle class Topological: def __init__(self,digraph): self._order = None cycleFinder = DirectedCycle(digraph) if not cycleFinder.hasCycle(): dfs = DepthFirstOrder(digraph) self._order = dfs.reversePost #Directed Acyclical Graph: A graph with no cycles def isDAG(self): return self._order is not None def order(self): return self._order j = Digraph(6) j.addEdge(1,2) j.addEdge(1,3) j.addEdge(0,2) j.addEdge(5,1) j.addEdge(3,4) j.addEdge(0,4) z = Topological(j) print(z.isDAG())
def dfs(self, G, v):
self._marked[v] = True
for w in G.adj(v):
if not self._marked[w]:
self.dfs(G, w)
def marked(self, v):
return self._marked[v]
# unittest
if __name__ == '__main__':
with open('tinyDG.txt', 'r') as g:
V = int(g.readline().split()[0])
E = int(g.readline().split()[0])
DG = Digraph(V)
for e in range(E):
v, w = g.readline().split()
DG.add_edge(v, w)
# sources: 1, 2, 6
# reachable: 0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12 # don't have 7
sources = [1, 2, 6]
reachable = DirectedDFS(DG, sources)
for i in range(V):
if i == 7:
assert reachable.marked(i) is False
else:
assert reachable.marked(i) is True
print(s)
if self.marked[s] is False:
self._dfs(digraph,s)
self.count += 1
def _dfs(self,digraph,v):
self.marked[v] = True
self.id[v] = self.count
for w in digraph.adj(v):
if self.marked[w] is False:
self._dfs(digraph,w)
def stronglyConnected(self,v,w):
print("id of V:",self.id[v])
print("id of W:",self.id[w])
return self.id[v] == self.id[w]
j = Digraph(6)
j.addEdge(1,2)
j.addEdge(1,3)
j.addEdge(0,2)
j.addEdge(5,1)
j.addEdge(3,4)
j.addEdge(0,4)
z = KosarajuSCC(j)
print(z.stronglyConnected(3,4))
for x in z.id:
print(x)
def run_on_network_attr(edgelist_filename,
param_func_list,
labels,
binattr_filename=None,
catattr_filename=None):
"""
Run on specified network with binary and/or categorical attributes.
Parameters:
edgelist_filename - filename of Pajek format edgelist
param_func_list - list of change statistic functions corresponding
to parameters to estimate
labels - list of strings corresponding to param_func_list
to label output (header line)
binattr_filename - filename of binary attributes (node per line)
Default None, in which case no binary attr.
catattr_filename - filename of categorical attributes (node per line)
Default None, in which case no categorical attr.
Write output to ifd_theta_values_<basename>.txt and
ifd_dzA_values_<basename>.txt
where <basename> is the baesname of edgelist filename e..g
if edgelist_filename is edges.txt then ifd_theta_values_edges.txt
and ifd_dzA_values_edges.txt
WARNING: these files are overwritten.
"""
assert (len(param_func_list) == len(labels))
basename = os.path.splitext(os.path.basename(edgelist_filename))[0]
THETA_OUTFILENAME = THETA_PREFIX + basename + os.extsep + 'txt'
DZA_OUTFILENAME = DZA_PREFIX + basename + os.extsep + 'txt'
G = Digraph(edgelist_filename, binattr_filename, catattr_filename)
M1_steps = 500
# steps of Alg 1
M1 = int(M1_steps * G.density() * (1 - G.density()) * G.numNodes()**2 /
sampler_m)
Mouter = 500 # outer iterations of Algorithm EE
Msteps = 100 # multiplier for number of inner steps of Algorithm EE
# inner steps of EE
M = int(Msteps * G.density() * (1 - G.density()) * G.numNodes()**2 /
sampler_m)
print('M1 = ', M1, ' Mouter = ', Mouter, ' M = ', M)
theta_outfile = open(THETA_OUTFILENAME, 'w', 1) # 1 means line buffering
theta_outfile.write('t ' + ' '.join(labels) + ' ' + 'AcceptanceRate' +
'\n')
print('Running Algorithm S...', end=' ')
start = time.time()
(theta, Dmean) = algorithm_S(G, param_func_list, M1, theta_outfile)
print(time.time() - start, 's')
print('after Algorithm S:')
print('theta = ', theta)
print('Dmean = ', Dmean)
dzA_outfile = open(DZA_OUTFILENAME, 'w', 1)
dzA_outfile.write('t ' + ' '.join(labels) + '\n')
print('Running Algorithm EE...', end=' ')
start = time.time()
theta = algorithm_EE(G, param_func_list, theta, Dmean, Mouter, M,
theta_outfile, dzA_outfile)
print(time.time() - start, 's')
theta_outfile.close()
dzA_outfile.close()
print('at end theta = ', theta)
def run_on_network_attr(edgelist_filename, param_func_list, labels,
outcome_bin_filename,
binattr_filename=None,
contattr_filename=None,
catattr_filename=None,
sampler_func = basicALAAMsampler,
zone_filename = None,
directed = False):
"""Run on specified network with binary and/or continuous and
categorical attributes.
