def reduction(self, exp):
xls = Xslxsaver()
xls.create_worksheet(exp)
self.otimizator = Ot()
exp_sorted = sorted(exp, reverse=True)
self.mdegree = exp_sorted[0]
self.max_collum = (2 * exp_sorted[0]) - 1
nr = self._calc_NR(exp_sorted)
self.matrix = self._generate_matrix()
exp_sorted.remove(self.mdegree)
j = 0
for i in range(0, len(exp_sorted)):
self._reduce_first(self.matrix, exp_sorted[i])
xls.save(self.matrix, "step_reduction_" + str(j))
j = j + 1
for i in range(0, nr):
self._reduce_others(self.matrix, exp_sorted)
j = j + 1
xls.save(self.matrix, "step_reduction_" + str(j))
# print "Sem remocoes"
# print_matrix(self.matrix)
# print "Size of Columns ", len(self.matrix)
print "Finish Reducing..."
# self.matrix_copy = copy.deepcopy(self.matrix)
# xls.save(self.matrix, 'Not Optimized_1')
# print_matrix(self.matrix)
# t = self.reduce_matrix(self.mdegree, self.matrix)
# print_matrix(t)
self._remove_repeat(self.matrix)
self.clean(self.matrix)
# xls.save_complete(self.matrix)
self.matrix = self.otimizator.sort(self.matrix)
self.clean(self.matrix)
self.matrix = self.reduce_matrix(self.mdegree, self.matrix)
# print_matrix(self.matrix)
xls.save(self.matrix, "Not Optimized")
self.p, self.matrix = self.otimizator.optimize(self.matrix, self.mdegree, xls)
self._remove_one(self.matrix)
# print_matrix(self.matrix)
row = [-1 for x in xrange(self.mdegree)]
self.matrix.append(row)
count = self._count_xor(self.matrix, self.p)
# count = count + self.countMatchs(otimizator.matches)
xls.save(self.matrix, "Optimized")
# self.p_, self.matrix_copy = otimizator.optimize(self.matrix_copy, self.mdegree, 1)
xls.save_matches(self.p)
# print_matrix(self.matrix)
# print self.p
del self.matrix
return count
def reduction(self,exp):
xls = Xslxsaver()
if self.debug:
xls.create_worksheet(exp)
self.otimizator = Ot()
exp_sorted = sorted(exp, reverse=True)
self.mdegree = exp_sorted[0]
self.max_collum = (2*exp_sorted[0])-1
nr = self._calc_NR(exp_sorted)
self.matrix = self._generate_matrix()
exp_sorted.remove(self.mdegree)
j = 0
for i in range(0,len(exp_sorted)):
self._reduce_first(self.matrix, exp_sorted[i])
if self.debug:
xls.save(self.matrix, 'step_reduction_'+str(j))
j = j+1
for i in range(0,nr):
self._reduce_others(self.matrix,exp_sorted)
j = j+1
if self.debug:
xls.save(self.matrix, 'step_reduction_'+str(j))
self._remove_repeat(self.matrix)
self.clean(self.matrix)
self.matrix = self.otimizator.sort(self.matrix)
self.clean(self.matrix)
self.matrix = self.reduce_matrix(self.mdegree, self.matrix)
if self.debug:
xls.save(self.matrix, 'Not Optimized')
if not self.debug:
self.p, self.matrix = self.otimizator.optimize(self.matrix, self.mdegree)
if self.debug:
self.p, self.matrix = self.otimizator.optimize(self.matrix, self.mdegree, xls, self.debug)
self._remove_one(self.matrix)
#print_matrix(self.matrix)
row = [-1 for x in xrange(self.mdegree)]
self.matrix.append(row)
count = self._count_xor(self.matrix,self.p)
#count = count + self.countMatchs(otimizator.matches)
if self.debug:
xls.save(self.matrix, 'Optimized')
#self.p_, self.matrix_copy = otimizator.optimize(self.matrix_copy, self.mdegree, 1)
if self.debug:
xls.save_matches(self.p)
#print_matrix(self.matrix)
#print self.p
del self.matrix
return count
def reduction(self,exp):
exp_sorted = sorted(exp, reverse=True)
print "Starting... ", exp_sorted
self.nr = self._calc_NR(exp_sorted)
self.otimizator = Ot()
self.mdegree = exp_sorted[0]
self.max_collum = (2*exp_sorted[0])-1
self.nr = self._calc_NR(exp_sorted)
self.matrix = self._generate_matrix()
exp_sorted.remove(self.mdegree)
print "Start Reducing..."
