def main():
n = 47 # test...
for i in range(20):
G = GNR(127,3)
numRemove = n - i
print("Removing " + str(numRemove) + " vertices")
G.removeNodes(numRemove)
r = reducer()
numVars, cnf = r.reduce(G.graph)
outFile = open('g127_' + str(numRemove) + '.cnf', 'w')
def __init__(self, n, r, sample, fix, na, smart, saturate, ne, nrr, nerr, nisr, out, dump):
self.n = n
self.r = r
self.sample = sample
self.graph = GNR(n, r)
self.fix = fix
self.na = na
self.smart = smart
self.saturate = saturate
self.ne = ne
self.nrr = nrr
self.nerr = nerr
self.nisr = nisr
self.out = out
self.dump = dump
# Preliminary error checking
if self.graph.getGraph().edges() < self.na:
raise Exception("Cannot remove more edges than the graph contains.")
if n < (nrr + nisr):
raise Exception("Cannot remove more vertices than the graph contains.")
# Generate the CNF formula
# r = reducer()
# print >> sys.stderr, 'Creating the original CNF formula'
# numVars, cnf = r.reduce(self.graph.getGraph())
# self.write("reduced", numVars, cnf, 0)
# Strip down to the induced subgraph
# using the structural/random properties, as specified
# by the command cmd line arguments
print >>sys.stderr, "Stripping the graph"
self.strip()
print >>sys.stderr, "Filling out edges"
# edgesAdded = self.fill() # Let exceptions carry up to -main-
# Saturate by default!
if saturate:
print >>sys.stderr, "Saturating with edges..."
self.graph.saturateAvoidK4()
r = reducer()
print >>sys.stderr, "Reducing to 3-SAT"
numVars, cnf = r.reduce(self.graph.getGraph())
self.write("reduced", numVars, cnf, 0)
if self.dump:
print >>sys.stderr, "Dumping the modified graph"
self.dumpGraph()
# Assign values and propagate, if fix was set to true
print >>sys.stderr, "Assigning the random variables"
if self.fix:
self.assignAndWrite(numVars, cnf)
def __init__(self, n = 127, r = 3, fix = True, sample_size = 1, na = 0, smart = False, saturate = False, ne = 0, nrr = 0, nerr = 0, nisr = 0, out = ""):
self.n = n
self.r = r
self.sample = sample_size
self.graph = GNR(n, r)
self.fix = fix
self.na = na
self.smart = smart
self.saturate = saturate
self.ne = ne
self.nrr = nrr
self.nerr = nerr
self.nisr = nisr
self.out = out
def __init__(self, n, r, na, ne, pl, nrr, nisr, out): self.n = n self.r = r self.graph = GNR(n, r) self.na = na self.ne = ne self.pl = pl self.nrr = nrr self.nisr = nisr self.out = out # Start down to the induced subgraph self.strip() # Generate the CNF formula r = reducer() print >> sys.stderr, 'Creating the CNF formula' numVars, cnf = r.reduce(self.graph.getGraph()) # Assign values and propagate print >> sys.stderr, 'Assigning the random variables' self.assignValues(numVars, cnf)
class injector:
def __init__(self, n, r, sample, fix, na, smart, saturate, ne, nrr, nerr, nisr, out, dump):
self.n = n
self.r = r
self.sample = sample
self.graph = GNR(n, r)
self.fix = fix
self.na = na
self.smart = smart
self.saturate = saturate
self.ne = ne
self.nrr = nrr
self.nerr = nerr
self.nisr = nisr
self.out = out
self.dump = dump
# Preliminary error checking
if self.graph.getGraph().edges() < self.na:
raise Exception("Cannot remove more edges than the graph contains.")
if n < (nrr + nisr):
raise Exception("Cannot remove more vertices than the graph contains.")