Parameters:
edgelist_filename - filename of Pajek format edgelist
param_func_list - list of change statistic functions corresponding
to parameters to estimate
labels - list of strings corresponding to param_func_list
to label output (header line)
outcome_bin_filename - filename of binary attribute (node per line)
of outcome variable for ALAAM
binattr_filename - filename of binary attributes (node per line)
Default None, in which case no binary attr.
contattr_filename - filename of continuous attributes (node per line)
Default None, in which case no continuous attr.
catattr_filename - filename of continuous attributes (node per line)
Default None, in which case no categorical attr.
sampler_func - ALAAM sampler function with signature
(G, A, changestats_func_list, theta, performMove,
sampler_m); see basicALAAMsampler.py
default basicALAAMsampler
zone_filename - filename of snowball sampling zone file
(header line 'zone' then zone number for nodes,
one per line)
Default None, in which case no snowball zones.
If not None then the sampler_func should take
account of snowball sample zones i.e.
conditionalALAAMsampler()
directed - Default False.
True for directed network else undirected.
Write output to stdout.
"""
assert(len(param_func_list) == len(labels))
if directed:
G = Digraph(edgelist_filename, binattr_filename, contattr_filename,
catattr_filename, zone_filename)
else:
G = Graph(edgelist_filename, binattr_filename, contattr_filename,
catattr_filename, zone_filename)
G.printSummary()
outcome_binvar = list(map(int_or_na, open(outcome_bin_filename).read().split()[1:]))
assert(len(outcome_binvar) == G.numNodes())
A = outcome_binvar
assert( all([x in [0,1,NA_VALUE] for x in A]) )
print('positive outcome attribute = ', (float(A.count(1))/len(A))*100.0, '%')
if NA_VALUE in A:
print('Warning: outcome variable has', A.count(NA_VALUE), 'NA values')
# Calculate observed statistics by summing change stats for each 1 variable
Zobs = computeObservedStatistics(G, A, param_func_list)
print('Zobs = ', Zobs)
theta = np.zeros(len(param_func_list))
estimation_start = time.time()
max_runs = 20
i = 0
converged = False
while i < max_runs and not converged:
i += 1
print('Running stochastic approximation (run', i,' of at most',max_runs,')...')
start = time.time()
(theta, std_error, t_ratio) = stochasticApproximation(G, A,
param_func_list,
theta, Zobs,
sampler_func)
print('Stochastic approximation took',time.time() - start, 's')
if theta is None:
print('Failed.')
break
print(' ',labels)
print('theta =', theta)
print('std_error =', std_error)
print('t_ratio =', t_ratio)
converged = np.all(np.abs(t_ratio) < 0.1)
print('Total estimation time (',i,'runs) was',time.time() - estimation_start, 's')
if converged:
print('Converged.')
significant = np.abs(theta) > 2 * std_error
sys.stdout.write(20*' ' + ' Parameter Std.Error t-ratio\n')
for j in range(len(theta)):
sys.stdout.write('%20.20s % 6.3f % 6.3f % 6.3f %c\n' % (labels[j], theta[j], std_error[j], t_ratio[j], ('*' if significant[j] else ' ')))
print()
# Do goodness-of-fit test
# change stats functions to add to GoF if not already in estimation
if directed:
# TODO GoF statistics for directed
gof_param_func_list = list(param_func_list)
goflabels = list(labels)
else:
statfuncs = [changeTwoStar, changeThreeStar, changePartnerActivityTwoPath,
changeTriangleT1, changeContagion,
changeIndirectPartnerAttribute,
changePartnerAttributeActivity,
changePartnerPartnerAttribute,
changeTriangleT2,
changeTriangleT3]
statlabels = ['Two-Star', 'Three-Star', 'Alter-2Star1A',
'T1', 'Contagion', 'Alter-2Star2A', 'Partner-Activity',
'Partner-Resource','T2', 'T3']
gof_param_func_list = (list(param_func_list) +
[f for f in statfuncs
if f not in param_func_list])
goflabels = (list(labels) + [f for f in statlabels
if f not in labels])
n = len(gof_param_func_list)
assert len(goflabels) == n
# pad theta vector with zeros for the added parameters
gof_theta = np.array(list(theta) + (n-len(theta))*[0])
Ainitial = None # default: use random intialization
if zone_filename is not None: # conditional estimation
# For snowball conditional estimation, we must not start with
# random initial outcome vector, but rather make sure the
# nodes in the outermost zone have the same outcome attributes
# as the obseved vector
Ainitial = np.copy(A) # copy of observed vector
# make vector of 50% ones, size of number of inner nodes
Arandom_inner = rand_bin_array(int(0.5*len(G.inner_nodes)), len(G.inner_nodes))
# set the outcome for inner nodes to random values, leaving
# value of outermost nodes at the original observed values
Ainitial[G.inner_nodes] = Arandom_inner
print('Running goodness-of-fit test...')