for i in range(0,len(exp_sorted)):
self._reduce_first(self.matrix, exp_sorted[i])
#for i in range(0,nr):
self._reduce_others(self.matrix,exp_sorted)
print "Finish Reducing..."
self._remove_repeat(self.matrix)
print "Finish Remove..."
self.clean(self.matrix)
#xls.save_complete(self.matrix)
self.matrix = self.otimizator.sort(self.matrix)
self.clean(self.matrix)
self.matrix = self.reduce_matrix(self.mdegree, self.matrix)
#print_matrix(self.matrix)
#xls.save(self.matrix, 'Not Optimized')
self.p, self.matrix = self.otimizator.optimize(self.matrix, self.mdegree)
self._remove_one(self.matrix)
#print_matrix(self.matrix)
row = [-1 for x in xrange(self.mdegree)]
self.matrix.append(row)
count = self._count_xor(self.matrix,self.p)
#count = count + self.countMatchs(otimizator.matches)
#xls.save(self.matrix, 'Optimized')
#self.p_, self.matrix_copy = otimizator.optimize(self.matrix_copy, self.mdegree, 1)
#xls.save_matches(self.p)
#print_matrix(self.matrix)
#print self.p
del self.matrix
return count
class ReductionT(object):
def __init__(self, exp):
exp_sorted = sorted(exp, reverse=True)
self.nr = self._calc_NR(exp_sorted)
def reduction(self,exp):
exp_sorted = sorted(exp, reverse=True)
print "Starting... ", exp_sorted
self.nr = self._calc_NR(exp_sorted)
self.otimizator = Ot()
self.mdegree = exp_sorted[0]
self.max_collum = (2*exp_sorted[0])-1
self.nr = self._calc_NR(exp_sorted)
self.matrix = self._generate_matrix()
exp_sorted.remove(self.mdegree)
print "Start Reducing..."
for i in range(0,len(exp_sorted)):
self._reduce_first(self.matrix, exp_sorted[i])
#for i in range(0,nr):
self._reduce_others(self.matrix,exp_sorted)
print "Finish Reducing..."
self._remove_repeat(self.matrix)
print "Finish Remove..."
self.clean(self.matrix)
#xls.save_complete(self.matrix)
self.matrix = self.otimizator.sort(self.matrix)
self.clean(self.matrix)
self.matrix = self.reduce_matrix(self.mdegree, self.matrix)
#print_matrix(self.matrix)
#xls.save(self.matrix, 'Not Optimized')
self.p, self.matrix = self.otimizator.optimize(self.matrix, self.mdegree)
self._remove_one(self.matrix)
#print_matrix(self.matrix)
row = [-1 for x in xrange(self.mdegree)]
self.matrix.append(row)
count = self._count_xor(self.matrix,self.p)
#count = count + self.countMatchs(otimizator.matches)
#xls.save(self.matrix, 'Optimized')
#self.p_, self.matrix_copy = otimizator.optimize(self.matrix_copy, self.mdegree, 1)
#xls.save_matches(self.p)
#print_matrix(self.matrix)
#print self.p
del self.matrix
return count
def returnNR(self):
return self.nr
def reduce_matrix(self, degree, matrix):
#print "printing..."
