def calculate_extended_classes(self, class_to_extend, n_of_the_class, extend_from, extend_to, print_results=True,
pedigreeStruct=None, classesStruct=None, partialClassStruct=None):
from src.database.databaseSol import DB
db = DB()
results = {}
self.extend_class(self.hash(class_to_extend), n_of_the_class, extend_to, extend_from, db, pedigreeStruct, classesStruct, partialClassStruct)
for current_hash in classesStruct[n_of_the_class][extend_to][class_to_extend[0]]:
for current_class, current_count in classesStruct[n_of_the_class][extend_to][class_to_extend[0]][current_hash].items():
results[current_class] = current_count
if pedigreeStruct is not None:
#save correlations classes
db.save(n=n_of_the_class, k=extend_from, k_extend=extend_to, w=class_to_extend[0],
objects=classesStruct[n_of_the_class][extend_to][class_to_extend[0]], type='correlation')
#end of save correlations
self.save_classes(classesStruct, n_of_the_class, extend_to, db, False)
self.save_pedigree(pedigreeStruct, db)
self.save_partial_class(partialClassStruct, n_of_the_class, extend_to, db, False)
'''for r_aux in results:
if self.is_correlation_immune(r_aux, n_of_the_class, extend_from):
try:
result[r_aux] += 1
except:
result[r_aux] = 1'''
if print_results:
print '#####'*15
print 'Original class: %s' % str(class_to_extend)
print '#####'*15
print 'Extending the class from k=%s to k=%s' % (extend_from, extend_to)
print '\n'.join([str(k) + ': ' + str(v) for k, v in results.items()])
print 'Total: %s classes' % len(results)
print '#####'*15
else:
return results.items()
def __init__(self, n, k):
if n <= 0 or k <= 0:
print "N and K must be integers > 0."
exit_program()
self.N = n
self.K = k
self.k_extend = 1
self.functions = Functions()
self.classes = {}
self.correlation = {}
self.solution = {}
self.partialClass = {}
self.pedigree = {}
self.allCalculated = {}
self.start_time = 0
MB = 1024 * 1024
self.limit_of_partial_in_memory = 1024 # xMB
self.batch_length = 100
self.db = DB()
self.init_classes()
try:
self.functions.save(1, 1, self.k_extend, self.correlation,
self.solution, self.classes, self.partialClass,
self.allCalculated, self.db)
self.functions.save_pedigree(self.pedigree, self.db)
except:
print 'The info its already saved'
def calculate_extended_classes(self,
class_to_extend,
n_of_the_class,
extend_from,
extend_to,
print_results=True,
pedigreeStruct=None,
classesStruct=None,
partialClassStruct=None):
from src.database.databaseSol import DB
db = DB()
results = {}
self.extend_class(self.hash(class_to_extend), n_of_the_class,
extend_to, extend_from, db, pedigreeStruct,
classesStruct, partialClassStruct)
for current_hash in classesStruct[n_of_the_class][extend_to][
class_to_extend[0]]:
for current_class, current_count in classesStruct[n_of_the_class][
extend_to][class_to_extend[0]][current_hash].items():
results[current_class] = current_count
if pedigreeStruct is not None:
#save correlations classes
db.save(n=n_of_the_class,
k=extend_from,
k_extend=extend_to,
w=class_to_extend[0],
objects=classesStruct[n_of_the_class][extend_to][
class_to_extend[0]],
type='correlation')
#end of save correlations
self.save_classes(classesStruct, n_of_the_class, extend_to, db,
False)
self.save_pedigree(pedigreeStruct, db)
self.save_partial_class(partialClassStruct, n_of_the_class,
extend_to, db, False)
'''for r_aux in results:
if self.is_correlation_immune(r_aux, n_of_the_class, extend_from):
try:
result[r_aux] += 1
except:
result[r_aux] = 1'''
if print_results:
print '#####' * 15
print 'Original class: %s' % str(class_to_extend)
print '#####' * 15
print 'Extending the class from k=%s to k=%s' % (extend_from,
extend_to)
print '\n'.join(
[str(k) + ': ' + str(v) for k, v in results.items()])
print 'Total: %s classes' % len(results)
print '#####' * 15
else:
return results.items()
def __init__(self, n, k, i):
self.N = n
self.K = k
self.I = i
self.db = DB()
self.functions = Functions()
self.boolean_functions = {
self.functions.hash((0, 0)): [0, 0],
self.functions.hash((1, -1)): [0, 1],
self.functions.hash((1, 1)): [1, 0],
self.functions.hash((2, 0)): [1, 1]
}
def __init__(self, n, k):
if n <= 0 or k <= 0:
print "N and K must be integers > 0."
exit_program()
self.N = n
self.K = k
self.k_extend = 1
self.functions = Functions()
self.classes = {}
self.correlation = {}
self.solution = {}
self.partialClass = {}
self.pedigree = {}
self.allCalculated = {}
self.start_time = 0
MB = 1024*1024
self.limit_of_partial_in_memory = 1024 # xMB
self.batch_length = 100
self.db = DB()
self.init_classes()
try:
self.functions.save(1, 1, self.k_extend, self.correlation, self.solution,
self.classes, self.partialClass,
self.allCalculated, self.db)
self.functions.save_pedigree(self.pedigree, self.db)
except:
print 'The info its already saved'
def __init__(self, n, k, i):
self.N = n
self.K = k
self.I = i
self.db = DB()
self.functions = Functions()
self.boolean_functions = {self.functions.hash((0, 0)): [0, 0],
self.functions.hash((1, -1)): [0, 1],
self.functions.hash((1, 1)): [1, 0],
self.functions.hash((2, 0)): [1, 1]}
class BooleanFunction():
def __init__(self, n, k, i):
self.N = n
self.K = k
self.I = i
self.db = DB()
self.functions = Functions()
self.boolean_functions = {
self.functions.hash((0, 0)): [0, 0],
self.functions.hash((1, -1)): [0, 1],
self.functions.hash((1, 1)): [1, 0],
self.functions.hash((2, 0)): [1, 1]
}
def solve(self):
db = self.db
sols_n_k = db.load(n=self.N, k=self.K, type='solution')
if self.I is not None:
sol_n_k = sols_n_k.keys()[0]
count = sols_n_k[sol_n_k]
if self.I >= count:
print "The index is bigger than the maximum allowed..."
