def try_sampling(self,subspace_dimension):
for j in range(subspace_dimension):
self.random_projection()
result = self.try_solving()
if result:
print "Random projection satisfied"
tp = self.holes2tape(result)
print parse_tape(tp)[0]
if self.is_solution_unique(tp):
print "Unique. Accepted."
else:
print "Sample rejected"
def try_sampling(self, subspace_dimension):
for j in range(subspace_dimension):
self.random_projection()
result = self.try_solving()
if result:
print "Random projection satisfied"
tp = self.holes2tape(result)
print parse_tape(tp)[0]
if self.is_solution_unique(tp):
print "Unique. Accepted."
else:
print "Sample rejected"
def uniqueness_clause(self,tape):
p,bit_mask = parse_tape(tape)
clause = []
for j in range(len(tape)):
if bit_mask[j] == 1:
# jth tape position
v = self.tape2variable[j]
if tape[j] == 1: v = -v
clause += [v]
return clause
def uniqueness_clause(self, tape):
p, bit_mask = parse_tape(tape)
clause = []
for j in range(len(tape)):
if bit_mask[j] == 1:
# jth tape position
v = self.tape2variable[j]
if tape[j] == 1: v = -v
clause += [v]
return clause
def is_solution_unique(self,tape):
d = self.generate_variable()
clause = [d] + self.uniqueness_clause(tape)
print "uniqueness clause",clause
self.s.add_clause(clause)
result = self.try_solving([-d])
self.s.add_clause([d]) # make the clause documents they satisfied
if result:
tp = self.holes2tape(result)
print "alternative:",parse_tape(tp)
print "alternative tape:",tp
return False
else:
return True
def is_solution_unique(self, tape):
d = self.generate_variable()
clause = [d] + self.uniqueness_clause(tape)
print "uniqueness clause", clause
self.s.add_clause(clause)
result = self.try_solving([-d])
self.s.add_clause([d]) # make the clause documents they satisfied
if result:
tp = self.holes2tape(result)
print "alternative:", parse_tape(tp)
print "alternative tape:", tp
return False
else:
return True
def adaptive_sample(self):
subspace_dimension = 1
result = self.try_solving()
if result:
print "Formula satisfied"
for j in range(subspace_dimension):
self.random_projection()
while True:
print "\n\niterating:"
result = self.try_solving()
if result:
print "Satisfied %d constraints" % subspace_dimension
tp = self.holes2tape(result)
print parse_tape(tp)
print "tape = ",tp
if self.is_solution_unique(tp):
print "UNIQUE"
print "<<< == == == >>>"
self.random_projection()
subspace_dimension += 1
else:
print "Rejected %d projections" % subspace_dimension
print "total time = ",self.tt
break
def adaptive_sample(self):
subspace_dimension = 1
result = self.try_solving()
if result:
print "Formula satisfied"
for j in range(subspace_dimension):
self.random_projection()
while True:
print "\n\niterating:"
result = self.try_solving()
if result:
print "Satisfied %d constraints" % subspace_dimension
tp = self.holes2tape(result)
print parse_tape(tp)
print "tape = ", tp
if self.is_solution_unique(tp):
print "UNIQUE"
print "<<< == == == >>>"
self.random_projection()
subspace_dimension += 1
else:
print "Rejected %d projections" % subspace_dimension
print "total time = ", self.tt
break
def enumerate_solutions(self):
solutions = []
result = self.try_solving()
d = self.generate_variable()
logZ = float('-inf')
while result:
tp = self.holes2tape(result)
program,mask = parse_tape(tp)
solutions = solutions + [program]
specified = sum(mask)
logZ = lse(logZ, -specified * 0.693)
print "Enumerated program", program, "with", specified, "specified bits."
self.s.add_clause([d] + self.uniqueness_clause(tp))
result = self.try_solving([-d])
print "log(z) = ",logZ, "\t1/p = ", math.exp(-logZ)
return solutions
def enumerate_solutions(self):
solutions = []
result = self.try_solving()
d = self.generate_variable()
logZ = float('-inf')
while result:
tp = self.holes2tape(result)
program, mask = parse_tape(tp)
solutions = solutions + [program]
specified = sum(mask)
logZ = lse(logZ, -specified * 0.693)
print "Enumerated program", program, "with", specified, "specified bits."
self.s.add_clause([d] + self.uniqueness_clause(tp))
result = self.try_solving([-d])
print "log(z) = ", logZ, "\t1/p = ", math.exp(-logZ)
return solutions