matrix_copy = [[-1 for x in range(degree)] for x in range(len(matrix))]
for i in xrange(0, len(matrix)):
h = 0
#print i
for j in xrange(degree-1, len(matrix[0])):
matrix_copy[i][h] = matrix[i][j]
h += 1
#print_matrix(matrix_copy)
return matrix_copy
def _count_matchs(self, matches):
count = 0;
for i in matches:
count = count + (len(matches[i])-1)
return count
def _count_xor(self, matrix, p):
rowToWrite = [-1 for x in xrange(self.mdegree)]
row = matrix[0]
for j in range(0,len(row)):
countT = 0
element = row[j]
if element <> NULL:
for l in range(1, len(matrix)):
rowToCompare = matrix[l]
elementToCompare = rowToCompare[j]
if elementToCompare <> NULL or (re.search('[a-zA-Z]', str(elementToCompare)) <> None):
countT = countT + 1;
rowToWrite[j] = countT
matrix.append(rowToWrite)
rowToCalc = matrix[len(matrix)-1]
count = 0
for i in range(0,len(rowToCalc)):
tx = rowToCalc[i]
count = count + tx
count = count + len(p)
#print
return count
def delete(self):
del self.matrix
def clean(self, matrix):
toRemove = []
for m in matrix:
if self.isClean(m):
toRemove.append(m)
for i in toRemove:
matrix.remove(i)
def isClean(self, row):
for i in row:
if i <> NULL:
return False
return True
def _reduce_others(self, matrix, exp):
threads = []
#for i_row in xrange(1, len(matrix)-1):
# t = ThreadRed(matrix[i], exp, self.max_collum)
#print_matrix(matrix)
i = 0
for i_row in xrange(1, len(matrix)-1):
t = ThreadRed(i, matrix[i_row],self.mdegree, exp, self.max_collum,self.nr)
threads.append(t)
i = i + 1
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
for i in t.getMatrix():
matrix.append(i)
self._remove_repeat(matrix)
def _remove_one(self, matrix):
for j in range(1, len(matrix)):
row = matrix[j]
for i in range(self.mdegree-1, len(row)):
valueToCompare = row[i]
if valueToCompare <> NULL:
for m in range(j+1, len(matrix)):
rowToCompare = matrix[m]
toCompare = rowToCompare[i]
if toCompare <> NULL:
if valueToCompare == toCompare:
rowToCompare[i] = NULL;
matrix[m] = rowToCompare
matrix[j] = row
def putColumn(self, j, column, matrix):
for i in xrange(0,len(matrix)):
matrix[i][j] = column[i]
def _column(self, matrix, i):
return [row[i] for row in matrix]
def _remove_repeat(self, matrix):
threads = []
for i in xrange(self.mdegree-1, len(matrix[0])):
thread = ThreadRemove(self._column(matrix, i), i)
threads.append(thread)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
index, column = t.getData()
self.putColumn(index, column, matrix)
print "Finished Remove"
# for j in range(1, len(matrix)):
# row = matrix[j]
# for i in range(self.mdegree-1, len(row)):
# found = False
# valueToCompare = row[i]
# if valueToCompare <> NULL:
# for m in range(j+1, len(matrix)):
# rowToCompare = matrix[m]
# toCompare = rowToCompare[i]
# if toCompare <> NULL:
# if valueToCompare == toCompare:
# rowToCompare[i] = NULL;
# row[i] = NULL;
# found = True;
# matrix[m] = rowToCompare
# if found:
# break
# matrix[j] = row
def _clean_reduced(self, matrix, index):