return
current_count = 0
for pedigree_sol in db.load(n=self.N,
hash=self.functions.hash(sol_n_k),
type='pedigree'):
hash_left = pedigree_sol[0]
hash_right = pedigree_sol[1]
if n - 1 == 1:
result = self.boolean_functions[
hash_left] + self.boolean_functions[hash_right]
print ' '.join([str(e) for e in result])
return
class_left, count_left = db.getClass(hash_left)
class_right, count_right = db.getClass(hash_right)
current_count += count_left * count_right
if current_count > self.I:
self.I -= current_count - count_left * count_right
break
index_left = self.I / count_right
index_right = self.I - (index_left * count_right)
result = self.get_boolean_function(
self.N - 1, hash_left, index_left) + self.get_boolean_function(
self.N - 1, hash_right, index_right)
print ' '.join([str(e) for e in result])
else:
for sol_n_k, count in sols_n_k.iteritems():
result = []
for pedigree_struct in db.load(
n=self.N,
hash=self.functions.hash(sol_n_k),
type='pedigree'):
class_left, count_left = db.getClass(pedigree_struct[0])
class_right, count_right = db.getClass(pedigree_struct[1])
for i in range(0, count_left):
for j in range(0, count_right):
lst_left = self.get_boolean_function(
self.N - 1, pedigree_struct[0], i)
lst_right = self.get_boolean_function(
self.N - 1, pedigree_struct[1], j)
result.append(lst_left + lst_right)
for current_result in result:
print ' '.join([str(e) for e in current_result])
if len(result) > 0:
print "Size of result %s" % len(result)
def get_boolean_function(self, n, hash, index):
if n == 1:
return self.boolean_functions[hash]
current_count = 0
for pedigree_sol in self.db.load(n=n, hash=hash, type='pedigree'):
hash_left = pedigree_sol[0]
hash_right = pedigree_sol[1]
class_left, count_left = self.db.getClass(hash_left)
class_right, count_right = self.db.getClass(hash_right)
current_count += count_left * count_right
if current_count > index:
index -= current_count - count_left * count_right
break
index_left = index / count_right
index_right = index - (index_left * count_right)
return self.get_boolean_function(
n - 1, hash_left, index_left) + self.get_boolean_function(
n - 1, hash_right, index_right)
def get_all_boolean_functions(self, n, hash):
if n == 1:
return [self.boolean_functions[hash]]
result = []
for pedigree_struct in self.db.load(hash=hash, n=n, type='pedigree'):
new_hash_left = pedigree_struct[0]
new_hash_right = pedigree_struct[1]
lst_left = self.get_all_boolean_functions(n - 1, new_hash_left)
lst_right = self.get_all_boolean_functions(n - 1, new_hash_right)
for left in lst_left:
for right in lst_right:
result.append(left + right)
return result
class Problem():
def __init__(self, n, k):
if n <= 0 or k <= 0:
print "N and K must be integers > 0."
exit_program()
self.N = n
self.K = k
self.k_extend = 1
self.functions = Functions()
self.classes = {}
self.correlation = {}
self.solution = {}
self.partialClass = {}
self.pedigree = {}
self.allCalculated = {}
self.start_time = 0
MB = 1024 * 1024
self.limit_of_partial_in_memory = 1024 # xMB
self.batch_length = 100
self.db = DB()
self.init_classes()
try:
self.functions.save(1, 1, self.k_extend, self.correlation,
self.solution, self.classes, self.partialClass,
self.allCalculated, self.db)
self.functions.save_pedigree(self.pedigree, self.db)
except:
print 'The info its already saved'
def init_classes(self):
##############################
# THE CLASSES
##############################
#classes[n][k_extend][w][hash] = {class: counter}
self.classes[1] = {}
self.classes[1][1] = {}
self.classes[1][1][0] = {self.functions.hash((0, 0)): {(0, 0): 1}}
self.classes[1][1][1] = {self.functions.hash((1, -1)): {(1, -1): 1}}
self.classes[1][1][1].update(
{self.functions.hash((1, 1)): {
(1, 1): 1
}})
self.classes[1][1][2] = {self.functions.hash((2, 0)): {(2, 0): 1}}
##############################
#Information about class generation
##############################
#pedigree[n][k_extend][hash] = [(class_left, class_right)]
self.pedigree[1] = {}
self.pedigree[1][1] = {}
self.pedigree[1][1][self.functions.hash((0, 0))] = [(None, None)]
self.pedigree[1][1][self.functions.hash((1, -1))] = [(None, None)]
self.pedigree[1][1][self.functions.hash((1, 1))] = [(None, None)]
self.pedigree[1][1][self.functions.hash((2, 0))] = [(None, None)]
##############################
# CORRELATION INMUNE
##############################
#correlation[n][k][k_extend][w][hash] = {class: counter}
self.correlation[1] = {}
self.correlation[1][1] = {}
self.correlation[1][1][1] = {}
self.correlation[1][1][1][0] = {
self.functions.hash((0, 0)): {
(0, 0): 1
}
}
self.correlation[1][1][1][2] = {
self.functions.hash((2, 0)): {
(2, 0): 1
}
}
##############################
# SOLUTIONS
##############################
#solucion[n][k] = {class: counter}
self.solution[1] = {}
self.solution[1][1] = {(2, 0): 1}
##############################
# PARTIALCLASS
##############################
#partialClass[N][k][k_extend][W][partial_class] = [ids]
self.partialClass[1] = {}
self.partialClass[1][1] = {}
self.partialClass[1][1][1] = {}
for w in self.classes[1][1]:
self.partialClass[1][1][1][w] = {}
for hash in self.classes[1][1][w]:
for c0, _ in self.classes[1][1][w][hash].iteritems():
partial_class = (c0[1], )
if partial_class in self.partialClass[1][1][1][
w] and self.functions.hash(
c0) not in self.partialClass[1][1][1][w][
partial_class]:
self.partialClass[1][1][1][w][partial_class].append(
self.functions.hash(c0))
else:
self.partialClass[1][1][1][w][partial_class] = [
self.functions.hash(c0)
]
##############################
# CLASSESS CALCULATED AT ALL
##############################
#allCalculated[N][k_extend][W] = true
self.allCalculated[1] = {}
self.allCalculated[1][1] = {}
self.allCalculated[1][1].update({0: True})
self.allCalculated[1][1].update({1: True})
self.allCalculated[1][1].update({2: True})
def get_all(self, n, weight):
res = []
k_extend_max = self.k_extend if self.k_extend <= n else n
try:
if n > 0 and (n not in self.classes
or weight not in self.classes[n][k_extend_max]):
self.functions.load_data(n, 1, k_extend_max, self.correlation,
self.solution, self.classes,
self.partialClass, self.db, weight)
if weight <= 2**n and (weight not in self.classes[n][k_extend_max]
or not self.functions.is_calculated(
n, weight, self.db, k_extend_max)):
self.get_all_classes(n, weight)
elif weight not in self.classes[n][k_extend_max]:
return []
if n not in self.classes:
self.functions.load_classes(n, weight, k_extend_max,
self.classes, self.db)
res = [(i, self.classes[n][k_extend_max][weight][hashclass][i])
for hashclass in self.classes[n][k_extend_max][weight]
for i in self.classes[n][k_extend_max][weight][hashclass]]
self.functions.clean_structures(self.classes,
self.pedigree,
self.partialClass,
self.correlation,
less_or_equal=n)
except:
import traceback
traceback.print_exc()
return res
def get_all_classes(self, n, weight, find_corr=False):
if n == 0:
return
k_extend_max = self.k_extend if self.k_extend <= n else n
f = self.functions
# verify if i have the data in memory, if not i will try to obtain the data from db
if f.is_calculated(n, weight, self.db, k_extend_max) and (
n not in self.classes
or weight not in self.classes[n][k_extend_max]):
f.load_data(n, 1, k_extend_max, self.correlation, self.solution,
self.classes, self.partialClass, self.db, weight)
return
f.remove_classes(self.classes, n)
# if i do not have the data in memory neither in db, i have to calculate
if n not in self.classes or weight not in self.classes[n][k_extend_max]:
print 'Calculating for N = %s and weight = %s' % (n, weight)
for w in xrange(0, weight + 1):
print "Crossing All Classes of weight %s VS of weight %s --> N = %s" % (
w, weight - w, n - 1)
ind = 0
for c_0, count in self.get_all(n - 1, w):
for c_1, count_aux in self.get_all(n - 1, weight - w):
new_class = f.new_class(c_0, c_1, n, k_extend_max)
new_weight = new_class[0]
f.refresh_pedigree(n, k_extend_max, new_class, c_0,
c_1, self.pedigree)
f.refresh_mirror_classes(n, k_extend_max, new_weight,
new_class, self.classes,
self.partialClass)
f.refresh_classes(n, k_extend_max, new_weight,
new_class, count, count_aux,
self.classes)
if find_corr and f.is_correlation_immune(
new_class, n, 1):
f.add_correlation(n, 1, k_extend_max, new_weight,
new_class, count * count_aux,
self.correlation)
ind += 1
if n not in self.allCalculated:
self.allCalculated[n] = {}
if k_extend_max not in self.allCalculated[n]:
self.allCalculated[n][k_extend_max] = {}
self.allCalculated[n][k_extend_max].update({weight: True})
print "END :: Crossing All Classes of weight %s (N=%s)" % (weight,
n - 1)
self.functions.save(n,
1,
k_extend_max,
self.correlation,
self.solution,
self.classes,
self.partialClass,
self.allCalculated,
self.db,
delete=True) # LEAVE delete IN FALSE!