# Generate the CNF formula
# r = reducer()
# print >> sys.stderr, 'Creating the original CNF formula'
# numVars, cnf = r.reduce(self.graph.getGraph())
# self.write("reduced", numVars, cnf, 0)
# Strip down to the induced subgraph
# using the structural/random properties, as specified
# by the command cmd line arguments
print >>sys.stderr, "Stripping the graph"
self.strip()
print >>sys.stderr, "Filling out edges"
# edgesAdded = self.fill() # Let exceptions carry up to -main-
# Saturate by default!
if saturate:
print >>sys.stderr, "Saturating with edges..."
self.graph.saturateAvoidK4()
r = reducer()
print >>sys.stderr, "Reducing to 3-SAT"
numVars, cnf = r.reduce(self.graph.getGraph())
self.write("reduced", numVars, cnf, 0)
if self.dump:
print >>sys.stderr, "Dumping the modified graph"
self.dumpGraph()
# Assign values and propagate, if fix was set to true
print >>sys.stderr, "Assigning the random variables"
if self.fix:
self.assignAndWrite(numVars, cnf)
def dumpGraph(self):
for e in self.graph.getGraph().edges():
print(str(e[0]) + " " + str(e[1]))
def strip(self):
for i in range(self.nrr):
node = random.randint(0, self.n)
self.graph.dropNode(node)
self.n = self.n - 1
for i in range(self.nisr):
self.graph.removeIndependentSet()
for i in range(self.nerr):
edge = random.randint(0, len(self.graph.getGraph().edges()))
self.graph.dropEdge(edge)
def fill(self):
edgesAdded = 0
for i in range(self.ne):
if self.graph.addRandomEdgeAvoidK4():
edgesAdded = edgesAdded + 1
return edgesAdded
def edgesIntersect(self, e, vars, cnf):
for c in cnf:
for v in vars:
if (v in c) and (e in c):
return True
return False
def assignAndWrite(self, numVars, cnf):
numClauses = len(cnf)
numFormulas = 0
minTwoClauses = numClauses
totalTwoClauses = 0
maxTwoClauses = 0
minThreeClauses = numClauses
maxThreeClauses = 0
totalThreeClauses = 0
totalRatio = 0
# list of random variables to remove
varsToAssign = []
if self.smart:
loop = 0
THRESHOlD = 100 # There has to be a way to determine the maximum number of disjoint things...
for i in range(self.na):
if loop > THRESHOlD:
break
else:
var = random.randint(0, numVars)
loop = 0
while self.edgesIntersect(var, varsToAssign, cnf) and loop < THRESHOlD:
loop = loop + 1
var = random.randint(0, numVars)
if loop < THRESHOlD:
varsToAssign.append(var)
else:
for i in range(self.na):
var = random.randint(0, numVars)
if var not in varsToAssign:
varsToAssign.append(var)
else:
while var in varsToAssign:
var = random.randint(0, numVars)
varsToAssign.append(var)
print >>sys.stderr, "Variables to assign: " + str(varsToAssign)
# list of all possible variable assignments, start with all false
assign = {}
for i in range(self.na):
assign[varsToAssign[i]] = False
print >>sys.stderr, "Assigning variables..."
configSamples = []
configIndex = 0
# for config in range(2 ** self.na):
numSamples = 2 ** self.na
while configIndex < self.sample or numFormulas == 0:
# Select a random configuration not already in the set
c = random.randint(0, numSamples)
while c in configSamples:
c = random.randint(0, numSamples)
configSamples.append(c)
# Advance the configuration array...
for i in range(len(assign.keys())):
if ((1 << i) & c) > 0:
assign[assign.keys()[i]] = True
else:
assign[assign.keys()[i]] = False
# Print out the configuration we're on...
if configIndex % 500 == 0:
print >>sys.stderr, "Config " + str(configIndex)
newCnf = []
for c in cnf: # for each clause
include = True
clause = c[:] # Copy over the clause
settled = False
# print("Examining clause: " + str(clause))
for l in c: # for each literal
if include and settled == False:
index = -1
inVars = False
negative = False
if l < 0 and (l * -1) in varsToAssign:
index = l * -1
inVars = True
negative = True
elif l > 1 and l in varsToAssign:
index = l
inVars = True
if inVars:
settled = True
if assign[index] == True and negative == False: # the literal is true, so drop the clause
# print("Dropping clause because " + str(index) + " was assigned to " + str(assign[index]))
include = False
elif assign[index] == True and negative == True:
# print("Dropping literal " + str(index) + " from the clause because it was assigned to " + str(assign[index]))
include = True
clause.remove(l) # remove the literal, it evaluated to false...
elif assign[index] == False and negative == False:
# print("Dropping literal " + str(index) + " from the clause because it was assigned to " + str(assign[index]))
include = True
clause.remove(l) # remove the literal, it evaluated to false...