row = matrix[index]
for j in range(0,self.mdegree-1):
row[j] = NULL
matrix[index] = row
def reduce(self, row, exp):
index = self.max_collum-1;
rowReduced = [-1 for x in xrange(self.max_collum)]
for j in range(self.mdegree-2,-1,-1):
element = row[j]
rowReduced[index - exp] = element
index = index -1
return rowReduced
def _need_to_reduce(self, matrix):
indexOfRows = []
index = (self.max_collum - 1 - self.mdegree);
for i in range(1,len(matrix)):
row = matrix[i]
if row[index] <> NULL:
indexOfRows.append(i)
return indexOfRows
def _reduce_first(self, matrix, exp):
index = self.max_collum-1;
row = [-1 for x in xrange(self.max_collum)]
for j in xrange(self.mdegree-2,-1,-1):
element = matrix[0][j]
row[index - exp] = element
index = index -1
matrix.append(row)
def _calc_NR(self, exp_sorted):
nr = 2
nr = int(math.floor((exp_sorted[0]-2)/(exp_sorted[0]-exp_sorted[1])))+1
print "NR = ", nr
#temp = (exp_sorted[0]+1)/2
#deg = math.floor(temp)
#if exp_sorted[1] > deg:
# nr = 2* (exp_sorted[0] + 1) - exp_sorted[0]
return nr
def _generate_matrix(self):
row = sorted(list(range(0, self.max_collum)), reverse=True)
matrix = [row]
return matrix
class Reduction(object):
def reduction(self, exp):
xls = Xslxsaver()
xls.create_worksheet(exp)
self.otimizator = Ot()
exp_sorted = sorted(exp, reverse=True)
self.mdegree = exp_sorted[0]
self.max_collum = (2 * exp_sorted[0]) - 1
nr = self._calc_NR(exp_sorted)
self.matrix = self._generate_matrix()
exp_sorted.remove(self.mdegree)
j = 0
for i in range(0, len(exp_sorted)):
self._reduce_first(self.matrix, exp_sorted[i])
xls.save(self.matrix, "step_reduction_" + str(j))
j = j + 1
for i in range(0, nr):
self._reduce_others(self.matrix, exp_sorted)
j = j + 1
xls.save(self.matrix, "step_reduction_" + str(j))
# print "Sem remocoes"
# print_matrix(self.matrix)
# print "Size of Columns ", len(self.matrix)
print "Finish Reducing..."
# self.matrix_copy = copy.deepcopy(self.matrix)
# xls.save(self.matrix, 'Not Optimized_1')
# print_matrix(self.matrix)
# t = self.reduce_matrix(self.mdegree, self.matrix)
# print_matrix(t)
self._remove_repeat(self.matrix)
self.clean(self.matrix)
# xls.save_complete(self.matrix)
self.matrix = self.otimizator.sort(self.matrix)
self.clean(self.matrix)
self.matrix = self.reduce_matrix(self.mdegree, self.matrix)
# print_matrix(self.matrix)
xls.save(self.matrix, "Not Optimized")
self.p, self.matrix = self.otimizator.optimize(self.matrix, self.mdegree, xls)
self._remove_one(self.matrix)
# print_matrix(self.matrix)
row = [-1 for x in xrange(self.mdegree)]
self.matrix.append(row)
count = self._count_xor(self.matrix, self.p)
# count = count + self.countMatchs(otimizator.matches)
xls.save(self.matrix, "Optimized")
# self.p_, self.matrix_copy = otimizator.optimize(self.matrix_copy, self.mdegree, 1)
xls.save_matches(self.p)
# print_matrix(self.matrix)
# print self.p
del self.matrix
return count
def reduce_matrix(self, degree, matrix):