self.functions.save_pedigree(self.pedigree, self.db)
def get_all_k_correlation(self, n, weight, k_corr):
res = []
k_extend_max = self.k_extend if self.k_extend <= n else n
try:
if k_corr == 0:
self.get_all_classes(n, weight)
self.functions.load_classes(n, weight, k_extend_max,
self.classes, self.db)
res = [
(i, self.classes[n][k_extend_max][weight][hashclass][i])
for hashclass in self.classes[n][k_extend_max][weight]
for i in self.classes[n][k_extend_max][weight][hashclass]
]
return res
elif k_corr == 1:
n_iter = n - 1 if n > 1 else 1
self.get_all_classes(n_iter, weight / 2)
self.generate_k_corr(n, weight, k_corr)
elif k_corr > 1:
self.get_all_k_correlation(n - 1, weight / 2, k_corr - 1)
self.generate_k_corr(n, weight, k_corr)
self.functions.load_correlation(n, k_corr, k_extend_max,
self.correlation, self.db, weight)
if weight in self.correlation[n][k_corr][k_extend_max]:
res = [(i, self.correlation[n][k_corr][k_extend_max][weight]
[hashclass][i]) for hashclass in self.correlation[n]
[k_corr][k_extend_max][weight]
for i in self.correlation[n][k_corr][k_extend_max]
[weight][hashclass]]
self.functions.clean_structures(self.classes, self.pedigree,
self.partialClass,
self.correlation)
except:
import traceback
traceback.print_exc()
return res
def generate_k_corr(self, n, weight, k_corr):
if n == 3 and weight == 4 and k_corr == 2:
pass
k_extend_max = self.k_extend if self.k_extend <= n else n
k_extend_max_load = self.k_extend if self.k_extend <= n - 1 else n - 1
f = self.functions
f.clean_structures(self.classes, self.pedigree, self.partialClass,
self.correlation)
if k_corr > 1:
f.load_correlation(n - 1, k_corr - 1, k_extend_max_load,
self.correlation, self.db, weight / 2)
if weight / 2 not in self.correlation[n - 1][k_corr -
1][k_extend_max_load]:
return # There is no k_corr for that N(=n-1) and Weight(=weight)
set_all_classes = [
(i, self.correlation[n -
1][k_corr -
1][k_extend_max_load][weight /
2][hashclass][i])
for hashclass in self.correlation[n - 1][
k_corr - 1][k_extend_max_load][weight / 2]
for i in self.correlation[n - 1][
k_corr - 1][k_extend_max_load][weight / 2][hashclass]
]
self.correlation[n - 1][k_corr - 1][k_extend_max_load][weight /
2] = {}
else:
f.load_classes(n - 1, weight / 2, k_extend_max_load, self.classes,
self.db)
set_all_classes = [
(i, self.classes[n - 1][k_extend_max_load][weight /
2][hashclass][i])
for hashclass in self.classes[n -
1][k_extend_max_load][weight / 2]
for i in self.classes[n - 1][k_extend_max_load][weight /
2][hashclass]
]
self.classes[n - 1][k_extend_max_load][weight / 2] = {}
print 'Generating all %s-corr of weight exactly = %s (N=%s)' % (
k_corr, weight, n)
ind = 0
ind_added = 0
size_set = len(set_all_classes)
for c_0, count in set_all_classes:
if ind % 100 == 0:
print '[%s/%s] ---> %s-corr, w=%s, n=%s, Size_Partial=%s' % (
ind + 1, size_set, k_corr, weight, n,
sys.getsizeof(dumps(self.partialClass)))
if ind % self.batch_length == 0:
self.mirror_batch(n, k_corr, k_extend_max, set_all_classes,
ind, weight)
for mirror, count_aux in self.find_mirror(c_0, n - 1, k_corr):
new_class = f.new_class(c_0, mirror, n, k_extend_max)
if f.is_correlation_immune(new_class, n, k_corr):
ind_added += 1
new_weight = new_class[0]
f.refresh_pedigree(n, k_extend_max, new_class, c_0, mirror,
self.pedigree)
f.refresh_mirror_classes(n, k_extend_max, new_weight,
new_class, self.classes,
self.partialClass)
f.refresh_classes(n, k_extend_max, new_weight, new_class,
count, count_aux, self.classes)
f.add_correlation(n, k_corr, k_extend_max, new_weight,
new_class, count * count_aux,
self.correlation)
ind += 1
print 'END:: Generating all %s-corr of weight exactly = %s (N=%s)' % (
k_corr, weight, n)
f.save_by_weight(n,
weight,
k_extend_max,
self.db,
self.classes,
self.correlation,
self.partialClass,
delete=False)
f.save_pedigree(self.pedigree, self.db)
def get_mirror_partial(self, class_aux, n, k):
top = self.functions.get_top(n, k)
ini = 1
for i in range(1, k):
ini += self.functions.get_top(n, i)
c0 = class_aux[ini:ini + top]
partial_class = []
for elem in c0:
partial_class.append(elem * -1)
return tuple(partial_class)
def find_mirror(self, class_aux, n, k):
k_extend_max = self.k_extend if self.k_extend <= n else n
#class = (2,0,0,0,2,alfa) and exists the mirror (2,0,0,0,-2,beta)
weight = class_aux[0]
partial_class = self.get_mirror_partial(class_aux, n, k)
result = []
if len(partial_class) == 0:
if k > 1:
return [(
i,
self.correlation[n][k -
1][k_extend_max][weight][hashclass][i]
) for hashclass in self.correlation[n][k -
1][k_extend_max][weight]
for i in self.correlation[n][k - 1][k_extend_max]
[weight][hashclass]]
else:
return [
(i, self.classes[n][k_extend_max][weight][hashclass][i])
for hashclass in self.classes[n][k_extend_max][weight]
for i in self.classes[n][k_extend_max][weight][hashclass]
]
try:
for hash_class in self.partialClass[n][k][k_extend_max][weight][
partial_class]:
try:
if k > 1:
c_aux = self.correlation[n][
k - 1][k_extend_max][weight][hash_class].keys()[0]
result.append((c_aux, self.correlation[n][
k - 1][k_extend_max][weight][hash_class][c_aux]))
else:
c_aux = self.classes[n][k_extend_max][weight][
hash_class].keys()[0]
result.append((c_aux, self.classes[n][k_extend_max]
[weight][hash_class][c_aux]))
except:
pass
finally:
return result
def calculate_classes(self, n, k_extend, weight):
self.k_extend = k_extend
self.get_all_classes(n, weight)
self.functions.load_classes(n, weight, k_extend, self.classes, self.db)
size_classes = 0
size_functions = 0
for hash in self.classes[n][k_extend][weight]:
aux_class = self.classes[n][k_extend][weight][hash].keys()[0]
