def check_non_bounds_constrained(file):
data = cU.readCSV(file)
data_tensor = cU.cleanData(data)[0]
for c in range(len(data_tensor)):
for d in range(len(data_tensor[0])):
trace = 0
for i in range(len(data_tensor[0][0])):
trace += data_tensor[c][d][i][i]
games_played = 0
games_played += sum(data_tensor[c][d][i])
games_played += sum(column(data_tensor[c][d], i))
if games_played > 1:
return 0
if trace != 0:
return 0
return 1
def learnConstraintsForAllDir(directory, num_teams, output):
start = time.clock()
tag = "Amt_T" + str(num_teams)
ind = 0
for file in glob.glob(directory + '/*.csv'):
data = cU.readCSV(file)
dataTensor, variables = cU.cleanData(data)
lenVar = []
for i in range(len(variables)):
lenVar.append(len(variables[i]))
if ind == 0:
saveConstraintsForAll(dataTensor, variables, ind, output,
tag + str(0))
ind = 1
saveConstraintsForAll(dataTensor, variables, ind, output, tag + str(0))
return time.clock() - start
def learnConstraintsForAll(directory, sampled_files, teamAmt, output):
tag = str(len(sampled_files)) + "Amt_T" + str(teamAmt)
ind = 0
for file in sampled_files:
file2 = (os.path.join(directory, file))
data = cU.readCSV(file2)
dataTensor, variables = cU.cleanData(data)
lenVar = []
for i in range(len(variables)):
lenVar.append(len(variables[i]))
if ind == 0:
saveConstraintsForAll(dataTensor, variables, ind, output,
tag + str(0))
ind = 1
saveConstraintsForAll(dataTensor, variables, ind, output, tag + str(0))
def readBounds(file, num_constrType, num_constr):
data = cU.readCSV(file)
newlist = []
for list2 in data:
if list2 != []:
newlist.append(list2)
# transpose function of the tensor
data_transpose = list(map(list, zip(*newlist)))
# build array of len datatranspose by len datatranspose[0]-1
data_int = np.zeros([len(data_transpose), len(data_transpose[0]) - 1])
for i in range(len(data_transpose)):
for j in range(1, len(data_transpose[i])):
if data_transpose[i][j] != '':
data_int[i, j - 1] = int(data_transpose[i][j])
bounds_tr = np.zeros([len(data_int[0]), num_constrType, num_constr])
for j in range(len(data_int[0])):
k = 0
for i in range(349):
if (i + 1) % 7 != 0:
bounds_tr[j, int(k / 6), k % 6] = data_int[i, j]
k += 1
return bounds_tr.astype(np.int64)
def learnConstraintsForAll(directory,
sampled_files,
teamAmt,
output,
info=None,
bk=False):
tag = str(len(sampled_files)) + "Amt_T" + str(teamAmt)
ind = 0
for file in sampled_files:
file2 = (os.path.join(directory, file))
data = cU.readCSV(file2)
dataTensor, variables = cU.cleanData(data)
lenVar = []
for i in range(len(variables)):
lenVar.append(len(variables[i]))
if ind == 0:
saveConstraintsForAll(dataTensor, variables, ind, output,
tag + str(0))
saveConstraintsForAll(dataTensor, variables, 1, output,
tag + str(0))
else:
saveConstraintsForAll(dataTensor, variables, ind, output,
tag + str(0))
if bk:
strengthgroup = np.zeros([2, teamAmt])
strengthgroup[0] = info
strengthgroup[1] = [int(x == 0) for x in info]
# ind = 0
for f in range(2):
if f == 0:
tmp = info
else:
tmp = [int(x == 0) for x in info]
strengthgroup = np.zeros([teamAmt, int(sum(tmp))])
k = 0
for j in range(len(tmp)):
if tmp[j] == 1:
strengthgroup[j][k] = 1
k += 1
dim = 3 # refers to the home dimension
updated_vars = variables[:]
updated_vars[dim] = [
x for x, y in zip(variables[dim], tmp) if y == 1
]
mat = np.tensordot(dataTensor, strengthgroup, [dim, 0])
if ind == 0:
saveConstraintsForAll(dataTensor=mat,
variables=updated_vars,
indicator=0,
directory=output,
tag=tag + str(0) + str(f))
saveConstraintsForAll(dataTensor=mat,
variables=updated_vars,
indicator=1,
directory=output,
tag=tag + str(0) + str(f))
else:
saveConstraintsForAll(dataTensor=mat,
variables=updated_vars,
indicator=ind,
directory=output,
tag=tag + str(0) + str(f))
ind = 1