elif assign[index] == False and negative == True:
# print("Dropping clause because " + str(index) + " was assigned to " + str(assign[index]))
include = False
if include:
# print("Resulting clause inserted: " + str(clause))
newCnf.append(clause)
# Pass over the new CNF and find all unit clauses
# This is for unit propagation...
units = {}
for c in newCnf:
if len(c) == 1:
literal = c[0]
if literal < 0:
units[literal * -1] = False
else:
units[literal] = True
# Now that we have all of the unit literals with their truth values, perform the walk again...
finalCnf = []
sizeTwoClauses = 0
sizeThreeClauses = 0
write = True
for c in newCnf: # for each clause
include = True
clause = c[:] # Copy over the clause
for l in c: # for each literal
if include:
index = -1
inVars = False
if l < 0 and (l * -1) in units.keys():
index = l * -1
inVars = True
elif l > 0 and l in units.keys():
index = l
inVars = True
if inVars:
if units[index] == True: # the literal is true, so drop the clauses
include = False
print >>sys.stderr, "HOW CAN THIS HAPPEN?"
return
else:
clause.remove(l) # remove the literal, it evaluated to false...
if include and len(clause) > 0: # do not append empty clauses, they cause immediate unsatisfiability...
if len(clause) == 2:
sizeTwoClauses = sizeTwoClauses + 1
elif len(clause) == 3:
sizeThreeClauses = sizeThreeClauses + 1
finalCnf.append(clause)
elif len(clause) == 0:
# print >> sys.stderr, "Found unsatisfiable clause in variable configuration " + str(configIndex) + ", discarding formula."
write = False
break
configIndex = configIndex + 1
# Write the output file
if write:
# Compound total number of formulas
numFormulas = numFormulas + 1
self.write(configIndex, numVars, finalCnf, sizeTwoClauses)
# Compound the statistics for the two clauses
if sizeTwoClauses < minTwoClauses:
minTwoClauses = sizeTwoClauses
if sizeTwoClauses > maxTwoClauses:
maxTwoClauses = sizeTwoClauses
totalTwoClauses = totalTwoClauses + sizeTwoClauses
# Compound stats for three clauses
if sizeThreeClauses < minThreeClauses:
minThreeClauses = sizeThreeClauses
if sizeThreeClauses > maxThreeClauses:
maxThreeClauses = sizeThreeClauses
totalThreeClauses = totalThreeClauses + sizeThreeClauses
# Compound ratio
totalRatio = totalRatio + (float(totalTwoClauses) / float(totalThreeClauses))
# Output the stats (if we actually wrote anything...)
if numFormulas > 0:
print >>sys.stderr, "Total number of formulas: " + str(numFormulas)
print >>sys.stderr, "Minimum number of 2-clauses: " + str(minTwoClauses)
print >>sys.stderr, "Maximum number of 2-clauses: " + str(maxTwoClauses)
print >>sys.stderr, "Average number of 2-clauses: " + str(totalTwoClauses / numFormulas)
print >>sys.stderr, "Minimum number of 3-clauses: " + str(minThreeClauses)
print >>sys.stderr, "Maximum number of 3-clauses: " + str(maxThreeClauses)
print >>sys.stderr, "Average number of 3-clauses: " + str(totalThreeClauses / numFormulas)
print >>sys.stderr, "Average ratio of 2-to-3 clauses: " + str(totalRatio / numFormulas)
def write(self, index, numVars, cnf, numTwoClauses):
filename = self.out + "_" + str(index) + ".pickle"
self.graph.dump(filename)
print >>sys.stderr, str(self.out + "_" + str(index))
f = open(self.out + "_" + str(index), "wb")
header, clauses = makeDimacsCNF(numVars, cnf)
f.write("c " + str(numTwoClauses) + "\n")
f.write(header + "\n")
for c in clauses:
for l in c:
f.write(str(l) + " ")
f.write("0 \n")
class mangler:
def __init__(self, n, r, na, ne, pl, nrr, nisr, out):
self.n = n
self.r = r
self.graph = GNR(n, r)
self.na = na
self.ne = ne
self.pl = pl
self.nrr = nrr
self.nisr = nisr
self.out = out
# Start down to the induced subgraph
self.strip()
# Generate the CNF formula
r = reducer()
print >> sys.stderr, 'Creating the CNF formula'
numVars, cnf = r.reduce(self.graph.getGraph())
# Assign values and propagate
print >> sys.stderr, 'Assigning the random variables'
self.assignValues(numVars, cnf)
def strip(self):
for i in range(self.nrr):
node = random.randint(0, self.n)
self.graph.dropNode(node)
self.n = self.n - 1
for i in range(self.nisr):
self.graph.removeIndependentSet()
def assignValues(self, numVars, cnf):
# list of random variables to remove
varsToAssign = []
for i in range(self.na):
var = random.randint(0, numVars)
if (var not in varsToAssign):
varsToAssign.append(var)
else:
while (var in varsToAssign):
var = random.randint(0, numVars)
varsToAssign.append(var)
# list of all possible variable assignments, start with all false
assign = {}
for i in range(self.na):
assign[varsToAssign[i]] = False
print("Assigning variables...")