count = self.classes[n][k_extend][weight][hash][aux_class]
print '#: %s - class: %s' % (count, aux_class)
size_classes += 1
size_functions += count
print 'Size of classes: %s' % size_classes
print 'Size of functions: %s\n\n' % size_functions
def calculate_correlation(self, n, k_extend, k, weight):
self.k_extend = k_extend
self.get_all_k_correlation(n, weight, k)
self.functions.load_correlation(n, k, k_extend, self.correlation,
self.db, weight)
size_correlations = 0
size_functions = 0
string_out = ''
for hash in self.correlation[n][k][k_extend][weight]:
aux_class = self.correlation[n][k][k_extend][weight][hash].keys(
)[0]
count = self.correlation[n][k][k_extend][weight][hash][aux_class]
current_out = '#: %s - correlation: %s' % (count, aux_class)
print current_out
string_out += current_out + '\n'
size_correlations += 1
size_functions += count
file_out = open(
'out_n%sk%sk_extend%sw%s.txt' % (n, k, k_extend, weight), 'w')
file_out.write(string_out)
file_out.write('Size of correlations: %s\n' % size_correlations)
print 'Size of correlations: %s' % size_correlations
file_out.write('Size of functions: %s\n\n' % size_functions)
print 'Size of functions: %s\n\n' % size_functions
file_out.close()
def mirror_batch(self, n_iter, k, k_extend, set_classes, ind, weight):
k_extend_max = k_extend if k_extend <= n_iter - 1 else n_iter - 1
#clean partial structure
if n_iter - 1 in self.partialClass:
del self.partialClass[n_iter - 1]
#clean correlation structure
if n_iter - 1 in self.correlation:
del self.correlation[n_iter - 1]
#clean classes structure
if n_iter - 1 in self.classes:
del self.classes[n_iter - 1]
#get batch of the partials
batch = [
self.get_mirror_partial(current_class, n_iter - 1, k)
for current_class, count in set_classes[ind:ind +
self.batch_length]
]
#load all the partials of the batch, in mirror query use the partials structure (dont call to bd!!)
self.functions.load_batch_partial_classes(n_iter - 1, k, k_extend_max,
weight / 2,
self.partialClass, batch,
self.db)
hashes = []
for elem_i in self.partialClass[n_iter -
1][k][k_extend_max][weight /
2].values():
try:
for hash_i in elem_i:
if hash_i not in hashes:
hashes.append(hash_i)
except:
pass
if k > 1:
if len(hashes) > 0:
#load correlation
self.db.getBatchCorrelation(n_iter - 1, k - 1, k_extend_max,
hashes, self.correlation)
else:
self.functions.load_correlation(n_iter - 1, k - 1,
k_extend_max, self.correlation,
self.db, weight / 2)
else:
if len(hashes) > 0:
#load classes
self.db.getBatchClasses(n_iter - 1, k_extend_max, hashes,
self.classes)
else:
self.functions.load_classes(n_iter - 1, weight / 2,
k_extend_max, self.classes,
self.db)
del batch
del hashes
def calculate_tree(self, n, k_extend, k, weight, class_tree):
self.k_extend = k_extend
self.calculate_classes(n, k_extend, weight)
self.functions.load_classes(n, weight, k_extend, self.classes, self.db)
if (class_tree != (-1, )):
hash = self.functions.hash(class_tree)
c_aux = self.classes[n][k_extend][weight][hash].keys()[0]
print 'Class: %s ' % (str(c_aux))
self.calculate_tree_class(hash, n, k_extend, weight)
else:
for hash in self.classes[n][k_extend][weight]:
#armado de arbol para cada correlations
c_aux = self.classes[n][k_extend][weight][hash].keys()[0]
print 'Class: %s ' % (str(c_aux))
self.calculate_tree_class(hash, n, k_extend, weight)
def calculate_tree_class(self, hash, n, k_extend, weight):
k_extend_max = k_extend if k_extend <= (n - 1) else (n - 1)
pedigrees = self.db.load(hash=hash, type='pedigree')
for pedigree in pedigrees:
left = pedigree[0]
right = pedigree[1]
if left is not None and right is not None:
c_left = self.db.getClass(left)[0]
c_right = self.db.getClass(right)[0]
print '%s * %s' % (c_left, c_right)
print '%s ' % (str(c_left))
self.calculate_tree_class(left, n - 1, k_extend_max,
weight / 2)
print '%s ' % (str(c_right))
self.calculate_tree_class(right, n - 1, k_extend_max,
weight / 2)
else:
return
def solve(self, individual=False):
self.start_time = time.time()
f = self.functions
for n_iter in xrange(2, self.N + 1):
for k in xrange(1, self.N + 1):
if individual:
if not (n_iter == self.N and k == self.K):
continue
else:
if k > n_iter or (n_iter == self.N and k > self.K):
break
self.k_extend = k
elapsed_time = time.time() - self.start_time
#print "EAT: %s seconds" % elapsed_time
find_solution = False
weight = self.db.get_last_weight(n_iter, k)
weight_multiple = 2**k
persist = False
if f.solution_exists(n_iter, k, self.solution, self.db):
f.load_correlation(n_iter, k, self.k_extend,
self.correlation, self.db, weight)
f.load_solution(n_iter, k, self.solution, self.db)
find_solution = True
persist = False
while not find_solution:
try:
print ">>>> Weight: %s <-> N = %s, K = %s" % (
weight, n_iter, k)
f.clean_structures(self.classes, self.pedigree,
self.partialClass, self.correlation)
if f.isCorrelationCalculated(n_iter - 1, k - 1, k - 1,
weight / 2, self.db):
set_correlations = f.getSolution(
n_iter - 1, k - 1, self.db)
if k <= n_iter - 1:
set_correlations = f.calculate_extended_classes(
set_correlations.keys()[0], n_iter - 1,
k - 1, k, False, self.pedigree,
self.classes, self.partialClass)
self.functions.clean_structures(
self.classes, self.pedigree,
self.partialClass, self.correlation, True)
else:
set_correlations = set_correlations.items()
else:
set_correlations = self.get_all_k_correlation(
n_iter - 1, weight / 2, k - 1)
ind = 0
ind_added = 0
print "All %s corr of weight %s VS all of weight %s (N=%s)" % (
k - 1, weight / 2, weight / 2, n_iter - 1)
size_set = len(set_correlations)
f.clean_structures(self.classes, self.pedigree,
self.partialClass, self.correlation)
