import sys
import pytoulbar2
Lines = open(sys.argv[1], 'r').readlines()
N = len(Lines)
Matrix = [[int(e) for e in l.split(' ')] for l in Lines]
symmetric = all([
Matrix[i][j] == Matrix[j][i] for i in range(N) for j in range(N) if j > i
])
K = int(sys.argv[2])
Top = 1 + N * N
Problem = pytoulbar2.CFN(Top)
Problem.Option.verbose = 0 # show toulbar2 statistics
Problem.Option.showSolutions = 3 # output solutions found with variable and value names
# order matrix M variables starting from the main diagonal and moving away progressively
# if input graph is symmetric then keep only the upper triangular matrix of M
for u in range(K):
Problem.AddVariable("M_" + str(u) + "_" + str(u), range(2))
for d in range(K):
for u in range(K):
for v in range(K):
if u != v and (not symmetric or u < v) and abs(u - v) == d:
Problem.AddVariable("M_" + str(u) + "_" + str(v), range(2))
# give node variable names
Var = [(chr(65 + i) if N < 28 else "x" + str(i))
for i in range(N):
token() # skip appearance time
# Times per plane: {earliest landing time, target landing time, latest landing time}
LT.append([token(), token(), token()])
# Penalty cost per unit of time per plane:
# [for landing before target, after target]
PC.append([token(), token()])
# Separation time required after i lands before j can land
ST.append([token() for j in range(N)])
top = 99999
Problem = pytoulbar2.CFN(top)
for i in range(N):
Problem.AddVariable('x' + str(i), range(LT[i][0], LT[i][2] + 1))
for i in range(N):
Problem.AddFunction(
[i],
[PC[i][0] * abs(a - LT[i][1]) for a in range(LT[i][0], LT[i][2] + 1)])
for i in range(N):
for j in range(i + 1, N):
Problem.AddFunction([i, j], [
top * (a + ST[i][j] > b and b + ST[j][i] > a)
for a in range(LT[i][0], LT[i][2] + 1)
for b in range(LT[j][0], LT[j][2] + 1)
])
##########################################################################
# Auxiliary CFN functions for Sudoku : 6 13
##########################################################################
CP_mode = False
grid_number = 13
cgrid = hints[grid_number]
csol = sols[grid_number]
grid = [int(h) for h in cgrid]
# list of row, column and cells variable indices
rows = [[] for _ in range(size)]
columns = [[] for _ in range(size)]
cells = [[] for _ in range(size)]
myCFN = pytoulbar2.CFN(1) if CP_mode else pytoulbar2.CFN(1000000, 6)
# create variables and keep indices in row, columns and cells
for i in range(size):
for j in range(size):
vIdx = myCFN.AddVariable("X" + str(i + 1) + "." + str(j + 1),
range(1, size + 1))
columns[j].append(vIdx)
rows[i].append(vIdx)
cells[(i // par) * par + (j // par)].append(vIdx)
# add the clique constraints on rows, columns and cells
for scope in rows + columns + cells:
addCliqueAllDiff(myCFN, scope, myCFN.GetUB())
# assign/bias variables
def test_1(self):
myCFN = pytoulbar2.CFN(2)
res = myCFN.Solve()
self.assertEqual(res[0],[])
self.assertEqual(res[1],0.0)
self.assertEqual(res[2],1)
theCFN.AddFunction(vp, different)
# Sets the value of variable with index "vIdx" to "value" using a unary function
def setHint(theCFN, vIdx, value):
costs = theCFN.GetUB() * np.ones(size, dtype=np.int64)
costs[value - 1] = 0
theCFN.AddFunction([vIdx], costs)
def printGrid(l):
for i, v in enumerate(l):
print(v, end=(' ' if (i + 1) % size else '\n'))
myCFN = pytoulbar2.CFN(1)
# Sudoku size parameter (typical 3 gives 3*3 x 3*3 grid)
par = 3
size = par * par
# Prefilled grids/solutions from the validation set of the RRN paper (0 meaning unknown)
valid = pd.read_csv("valid.csv.xz", sep=",", header=None).values
hints = valid[:][:, 0]
sols = valid[:][:, 1]
grid = [int(h) for h in hints[0]]
# list of row, column and cells variable indices
rows = [[] for _ in range(size)]
columns = [[] for _ in range(size)]
"""
Test basic pytoulbar2 API.
"""
import sys
import random
random.seed()
import pytoulbar2
# create a new empty cost function network with 2-digit precision and initial upper bound of 100
Problem = pytoulbar2.CFN(100., resolution=2)
# add three Boolean variables and a 4-value variable
x = Problem.AddVariable('x', range(2))
y = Problem.AddVariable('y', range(2))
z = Problem.AddVariable('z', range(2))
u = Problem.AddVariable('u', range(4))
# add random unary cost functions on each variable
Problem.AddFunction([x],
[random.uniform(-99.9, 99.9),
random.uniform(-99.9, 99.9)])
Problem.AddFunction([y],
[random.uniform(-99.9, 99.9),
random.uniform(-99.9, 99.9)])
Problem.AddFunction([z],
[random.uniform(-99.9, 99.9),
random.uniform(-99.9, 99.9)])