configIndex = 0
for config in range(2 ** self.na):
print("Config " + str(configIndex))
newCnf = []
for c in cnf: # for each clause
include = True
clause = c[:] # Copy over the clause
for l in c: # for each literal
if include:
index = -1
inVars = False
if (l < 0 and (l * -1) in varsToAssign):
index = (l * -1)
inVars = True
elif (l > 1 and l in varsToAssign):
index = l
inVars = True
if inVars:
if (assign[index] == True): # the literal is true, so drop the clauses
include = False
else:
clause.remove(l) # remove the literal, it evaluated to false...
if include:
newCnf.append(clause)
# Pass over the new CNF and find all unit clauses
# This is for unit propagation...
units = {}
for c in newCnf:
if (len(c) == 1):
literal = c[0]
if (literal < 0):
units[literal * -1] = False
# unitAssigns.append(False) # must be false
else:
units[literal] = True
# unitAssigns.append(True)
# Now that we have all of the unit literals with their truth values, perform the walk again...
finalCnf = []
# compute average time for each of the results
# multiply by 2^20 for hardness!
# TODO: Make this into a method? (oldCnf, variable list with assignments, ...)
sizeTwoClauses = 0
for c in newCnf: # for each clause
include = True
clause = c[:] # Copy over the clause
for l in c: # for each literal
if include:
index = -1
inVars = False
if (l < 0 and (l * -1) in units.keys()):
index = (l * -1)
inVars = True
elif (l > 1 and l in units.keys()):
index = l
inVars = True
if inVars:
if (units[index] == True): # the literal is true, so drop the clauses
include = False
else:
clause.remove(l) # remove the literal, it evaluated to false...
if include and len(clause) > 0: # do not append empty clauses, they cause immediate unsatisfiability...
if (len(clause) == 2):
sizeTwoClauses = sizeTwoClauses + 1
finalCnf.append(clause)
elif (len(clause) == 0):
print("Found unsatisfiable clause in variable configuration " + str(configIndex) + ", discarding formula.")
# Write the output file
self.write(configIndex, numVars, finalCnf, sizeTwoClauses)
configIndex = configIndex + 1
# advance the configuration
for k in assign.keys():
if (assign[k] == True): # propagate to the next clause!
assign[k] = False
else:
assign[k] = True
break # hop out early...
def write(self, index, numVars, cnf, numTwoClauses):
print("Writing: " + str(self.out + "_" + str(index)))
f = open(self.out + "_" + str(index), 'wb')
header, clauses = makeDimacsCNF(numVars, cnf)
f.write('c ' + str(numTwoClauses) + "\n")
f.write(header + "\n")
for c in clauses:
for l in c:
f.write(str(l) + " ")
f.write("0 \n")
class injector:
def __init__(self, n = 127, r = 3, fix = True, sample_size = 1, na = 0, smart = False, saturate = False, ne = 0, nrr = 0, nerr = 0, nisr = 0, out = ""):
self.n = n
self.r = r
self.sample = sample_size
self.graph = GNR(n, r)
self.fix = fix
self.na = na
self.smart = smart
self.saturate = saturate
self.ne = ne
self.nrr = nrr
self.nerr = nerr
self.nisr = nisr
self.out = out
def apply_modifications(self, pcmap = None):
# Preliminary error checking
if (self.graph.getGraph().edges() < self.na):
raise Exception("Cannot remove more edges than the graph contains.")
if (self.n < (self.nrr + self.nisr)):
raise Exception("Cannot remove more vertices than the graph contains.")