for c_0, count in set_correlations:
if ind % self.batch_length == 0:
print '[%s/%s] ---> %s-corr, w=%s, n=%s, Size_Partial=%s' % (
ind + 1, size_set, k - 1, weight / 2,
n_iter - 1,
sys.getsizeof(dumps(self.partialClass)))
if ind % self.batch_length == 0:
self.mirror_batch(n_iter, k, self.k_extend,
set_correlations, ind,
weight)
for mirror, count_aux in self.find_mirror(
c_0, n_iter - 1, k):
new_class = f.new_class(
c_0, mirror, n_iter, self.k_extend)
new_weight = new_class[0]
if new_weight > 0 and f.is_correlation_immune(
new_class, n_iter, k):
ind_added += 1
f.refresh_pedigree(n_iter, self.k_extend,
new_class, c_0, mirror,
self.pedigree)
f.refresh_mirror_classes(
n_iter, self.k_extend, new_weight,
new_class, self.classes,
self.partialClass)
f.refresh_classes(n_iter, self.k_extend,
new_weight, new_class,
count, count_aux,
self.classes)
f.add_correlation(n_iter, k, self.k_extend,
new_weight, new_class,
count * count_aux,
self.correlation)
f.add_solution(n_iter, new_class,
count * count_aux,
self.solution, k)
find_solution = True
persist = True
ind += 1
f.remove_correlation(self.correlation, n_iter - 1,
k - 1)
print "All %s corr of weight %s VS all of weight %s - OK (N=%s)" % (
k - 1, weight / 2, weight / 2, n_iter - 1)
weight += weight_multiple
except KeyboardInterrupt:
exit(1)
if persist:
f.save(n_iter,
k,
self.k_extend,
self.correlation,
self.solution,
self.classes,
self.partialClass,
self.allCalculated,
self.db,
delete=False)
f.save_pedigree(self.pedigree, self.db)
f.print_results(n_iter, k, self.start_time, self.correlation,
self.solution, True)
print '(n, k) = (%s,%s)' % (n, k)
sleep(2)
BooleanFunction(n, k, None).solve()
elif option == 3:
try:
n = input('Select N: ')
k = input('Select K: ')
except:
print 'N and K must be integers > 0.'
continue
print '(n, k) = (%s,%s)' % (n, k)
sleep(2)
functions = Functions()
solutions = {}
db = DB()
functions.load_solution(n, k, solutions, db)
functions.print_results(n, k, time.time(), None, solutions, True)
if option == 31:
try:
n = input('Select N: ')
k = input('Select K: ')
k_extend = input('Select K_extend: ')
w = input('Select W: ')
class_tree = raw_input('Class (-1 no class): ')
class_tree = tuple([int(e) for e in class_tree.split(',')])
except:
print 'N, K, K_extend, W must be integers > 0'
continue
print '(n, k, k_extend, w) = (%s,%s,%s,%s)' % (n, k, k_extend, w)
sleep(2)
class BooleanFunction():
def __init__(self, n, k, i):
self.N = n
self.K = k
self.I = i
self.db = DB()
self.functions = Functions()
self.boolean_functions = {self.functions.hash((0, 0)): [0, 0],
self.functions.hash((1, -1)): [0, 1],
self.functions.hash((1, 1)): [1, 0],
self.functions.hash((2, 0)): [1, 1]}
def solve(self):
db = self.db
sols_n_k = db.load(n=self.N, k=self.K, type='solution')
if self.I is not None:
sol_n_k = sols_n_k.keys()[0]
count = sols_n_k[sol_n_k]
if self.I >= count:
print "The index is bigger than the maximum allowed..."
return
current_count = 0
for pedigree_sol in db.load(n=self.N, hash=self.functions.hash(sol_n_k), type='pedigree'):
hash_left = pedigree_sol[0]
hash_right = pedigree_sol[1]
if n - 1 == 1:
result = self.boolean_functions[hash_left] + self.boolean_functions[hash_right]
print ' '.join([str(e) for e in result])
return
class_left, count_left = db.getClass(hash_left)
class_right, count_right = db.getClass(hash_right)
current_count += count_left*count_right
if current_count > self.I:
self.I -= current_count - count_left*count_right
break
index_left = self.I / count_right
index_right = self.I - (index_left * count_right)
result = self.get_boolean_function(self.N-1, hash_left, index_left) + self.get_boolean_function(self.N-1, hash_right, index_right)
print ' '.join([str(e) for e in result])
else:
for sol_n_k, count in sols_n_k.iteritems():
result = []
for pedigree_struct in db.load(n=self.N, hash=self.functions.hash(sol_n_k), type='pedigree'):
class_left, count_left = db.getClass(pedigree_struct[0])
class_right, count_right = db.getClass(pedigree_struct[1])
for i in range(0, count_left):
for j in range(0, count_right):
lst_left = self.get_boolean_function(self.N-1, pedigree_struct[0], i)
lst_right = self.get_boolean_function(self.N-1, pedigree_struct[1], j)
result.append(lst_left + lst_right)
for current_result in result:
print ' '.join([str(e) for e in current_result])
if len(result) > 0:
print "Size of result %s" % len(result)
def get_boolean_function(self, n, hash, index):
if n == 1:
return self.boolean_functions[hash]
current_count = 0
for pedigree_sol in self.db.load(n=n, hash=hash, type='pedigree'):
hash_left = pedigree_sol[0]
hash_right = pedigree_sol[1]
class_left, count_left = self.db.getClass(hash_left)
class_right, count_right = self.db.getClass(hash_right)
current_count += count_left*count_right
if current_count > index:
index -= current_count - count_left*count_right
break
index_left = index / count_right
index_right = index - (index_left * count_right)
return self.get_boolean_function(n-1, hash_left, index_left) + self.get_boolean_function(n-1, hash_right, index_right)
def get_all_boolean_functions(self, n, hash):
if n == 1:
return [self.boolean_functions[hash]]
result = []
for pedigree_struct in self.db.load(hash=hash, n=n, type='pedigree'):
new_hash_left = pedigree_struct[0]
new_hash_right = pedigree_struct[1]
lst_left = self.get_all_boolean_functions(n-1, new_hash_left)
lst_right = self.get_all_boolean_functions(n-1, new_hash_right)
for left in lst_left:
for right in lst_right:
result.append(left + right)
return result
class Problem():
def __init__(self, n, k):
if n <= 0 or k <= 0:
print "N and K must be integers > 0."