start = time.time()
gofresult = gof(G, A, gof_param_func_list, gof_theta,
sampler_func = sampler_func, Ainitial = Ainitial)
print('GoF took',time.time() - start, 's')
print(' ',goflabels)
print('t_ratios = ',gofresult)
sys.stdout.write(20*' ' + ' t-ratio\n')
for j in range(n):
sys.stdout.write('%20.20s % 6.3f\n' % (goflabels[j], gofresult[j]))
print()
def lastTransitiveClosureTime(numNodes, transaction_list, verbose):
"""
Given a list of transactions which are in the form of
a list of (sender, receiver, timestamp) tuples, return the list
of times at which open two-paths are closed.
Parameters:
numNodes - number of actors (nodes)
transaction_list - list of (sender, receiver, timestamp) tuples.
Sender and receiver are intergers in 0..numNodes-1
and timestamps are numeric values.
The list must be ordered by timestamp ascending.
verbose - if True write debug output to stdout
Return value:
List of tuples (open_time, delta_time)
where open_time is second timestamp in open two-path and
and delta_time is it took open two-paths to be closed (the difference
in timestamp between the closing transaction (arc) and the second
(along arc) timestamp in the open two-path, BUT only if the second
arc in two-path has higher timestamp than the first (i.e. we
don't count a two-path that goes backward in time along the path)
"""
G = Digraph(numNodes)
lastTime = None
delta_time_list = []
for trans in transaction_list:
assert (trans[TSENDER] >= 0 and trans[TSENDER] < numNodes)
assert (trans[TRECEIVER] >= 0 and trans[TRECEIVER] < numNodes)
assert (lastTime is None or trans[TTIME] >= lastTime)
if verbose:
print trans[TSENDER], '->', trans[TRECEIVER], ' at time ', trans[
TTIME],
closed_2paths_v = closedTwoPaths(G, trans[TSENDER], trans[TRECEIVER])
if len(closed_2paths_v) > 0:
if verbose:
print 'closed', len(
closed_2paths_v), 'two-paths via', closed_2paths_v
path_2nd_time_list = []
for v in closed_2paths_v:
path_1st_time = G.G[trans[TSENDER]][v]
path_2nd_time = G.G[v][trans[TRECEIVER]]
if path_2nd_time > path_1st_time:
if verbose:
print ' path via', v, 'is forward in time (', path_1st_time, ',', path_2nd_time, '), considering'
path_2nd_time_list.append(path_2nd_time)
else:
if verbose:
print ' path via', v, 'is backwards in time (', path_1st_time, ',', path_2nd_time, '), skipping'
if len(path_2nd_time_list) > 0:
path_2nd_time_max = max(path_2nd_time_list)
delta_time = trans[TTIME] - path_2nd_time_max
if verbose:
print ' ', len(
path_2nd_time_list
), 'paths considered as forward in time, max 2nd time is', path_2nd_time_max, ' appending delta_time =', delta_time
delta_time_list.append((path_2nd_time_max, delta_time))
else:
if verbose:
print ' (no paths forward in time)'
else:
if verbose:
print
if not G.isArc(trans[TSENDER], trans[TRECEIVER]):
# only insert arc if not already one there, to keep first
# time on transactions, not subsequent times.
G.insertArc(trans[TSENDER], trans[TRECEIVER], trans[TTIME])
lastTime = trans[TTIME]
return delta_time_list
def vid(self, v):
self.validateVertex(v)
return self.id[v]
def check(self, G):
pass
def validateVertex(self, v):
V = len(self.marked)
if v < 0 or v >= V:
raise ("vertex %d is not between 0 and %d" % (v, V - 1))
if __name__ == '__main__':
myin = In(sys.argv[1], None)
G = Digraph(myin)
scc = KosarajuSharirSCC(G)
m = scc.mcount()
print("%d strong components" % m)
components = [None] * m
for i in range(0, m):
components[i] = []
for v in range(0, G.Vertex()):
components[scc.vid(v)].append(v)
s = ""
for i in range(0, m):
for v in components[i]:
s += "%d " % v
s += '\n'
self.cycle = []
x = v
while x != w:
self.cycle.append(x)
x = self.edgeTo[x]
self.cycle.append(w)
self.cycle.append(v)
self.onStack[v] = False
def hasCycle(self):
return self.cycle != []
def cycle_l(self):
return self.cycle
if __name__ == '__main__':
myin = In(sys.argv[1])
G = Digraph(myin)
finder = DirectedCycle(G)
s = ""
if finder.hasCycle():
print("Directed cycle: ")
for v in finder.cycle_l():
s += "%d " % v
print(s)
else:
print("No directed cycle")
print("\n")