# Strip down to the induced subgraph
# using the structural/random properties, as specified
# by the command cmd line arguments
print >> sys.stderr, 'Stripping the graph...'
print >> sys.stderr, "Random vertices to remove: " + str(self.nrr)
print >> sys.stderr, "Random edges to remove: " + str(self.nerr)
print >> sys.stderr, "Maximal independent sets to remove: " + str(self.nisr)
self.strip()
print >> sys.stderr, 'Done.'
# Saturate by default!
if (self.saturate):
print >> sys.stderr, "Saturating with edges..."
self.graph.saturateAvoidK4();
print >> sys.stderr, "Done."
# Now reduce the graph to SAT
r = reducer()
print >> sys.stderr, "Reducing to 3-SAT..."
numVars, edgeMap, cnf = r.reduce(self.graph.getGraph())
print >> sys.stderr, "Done."
# Output or not...
print >> sys.stderr, "Writing the original CNF formula..."
self.write("reduce", numVars, cnf, 0)
print >> sys.stderr, "Done."
print >> sys.stderr, "Writing reduced graph edge list..."
self.dumpGraph()
print >> sys.stderr, "Done."
return numVars, edgeMap, cnf
def inject(self):
# Apply graph modifications
numVars, edgeMap, cnf = self.apply_modifications()
# Assign values and propagate, if fix was set to true
print >> sys.stderr, "Performing edge assignment and writing the output..."
self.assignAndWrite(numVars, edgeMap, cnf)
print >> sys.stderr, "Done."
def inject_arbitrary_graph_with_edge_precoloring(self, gnrG, pcmap):
''' For an arbitrary GNR graph G and set of precolored edges, precolor the edges and
perform the reduction to SAT using the same approach as the general inject() above.
If the structure of the graph is to be modified, then the appropriate parameters
should be passed into the injector constructor above.
'''
# Apply graph modifications
self.n = gnrG.n
self.r = gnrG.r
self.graph = gnrG
numVars, edgeMap, cnf = self.apply_modifications()
print >> sys.stderr, "pcmap: " + str(pcmap)
# Assign values and propagate, if fix was set to true
print >> sys.stderr, "Performing edge assignment and writing the output..."
self.assignAndWrite(numVars, edgeMap, cnf, pcmap)
print >> sys.stderr, "Done."
# Write the edge-list representation of the graph (after preprocessed)
# as well as the pickled graph object
def dumpGraph(self):
filename = self.out + "_graph.pickle"
self.graph.dump(filename)
# Strip down the graph according to the specified parameters
def strip(self):
for i in range(self.nrr):
node = random.randint(0, self.n)
self.graph.dropNode(node)
self.n = self.n - 1
for i in range(self.nisr):
self.graph.removeIndependentSet()
for i in range(self.nerr):
edge = random.randint(0, len(self.graph.getGraph().edges()))
self.graph.dropEdge(edge)
# Fill in the graph with the parameters specificed
def fill(self):
edgesAdded = 0
for i in range(self.ne):
if self.graph.addRandomEdgeAvoidK4():
edgesAdded = edgesAdded + 1
return edgesAdded
def edgesIntersect(self, e, vars, cnf):
for c in cnf:
for v in vars:
if (v in c) and (e in c):
return True
return False
def vte(self, var, edgeMap):
for e in self.graph.edges():
if edgeMap[e] == var:
return e
def pickRandomEdgeToAssign(varsToAssign, numVars, edgeMap, pcmap):
var = random.randint(0, numVars)
if (pcmap == None):
if (var in varsToAssign):
while (var in varsToAssign):
var = random.randint(0, numVars)
else:
ei = self.vte(var, edgeMap)
if (var in varsToAssign or edgeMap[ei] in pcmap[0] or edgeMap[ei] in pcmap[1]):
while (var in varsToAssign or edgeMap[ei] in pcmap[0] or edgeMap[ei] in pcmap[1]):
var = random.randint(0, numVars)
ei = self.vte(var, edgeMap)
return var
def assignAndWrite(self, numVars, edgeMap, cnf, pcmap = None):
numClauses = len(cnf)
numFormulas = 0
minTwoClauses = numClauses
totalTwoClauses = 0
maxTwoClauses = 0
minThreeClauses = numClauses
maxThreeClauses = 0
totalThreeClauses = 0
totalRatio = 0
# Pick the set of edges to assign
# Smart selection of edges picks those that are disjoint (well, it tries to THRESHOLD times),
# and then it gives up and just picks a random one
# "Dumb" selection just picks random edges, not caring whether or not they are disjoint
varsToAssign = []
if (self.smart):
loop = 0
THRESHOLD = 100 # There has to be a way to determine the maximum number of disjoint things...