exit_program()
self.N = n
self.K = k
self.k_extend = 1
self.functions = Functions()
self.classes = {}
self.correlation = {}
self.solution = {}
self.partialClass = {}
self.pedigree = {}
self.allCalculated = {}
self.start_time = 0
MB = 1024*1024
self.limit_of_partial_in_memory = 1024 # xMB
self.batch_length = 100
self.db = DB()
self.init_classes()
try:
self.functions.save(1, 1, self.k_extend, self.correlation, self.solution,
self.classes, self.partialClass,
self.allCalculated, self.db)
self.functions.save_pedigree(self.pedigree, self.db)
except:
print 'The info its already saved'
def init_classes(self):
##############################
# THE CLASSES
##############################
#classes[n][k_extend][w][hash] = {class: counter}
self.classes[1] = {}
self.classes[1][1] = {}
self.classes[1][1][0] = {self.functions.hash((0, 0)): {(0, 0): 1}}
self.classes[1][1][1] = {self.functions.hash((1, -1)): {(1, -1): 1}}
self.classes[1][1][1].update({self.functions.hash((1, 1)): {(1, 1): 1}})
self.classes[1][1][2] = {self.functions.hash((2, 0)): {(2, 0): 1}}
##############################
#Information about class generation
##############################
#pedigree[n][k_extend][hash] = [(class_left, class_right)]
self.pedigree[1] = {}
self.pedigree[1][1] = {}
self.pedigree[1][1][self.functions.hash((0, 0))] = [(None, None)]
self.pedigree[1][1][self.functions.hash((1, -1))] = [(None, None)]
self.pedigree[1][1][self.functions.hash((1, 1))] = [(None, None)]
self.pedigree[1][1][self.functions.hash((2, 0))] = [(None, None)]
##############################
# CORRELATION INMUNE
##############################
#correlation[n][k][k_extend][w][hash] = {class: counter}
self.correlation[1] = {}
self.correlation[1][1] = {}
self.correlation[1][1][1] = {}
self.correlation[1][1][1][0] = {self.functions.hash((0, 0)): {(0, 0): 1}}
self.correlation[1][1][1][2] = {self.functions.hash((2, 0)): {(2, 0): 1}}
##############################
# SOLUTIONS
##############################
#solucion[n][k] = {class: counter}
self.solution[1] = {}
self.solution[1][1] = {(2, 0): 1}
##############################
# PARTIALCLASS
##############################
#partialClass[N][k][k_extend][W][partial_class] = [ids]
self.partialClass[1] = {}
self.partialClass[1][1] = {}
self.partialClass[1][1][1] = {}
for w in self.classes[1][1]:
self.partialClass[1][1][1][w] = {}
for hash in self.classes[1][1][w]:
for c0, _ in self.classes[1][1][w][hash].iteritems():
partial_class = (c0[1],)
if partial_class in self.partialClass[1][1][1][w] and self.functions.hash(c0) not in self.partialClass[1][1][1][w][partial_class]:
self.partialClass[1][1][1][w][partial_class].append(self.functions.hash(c0))
else:
self.partialClass[1][1][1][w][partial_class] = [self.functions.hash(c0)]
##############################
# CLASSESS CALCULATED AT ALL
##############################
#allCalculated[N][k_extend][W] = true
self.allCalculated[1] = {}
self.allCalculated[1][1] = {}
self.allCalculated[1][1].update({0: True})
self.allCalculated[1][1].update({1: True})
self.allCalculated[1][1].update({2: True})
def get_all(self, n, weight):
res = []
k_extend_max = self.k_extend if self.k_extend <= n else n
try:
if n > 0 and (n not in self.classes or weight not in self.classes[n][k_extend_max]):
self.functions.load_data(n, 1, k_extend_max, self.correlation, self.solution, self.classes,
self.partialClass, self.db, weight)
if weight <= 2**n and (weight not in self.classes[n][k_extend_max] or not self.functions.is_calculated(n, weight, self.db, k_extend_max)):
self.get_all_classes(n, weight)
elif weight not in self.classes[n][k_extend_max]:
return []
if n not in self.classes:
self.functions.load_classes(n, weight, k_extend_max, self.classes, self.db)
res = [(i, self.classes[n][k_extend_max][weight][hashclass][i]) for hashclass in self.classes[n][k_extend_max][weight]
for i in self.classes[n][k_extend_max][weight][hashclass]]
self.functions.clean_structures(self.classes, self.pedigree, self.partialClass, self.correlation, less_or_equal=n)
except:
import traceback
traceback.print_exc()
return res
def get_all_classes(self, n, weight, find_corr=False):
if n == 0:
return
k_extend_max = self.k_extend if self.k_extend <= n else n
f = self.functions
# verify if i have the data in memory, if not i will try to obtain the data from db
if f.is_calculated(n, weight, self.db, k_extend_max) and (n not in self.classes or weight not in self.classes[n][k_extend_max]):
f.load_data(n, 1, k_extend_max, self.correlation, self.solution, self.classes, self.partialClass, self.db, weight)
return
f.remove_classes(self.classes, n)
# if i do not have the data in memory neither in db, i have to calculate
if n not in self.classes or weight not in self.classes[n][k_extend_max]:
print 'Calculating for N = %s and weight = %s' % (n, weight)
for w in xrange(0, weight+1):
print "Crossing All Classes of weight %s VS of weight %s --> N = %s" % (w, weight-w, n-1)
ind = 0
for c_0, count in self.get_all(n-1, w):
for c_1, count_aux in self.get_all(n-1, weight-w):
new_class = f.new_class(c_0, c_1, n, k_extend_max)
new_weight = new_class[0]
f.refresh_pedigree(n, k_extend_max, new_class, c_0, c_1, self.pedigree)
f.refresh_mirror_classes(n, k_extend_max, new_weight, new_class, self.classes, self.partialClass)
f.refresh_classes(n, k_extend_max, new_weight, new_class, count, count_aux, self.classes)
if find_corr and f.is_correlation_immune(new_class, n, 1):
f.add_correlation(n, 1, k_extend_max, new_weight, new_class, count * count_aux, self.correlation)
ind += 1
if n not in self.allCalculated:
self.allCalculated[n] = {}
if k_extend_max not in self.allCalculated[n]:
self.allCalculated[n][k_extend_max] = {}
self.allCalculated[n][k_extend_max].update({weight: True})
print "END :: Crossing All Classes of weight %s (N=%s)" % (weight, n-1)
self.functions.save(n, 1, k_extend_max, self.correlation, self.solution, self.classes, self.partialClass, self.allCalculated,
self.db, delete=True) # LEAVE delete IN FALSE!