# print "printing..."
matrix_copy = [[-1 for x in range(degree)] for x in range(len(matrix))]
for i in xrange(0, len(matrix)):
h = 0
# print i
for j in xrange(degree - 1, len(matrix[0])):
matrix_copy[i][h] = matrix[i][j]
h += 1
# print_matrix(matrix_copy)
return matrix_copy
def _count_matchs(self, matches):
count = 0
for i in matches:
count = count + (len(matches[i]) - 1)
return count
def _count_xor(self, matrix, p):
rowToWrite = [-1 for x in xrange(self.mdegree)]
row = matrix[0]
for j in range(0, len(row)):
countT = 0
element = row[j]
if element <> NULL:
for l in range(1, len(matrix)):
rowToCompare = matrix[l]
elementToCompare = rowToCompare[j]
if elementToCompare <> NULL or (re.search("[a-zA-Z]", str(elementToCompare)) <> None):
countT = countT + 1
rowToWrite[j] = countT
matrix.append(rowToWrite)
rowToCalc = matrix[len(matrix) - 1]
count = 0
for i in range(0, len(rowToCalc)):
tx = rowToCalc[i]
count = count + tx
count = count + len(p)
# print
return count
def delete(self):
del self.matrix
def clean(self, matrix):
toRemove = []
for m in matrix:
if self.isClean(m):
toRemove.append(m)
for i in toRemove:
matrix.remove(i)
def isClean(self, row):
for i in row:
if i <> NULL:
return False
return True
def _reduce_others(self, matrix, exp):
to_reduce = self._need_to_reduce(matrix)
for index in to_reduce:
for e in exp:
reduceRow = self.reduce(matrix[index], e)
matrix.append(reduceRow)
self._clean_reduced(matrix, index)
self._remove_repeat(self.matrix)
matrix = self.clean(matrix)
def _remove_one(self, matrix):
for j in range(1, len(matrix)):
row = matrix[j]
for i in range(self.mdegree - 1, len(row)):
valueToCompare = row[i]
if valueToCompare <> NULL:
for m in range(j + 1, len(matrix)):
rowToCompare = matrix[m]
toCompare = rowToCompare[i]
if toCompare <> NULL:
if valueToCompare == toCompare:
rowToCompare[i] = NULL
matrix[m] = rowToCompare
matrix[j] = row
def _remove_repeat(self, matrix):
for j in range(1, len(matrix)):
row = matrix[j]
for i in range(0, len(row)):
found = False
valueToCompare = row[i]
if valueToCompare <> NULL:
for m in range(j + 1, len(matrix)):
rowToCompare = matrix[m]
toCompare = rowToCompare[i]
if toCompare <> NULL:
if valueToCompare == toCompare:
rowToCompare[i] = NULL
row[i] = NULL
found = True
matrix[m] = rowToCompare
if found:
break
matrix[j] = row
def _clean_reduced(self, matrix, index):
row = matrix[index]
for j in range(0, self.mdegree - 1):
row[j] = NULL
matrix[index] = row
def reduce(self, row, exp):
index = self.max_collum - 1
rowReduced = [-1 for x in xrange(self.max_collum)]
for j in range(self.mdegree - 2, -1, -1):
element = row[j]
rowReduced[index - exp] = element
index = index - 1
return rowReduced
def _need_to_reduce(self, matrix):
indexOfRows = []
index = self.max_collum - 1 - self.mdegree
for i in range(1, len(matrix)):
row = matrix[i]
if row[index] <> NULL:
indexOfRows.append(i)
return indexOfRows
def _reduce_first(self, matrix, exp):
index = self.max_collum - 1
row = [-1 for x in xrange(self.max_collum)]
for j in xrange(self.mdegree - 2, -1, -1):
element = matrix[0][j]
row[index - exp] = element
index = index - 1
matrix.append(row)
def _calc_NR(self, exp_sorted):
nr = 2
nr = int(math.floor((exp_sorted[0] - 2) / (exp_sorted[0] - exp_sorted[1])))
print "NR = ", nr
# temp = (exp_sorted[0]+1)/2
# deg = math.floor(temp)
# if exp_sorted[1] > deg:
# nr = 2* (exp_sorted[0] + 1) - exp_sorted[0]
return nr
def _generate_matrix(self):
row = sorted(list(range(0, self.max_collum)), reverse=True)
matrix = [row]
return matrix