for i in range(self.na):
if loop > THRESHOLD:
break
else:
var = random.randint(0, numVars)
loop = 0
ei = self.vte(var, edgeMap)
while (self.edgesIntersect(var, varsToAssign, cnf) and loop < THRESHOLD or edgeMap[ei] in pcmap[0] or edgeMap[ei] in pcmap[1]):
loop = loop + 1
var = random.randint(0, numVars)
if (loop < THRESHOLD): # We found a disjoint one...
varsToAssign.append(var)
else: # Failed to find a disjoint one, so pick something at random
varsToAssign.append(pickRandomEdgeToAssign(varsToAssign, numVars, edgeMap, pcmap))
else:
for i in range(self.na):
varsToAssign.append(pickRandomEdgeToAssign(varsToAssign, numVars, edgeMap, pcmap))
# debug
print >> sys.stderr, "Variables to assign: " + str(varsToAssign)
# list of all possible variable assignments, start with all false
assign = {}
for i in range(self.na):
assign[varsToAssign[i]] = False
print >> sys.stderr, "Assigning variables..."
configSamples = []
configIndex = 0
#for config in range(2 ** self.na):
numSamples = 2 ** self.na
while (configIndex < self.sample or numFormulas == 0):
# Select a random configuration not already in the set
c = random.randint(0, numSamples)
while (c in configSamples):
c = random.randint(0, numSamples)
configSamples.append(c)
# Advance the configuration array...
for i in range(len(assign.keys())):
if (((1 << i) & c) > 0):
assign[assign.keys()[i]] = True
else:
assign[assign.keys()[i]] = False
# Print out the configuration we're on...
if (configIndex % 500 == 0):
print >> sys.stderr, "Config " + str(configIndex)
newCnf = []
# Quick hack so we don't throw NPEs
if pcmap == None:
print >> sys.stderr, "nullifying the pcmap because one was not provided"
pcmap = {}
pcmap[0] = []
pcmap[1] = []
else:
print >> sys.stdout, str(pcmap)
for c in cnf: # for each clause
include = True
clause = c[:] # Copy over the clause
settled = False
# print("Examining clause: " + str(clause))
for l in c: # for each literal
if include and settled == False:
index = -1
inVars = False
negative = False
ei = (0,0)
if (l < 0):
ei = self.vte(l * -1, edgeMap)
else:
ei = self.vte(l, edgeMap)
if (l < 0 and ((l * -1) in varsToAssign or ei in pcmap[0] or ei in pcmap[1])):
index = (l * -1)
inVars = True
negative = True
elif (l > 1 and (l in varsToAssign or ei in pcmap[0] or ei in pcmap[1])):
index = l
inVars = True
# Now check to see if this is a variable we're precoloring, and if so, handle accordingly
if inVars:
settled = True
truthValue = False
if ei in pcmap[0]:
truthValue = False
elif ei in pcmap[1]:
truthValue = True
else:
truthValue = assign[index]
# Use the truth value assignment and value of the literal (whether it was negated or not) to determine what value to give it
if (truthValue == True and negative == False): # the literal is true, so drop the clause
include = False
elif (truthValue == True and negative == True):
include = True
clause.remove(l) # remove the literal, it evaluated to false...
elif (truthValue == False and negative == False):
include = True
clause.remove(l) # remove the literal, it evaluated to false...
elif (truthValue == False and negative == True):
include = False
if include:
# print("Resulting clause inserted: " + str(clause))
newCnf.append(clause)
# Pass over the new CNF and find all unit clauses
# This is for unit propagation...
units = {}
for c in newCnf:
if (len(c) == 1):
literal = c[0]
if (literal < 0):
units[literal * -1] = False
else:
units[literal] = True
# Now that we have all of the unit literals with their truth values, perform the walk again and do the substitution
# where we either remove clauses that evaluate to true or discard variables from clauses if the variable is false
finalCnf = []
sizeTwoClauses = 0
sizeThreeClauses = 0
write = True
for c in newCnf: # for each clause
include = True
clause = c[:] # Copy over the clause
for l in c: # for each literal
if include:
index = -1
inVars = False
if (l < 0 and (l * -1) in units.keys()):
index = (l * -1)
inVars = True
elif (l > 0 and l in units.keys()):
index = l
inVars = True
if inVars:
if (units[index] == True): # the literal is true, so drop the clause
include = False
else: # the literal is false, so remove the variable from the clause
clause.remove(l) # remove the literal, it evaluated to false...