self.functions.save_pedigree(self.pedigree, self.db)
def get_all_k_correlation(self, n, weight, k_corr):
res = []
k_extend_max = self.k_extend if self.k_extend <= n else n
try:
if k_corr == 0:
self.get_all_classes(n, weight)
self.functions.load_classes(n, weight, k_extend_max, self.classes, self.db)
res = [(i, self.classes[n][k_extend_max][weight][hashclass][i]) for hashclass in self.classes[n][k_extend_max][weight]
for i in self.classes[n][k_extend_max][weight][hashclass]]
return res
elif k_corr == 1:
n_iter = n - 1 if n > 1 else 1
self.get_all_classes(n_iter, weight/2)
self.generate_k_corr(n, weight, k_corr)
elif k_corr > 1:
self.get_all_k_correlation(n-1, weight/2, k_corr-1)
self.generate_k_corr(n, weight, k_corr)
self.functions.load_correlation(n, k_corr, k_extend_max, self.correlation, self.db, weight)
if weight in self.correlation[n][k_corr][k_extend_max]:
res = [(i, self.correlation[n][k_corr][k_extend_max][weight][hashclass][i]) for hashclass in self.correlation[n][k_corr][k_extend_max][weight]
for i in self.correlation[n][k_corr][k_extend_max][weight][hashclass]]
self.functions.clean_structures(self.classes, self.pedigree, self.partialClass, self.correlation)
except:
import traceback
traceback.print_exc()
return res
def generate_k_corr(self, n, weight, k_corr):
if n==3 and weight==4 and k_corr==2:
pass
k_extend_max = self.k_extend if self.k_extend <= n else n
k_extend_max_load = self.k_extend if self.k_extend <= n - 1 else n - 1
f = self.functions
f.clean_structures(self.classes, self.pedigree, self.partialClass, self.correlation)
if k_corr > 1:
f.load_correlation(n-1, k_corr-1, k_extend_max_load, self.correlation, self.db, weight/2)
if weight/2 not in self.correlation[n-1][k_corr-1][k_extend_max_load]:
return # There is no k_corr for that N(=n-1) and Weight(=weight)
set_all_classes = [(i, self.correlation[n-1][k_corr-1][k_extend_max_load][weight/2][hashclass][i]) for hashclass in self.correlation[n-1][k_corr-1][k_extend_max_load][weight/2]
for i in self.correlation[n-1][k_corr-1][k_extend_max_load][weight/2][hashclass]]
self.correlation[n-1][k_corr-1][k_extend_max_load][weight/2] = {}
else:
f.load_classes(n-1, weight/2, k_extend_max_load, self.classes, self.db)
set_all_classes = [(i, self.classes[n-1][k_extend_max_load][weight/2][hashclass][i]) for hashclass in self.classes[n-1][k_extend_max_load][weight/2]
for i in self.classes[n-1][k_extend_max_load][weight/2][hashclass]]
self.classes[n-1][k_extend_max_load][weight/2] = {}
print 'Generating all %s-corr of weight exactly = %s (N=%s)' % (k_corr, weight, n)
ind = 0
ind_added = 0
size_set = len(set_all_classes)
for c_0, count in set_all_classes:
if ind % 100 == 0:
print '[%s/%s] ---> %s-corr, w=%s, n=%s, Size_Partial=%s' % (ind+1, size_set, k_corr, weight, n, sys.getsizeof(dumps(self.partialClass)))
if ind % self.batch_length == 0:
self.mirror_batch(n, k_corr, k_extend_max, set_all_classes, ind, weight)
for mirror, count_aux in self.find_mirror(c_0, n-1, k_corr):
new_class = f.new_class(c_0, mirror, n, k_extend_max)
if f.is_correlation_immune(new_class, n, k_corr):
ind_added += 1
new_weight = new_class[0]
f.refresh_pedigree(n, k_extend_max, new_class, c_0, mirror, self.pedigree)
f.refresh_mirror_classes(n, k_extend_max, new_weight, new_class, self.classes, self.partialClass)
f.refresh_classes(n, k_extend_max, new_weight, new_class, count, count_aux, self.classes)
f.add_correlation(n, k_corr, k_extend_max, new_weight, new_class, count * count_aux, self.correlation)
ind += 1
print 'END:: Generating all %s-corr of weight exactly = %s (N=%s)' % (k_corr, weight, n)
f.save_by_weight(n, weight, k_extend_max, self.db, self.classes, self.correlation, self.partialClass, delete=False)
f.save_pedigree(self.pedigree, self.db)
def get_mirror_partial(self, class_aux, n, k):
top = self.functions.get_top(n, k)
ini = 1
for i in range(1, k):
ini += self.functions.get_top(n, i)
c0 = class_aux[ini:ini+top]
partial_class = []
for elem in c0:
partial_class.append(elem*-1)
return tuple(partial_class)
def find_mirror(self, class_aux, n, k):
k_extend_max = self.k_extend if self.k_extend <= n else n
#class = (2,0,0,0,2,alfa) and exists the mirror (2,0,0,0,-2,beta)
weight = class_aux[0]
partial_class = self.get_mirror_partial(class_aux, n, k)
result = []
if len(partial_class) == 0:
if k > 1:
return [(i, self.correlation[n][k-1][k_extend_max][weight][hashclass][i]) for hashclass in self.correlation[n][k-1][k_extend_max][weight]
for i in self.correlation[n][k-1][k_extend_max][weight][hashclass]]
else:
return [(i, self.classes[n][k_extend_max][weight][hashclass][i]) for hashclass in self.classes[n][k_extend_max][weight]
for i in self.classes[n][k_extend_max][weight][hashclass]]
try:
for hash_class in self.partialClass[n][k][k_extend_max][weight][partial_class]:
try:
if k > 1:
c_aux = self.correlation[n][k-1][k_extend_max][weight][hash_class].keys()[0]
result.append((c_aux, self.correlation[n][k-1][k_extend_max][weight][hash_class][c_aux]))
else:
c_aux = self.classes[n][k_extend_max][weight][hash_class].keys()[0]
result.append((c_aux, self.classes[n][k_extend_max][weight][hash_class][c_aux]))
except:
pass
finally:
return result
def calculate_classes(self, n, k_extend, weight):
self.k_extend = k_extend
self.get_all_classes(n, weight)
self.functions.load_classes(n, weight, k_extend, self.classes, self.db)
size_classes = 0
size_functions = 0
for hash in self.classes[n][k_extend][weight]:
aux_class = self.classes[n][k_extend][weight][hash].keys()[0]
count = self.classes[n][k_extend][weight][hash][aux_class]
print '#: %s - class: %s' % (count, aux_class)
size_classes += 1
size_functions += count
print 'Size of classes: %s' % size_classes
print 'Size of functions: %s\n\n' % size_functions
def calculate_correlation(self, n, k_extend, k, weight):
self.k_extend = k_extend
self.get_all_k_correlation(n, weight, k)
self.functions.load_correlation(n, k, k_extend, self.correlation, self.db, weight)
size_correlations = 0
size_functions = 0
string_out = ''
for hash in self.correlation[n][k][k_extend][weight]:
aux_class = self.correlation[n][k][k_extend][weight][hash].keys()[0]
count = self.correlation[n][k][k_extend][weight][hash][aux_class]
current_out = '#: %s - correlation: %s' % (count, aux_class)
print current_out
string_out += current_out + '\n'
size_correlations += 1
size_functions += count
file_out = open('out_n%sk%sk_extend%sw%s.txt' % (n, k, k_extend, weight), 'w')
file_out.write(string_out)
file_out.write('Size of correlations: %s\n' % size_correlations)
print 'Size of correlations: %s' % size_correlations
file_out.write('Size of functions: %s\n\n' % size_functions)
print 'Size of functions: %s\n\n' % size_functions
file_out.close()
def mirror_batch(self, n_iter, k, k_extend, set_classes, ind, weight):
k_extend_max = k_extend if k_extend <= n_iter-1 else n_iter-1
#clean partial structure
if n_iter-1 in self.partialClass:
del self.partialClass[n_iter-1]
#clean correlation structure
if n_iter-1 in self.correlation:
del self.correlation[n_iter-1]
#clean classes structure
if n_iter-1 in self.classes:
del self.classes[n_iter-1]
#get batch of the partials
batch = [self.get_mirror_partial(current_class, n_iter-1, k) for current_class, count in set_classes[ind:ind+self.batch_length]]
#load all the partials of the batch, in mirror query use the partials structure (dont call to bd!!)