# Do not append empty clauses, they cause immediate unsatisfiability...
if include and len(clause) > 0:
if (len(clause) == 2):
sizeTwoClauses = sizeTwoClauses + 1
elif (len(clause) == 3):
sizeThreeClauses = sizeThreeClauses + 1
finalCnf.append(clause)
elif (len(clause) == 0):
# if the clause is empty, then all literals evaluted to false, and so the
# solution is unsatisfiable, and we don't write the output...
write = False
break
# Bump up our unique identifier
configIndex = configIndex + 1
# Write the output file
if (write):
# Compound total number of formulas collected and written so far
numFormulas = numFormulas + 1
self.write(configIndex, numVars, finalCnf, sizeTwoClauses)
# Compound the statistics for the two clauses
if (sizeTwoClauses < minTwoClauses):
minTwoClauses = sizeTwoClauses
if (sizeTwoClauses > maxTwoClauses):
maxTwoClauses = sizeTwoClauses
totalTwoClauses = totalTwoClauses + sizeTwoClauses
# Compound stats for three clauses
if (sizeThreeClauses < minThreeClauses):
minThreeClauses = sizeThreeClauses
if (sizeThreeClauses > maxThreeClauses):
maxThreeClauses = sizeThreeClauses
totalThreeClauses = totalThreeClauses + sizeThreeClauses
# Output the stats (if we actually wrote anything...)
if (numFormulas > 0):
# Compute the 2/3 ratio first
totalRatio = 1.0
if totalThreeClauses != 0:
totalRatio = totalRatio + (float(totalTwoClauses) / float(totalThreeClauses))
# Now display the stats
print >> sys.stderr, "Total number of formulas: " + str(numFormulas)
print >> sys.stderr, "Minimum number of 2-clauses: " + str(minTwoClauses)
print >> sys.stderr, "Maximum number of 2-clauses: " + str(maxTwoClauses)
print >> sys.stderr, "Average number of 2-clauses: " + str(totalTwoClauses / numFormulas)
print >> sys.stderr, "Minimum number of 3-clauses: " + str(minThreeClauses)
print >> sys.stderr, "Maximum number of 3-clauses: " + str(maxThreeClauses)
print >> sys.stderr, "Average number of 3-clauses: " + str(totalThreeClauses / numFormulas)
print >> sys.stderr, "Average ratio of 2-to-3 clauses: " + str(totalRatio / numFormulas)
# Write the resulting CNF file to disk
def write(self, index, numVars, cnf, numTwoClauses):
print >> sys.stderr, str(self.out + "_" + str(index))
f = open(self.out + "_" + str(index), 'wb')
header, clauses = makeDimacsCNF(numVars, cnf)
f.write('c ' + str(numTwoClauses) + "\n")
f.write(header + "\n")
for c in clauses:
for l in c:
f.write(str(l) + " ")
f.write("0 \n")
index = triangle_type(G, v, v1, v2, v3, B, R, T) classes[index - 1].append((v1, v2, v3)) counts[index - 1] = counts[index - 1] + 1 return triangles, classes, counts # Heuristic: # 1. #(random) split Nv = B union R with n(v) >= 154. #2. #Precolor the edges from v to Nv and those induced by B and R, #as described above. This precolors 42 + about (231-154), #or almost 120 edges. #3. #Make a SAT instance alpha for this precoloring.= GNR(127, 3) G = GNR(127,3) nv = int(sys.argv[1]) # THIS SHOULD BE 157 as per SPR's comments print >> sys.stderr, "Searching for split" start = time.time() v, bags, B, R, T, pcmap = G.find_candidate_rb_split(nv, target = 21, delta = 0.9, random_vertex_selection = False, exhaustive = True) end = time.time() print >> sys.stdout, str((end - start) * 1000) + "ms" print >> sys.stderr, str((end - start) * 1000) + "ms" print >> sys.stdout, str(v) print >> sys.stdout, str(B) print >> sys.stdout, str(R) print >> sys.stderr, str(v) print >> sys.stderr, str(B) print >> sys.stderr, str(R) print >> sys.stderr, "Split found!"