self.functions.load_batch_partial_classes(n_iter-1, k, k_extend_max, weight/2, self.partialClass, batch, self.db)
hashes = []
for elem_i in self.partialClass[n_iter-1][k][k_extend_max][weight/2].values():
try:
for hash_i in elem_i:
if hash_i not in hashes:
hashes.append(hash_i)
except:
pass
if k > 1:
if len(hashes) > 0:
#load correlation
self.db.getBatchCorrelation(n_iter-1, k-1, k_extend_max, hashes, self.correlation)
else:
self.functions.load_correlation(n_iter-1, k-1, k_extend_max, self.correlation, self.db, weight/2)
else:
if len(hashes) > 0:
#load classes
self.db.getBatchClasses(n_iter-1, k_extend_max, hashes, self.classes)
else:
self.functions.load_classes(n_iter-1, weight/2, k_extend_max, self.classes, self.db)
del batch
del hashes
def calculate_tree(self, n, k_extend, k, weight, class_tree):
self.k_extend = k_extend
self.calculate_classes(n, k_extend, weight)
self.functions.load_classes(n, weight, k_extend, self.classes,self.db)
if (class_tree != (-1,)):
hash = self.functions.hash(class_tree)
c_aux = self.classes[n][k_extend][weight][hash].keys()[0]
print 'Class: %s ' % (str(c_aux))
self.calculate_tree_class(hash, n, k_extend, weight)
else:
for hash in self.classes[n][k_extend][weight]:
#armado de arbol para cada correlations
c_aux = self.classes[n][k_extend][weight][hash].keys()[0]
print 'Class: %s ' % (str(c_aux))
self.calculate_tree_class(hash, n, k_extend, weight)
def calculate_tree_class(self, hash, n , k_extend, weight):
k_extend_max = k_extend if k_extend <= (n-1) else (n-1)
pedigrees = self.db.load(hash=hash, type='pedigree')
for pedigree in pedigrees:
left = pedigree[0]
right = pedigree[1]
if left is not None and right is not None:
c_left = self.db.getClass(left)[0]
c_right = self.db.getClass(right)[0]
print '%s * %s' % (c_left, c_right)
print '%s ' %(str(c_left))
self.calculate_tree_class(left, n-1, k_extend_max, weight/2)
print '%s ' %(str(c_right))
self.calculate_tree_class(right, n-1, k_extend_max, weight/2)
else:
return
def solve(self, individual=False):
self.start_time = time.time()
f = self.functions
for n_iter in xrange(2, self.N+1):
for k in xrange(1, self.N+1):
if individual:
if not (n_iter == self.N and k == self.K):
continue
else:
if k > n_iter or (n_iter == self.N and k > self.K):
break
self.k_extend = k
elapsed_time = time.time() - self.start_time
#print "EAT: %s seconds" % elapsed_time
find_solution = False
weight = self.db.get_last_weight(n_iter, k)
weight_multiple = 2**k
persist = False
if f.solution_exists(n_iter, k, self.solution, self.db):
f.load_correlation(n_iter, k, self.k_extend, self.correlation, self.db, weight)
f.load_solution(n_iter, k, self.solution, self.db)
find_solution = True
persist = False
while not find_solution:
try:
print ">>>> Weight: %s <-> N = %s, K = %s" % (weight, n_iter, k)
f.clean_structures(self.classes, self.pedigree, self.partialClass, self.correlation)
if f.isCorrelationCalculated(n_iter-1, k-1, k-1, weight/2, self.db):
set_correlations = f.getSolution(n_iter-1, k-1, self.db)
if k <= n_iter-1:
set_correlations = f.calculate_extended_classes(set_correlations.keys()[0], n_iter-1, k-1, k, False,
self.pedigree, self.classes, self.partialClass)
self.functions.clean_structures(self.classes, self.pedigree, self.partialClass, self.correlation, True)
else:
set_correlations = set_correlations.items()
else:
set_correlations = self.get_all_k_correlation(n_iter-1, weight/2, k-1)
ind = 0
ind_added = 0
print "All %s corr of weight %s VS all of weight %s (N=%s)" % (k-1, weight/2, weight/2, n_iter-1)
size_set = len(set_correlations)
f.clean_structures(self.classes, self.pedigree, self.partialClass, self.correlation)
for c_0, count in set_correlations:
if ind % self.batch_length == 0:
print '[%s/%s] ---> %s-corr, w=%s, n=%s, Size_Partial=%s' % (ind+1, size_set, k-1, weight/2, n_iter-1, sys.getsizeof(dumps(self.partialClass)))
if ind % self.batch_length == 0:
self.mirror_batch(n_iter, k, self.k_extend, set_correlations, ind, weight)
for mirror, count_aux in self.find_mirror(c_0, n_iter - 1, k):
new_class = f.new_class(c_0, mirror, n_iter, self.k_extend)
new_weight = new_class[0]
if new_weight > 0 and f.is_correlation_immune(new_class, n_iter, k):
ind_added += 1
f.refresh_pedigree(n_iter, self.k_extend, new_class, c_0, mirror, self.pedigree)
f.refresh_mirror_classes(n_iter, self.k_extend, new_weight, new_class, self.classes, self.partialClass)
f.refresh_classes(n_iter, self.k_extend, new_weight, new_class, count, count_aux, self.classes)
f.add_correlation(n_iter, k, self.k_extend, new_weight, new_class, count * count_aux, self.correlation)
f.add_solution(n_iter, new_class, count * count_aux, self.solution, k)
find_solution = True
persist = True
ind += 1
f.remove_correlation(self.correlation, n_iter-1, k-1)
print "All %s corr of weight %s VS all of weight %s - OK (N=%s)" % (k-1, weight/2, weight/2, n_iter-1)
weight += weight_multiple
except KeyboardInterrupt:
exit(1)
if persist:
f.save(n_iter, k, self.k_extend, self.correlation, self.solution, self.classes,
self.partialClass, self.allCalculated, self.db, delete=False)
f.save_pedigree(self.pedigree, self.db)
f.print_results(n_iter, k, self.start_time, self.correlation, self.solution, True)
print '(n, k) = (%s,%s)' % (n, k)
sleep(2)
BooleanFunction(n, k, None).solve()
elif option == 3:
try:
n = input('Select N: ')
k = input('Select K: ')
except:
print 'N and K must be integers > 0.'
continue
print '(n, k) = (%s,%s)' % (n, k)
sleep(2)
functions = Functions()
solutions = {}
db = DB()
functions.load_solution(n,k,solutions,db)
functions.print_results(n,k,time.time(),None,solutions,True)
if option == 31:
try:
n = input('Select N: ')
k = input('Select K: ')
k_extend = input('Select K_extend: ')
w = input('Select W: ')
class_tree = raw_input('Class (-1 no class): ')
class_tree = tuple([int(e) for e in class_tree.split(',')])
except:
print 'N, K, K_extend, W must be integers > 0'
continue
print '(n, k, k_extend, w) = (%s,%s,%s,%s)' % (n, k, k_extend, w)
sleep(2)