def test_sophisticated():
"""make sure the functions we test
all implement the same computation"""
values = some_values()
values_nparray = (numpyarray(values[0]),
numpyarray(values[1]),
values[2])
result = python_abs(*values)
for func in TEST_THESE_ARRAY:
assert result == func(*values)
for func in TEST_THESE_NPARRAY:
assert result == func(*values_nparray)
def possible(self, matrix):
m = numpyarray(matrix)
indices = numpywhere(m == '#')
indice_row = int(indices[0])
indice_col = int(indices[-1])
move_right = indice_col + 1
move_down = indice_row + 1
if int(move_right) >= int(len(matrix[0])):
move_right = len(matrix[0]) - 1
if matrix[indice_row][move_right] == 'x' and matrix[move_down][indice_col] == 'x':
print('\n!!!Fim das jogadas possíveis!!!')
print(' ###### GAME OVER ##### ')
print('')
return True
else:
return False
def move_right(self,matrix):
m = numpyarray(matrix)
indices = numpywhere(m == '#')
indice_row = int(indices[0])
indice_col = int(indices[-1])
move_right = indice_col + 1
if int(move_right) >= int(len(matrix[0])):
move_right = len(matrix[0]) - 1
print('Você não pode andar para a direita\n')
if matrix[indice_row][move_right] != 'x':
matrix[indice_row][indice_col] = 'o'
matrix[indice_row][move_right] = '#'
return matrix
else:
print('Você não pode andar para a direita\n')
return False
def move_down(self,matrix):
#matrix = self.play()
#recebe x e y de onde esta o robo | get robot coord
m = numpyarray(matrix)
indices = numpywhere(m == '#')
indice_row = int(indices[0])
indice_col = int(indices[-1])
move_down = indice_row + 1
if int(move_down) >= int(len(matrix)):
move_down = len(matrix) - 1
print('Você não pode andar para a baixo\n')
if matrix[move_down][indice_col] != 'x':
matrix[indice_row][indice_col] = 'o'
matrix[move_down][indice_col] = '#'
return matrix
else:
print('Você não pode andar para baixo\n')
return False
def some_values_nparray():
d = TEST_WORLDLENGTH // 2
r = TEST_RADIUS
return (numpyarray([randint(-d, d), randint(-d, d)]),
numpyarray([randint(-d, d), randint(-d, d)]),
randint(r))
elif (network2_prediction == ((network_prediction + 1) % 3)):
network2_wins += 1
print("network 2 wins")
print(" network 1 won ", network_wins, " network 2 won ", network2_wins,
" round played ", round_played)
print("Winrate of neural network 1 = ", network_wins / round_played * 100,
"%", " Winrate of neural network 2 = ",
network2_wins / round_played * 100, "%")
for i in range(rounds):
while len(moves) < model_size:
moves.append(random.randint(0, 2))
network_moves = []
network_moves.append(moves)
network_moves = numpyarray(network_moves)
network_moves = py.reshape(
network_moves, (network_moves.shape[0], network_moves.shape[1]))
network_prediction = model.predict(network_moves).argmax()
network_prediction = (network_prediction + 1) % 3
moves.append(network2_prediction)
#moves.append(network_prediction)
while len(moves) > model_size:
del moves[0]
while len(moves2) < model2_size:
moves2.append(random.randint(0, 2))
network2_moves = []
network2_moves.append(moves)
network2_moves = numpyarray(network2_moves)
network2_moves = py.reshape(
# extract array of points
arrdict = {}
errdict = {}
arrdata = []
errdata = []
for key, val in plfiledict.iteritems():
pointlist = []
errlist = []
for k in xrange(1, val.__histo__.GetNbinsX() + 1):
pointlist.append(val.__histo__.GetBinContent(k))
errlist.append(val.__histo__.GetBinError(k))
if len(arrdata) == 0:
for k in xrange(1, val.__data__.GetNbinsX() + 1):
arrdata.append(val.__data__.GetBinContent(k))
errdata.append(val.__data__.GetBinError(k))
arrdict[key] = numpyarray(pointlist, "d")
errdict[key] = numpyarray(errlist, "d")
sampleslist = arrdict.keys()
ks_dataprob = {}
print "\033[34;1mkstest \033[m histo::" + plfiledict.values()[0].histoname
print " |-- kolmogorov-smirnov test"
for i in sampleslist:
ks_dataprob[i] = checkks(arrdict[i], arrdata, opt.icl, opt.verbose)
try:
print " |-- %s and %s emerge from the same distribution? %u " % ("data", i, ks_dataprob[i])
except TypeError:
print " |-- %s and %s emerge from the same distribution? CANNOT SAY ANYTHING " % ("data", i)
print " |-- psi test (0 db is the perfect hypothesis)"
nearestzero = (None, 1.0e10)
psiBoutput = {}
# extract array of points
arrdict = {}
errdict = {}
arrdata = []
errdata = []
for key, val in plfiledict.iteritems():
pointlist = []
errlist = []
for k in xrange(1, val.__histo__.GetNbinsX() + 1):
pointlist.append(val.__histo__.GetBinContent(k))
errlist.append(val.__histo__.GetBinError(k))
if len(arrdata) == 0:
for k in xrange(1, val.__data__.GetNbinsX() + 1):
arrdata.append(val.__data__.GetBinContent(k))
errdata.append(val.__data__.GetBinError(k))
arrdict[key] = numpyarray(pointlist, 'd')
errdict[key] = numpyarray(errlist, 'd')
sampleslist = arrdict.keys()
ks_dataprob = {}
print "\033[34;1mkstest \033[m histo::" + plfiledict.values()[0].histoname
print " |-- kolmogorov-smirnov test"
for i in sampleslist:
ks_dataprob[i] = checkks(arrdict[i], arrdata, opt.icl, opt.verbose)
try:
print " |-- %s and %s emerge from the same distribution? %u " % (
"data", i, ks_dataprob[i])
except TypeError:
print " |-- %s and %s emerge from the same distribution? CANNOT SAY ANYTHING " % (
"data", i)
print " |-- psi test (0 db is the perfect hypothesis)"
def __init__(self,np_array):
from numpy import array as numpyarray
self.np_array = numpyarray(np_array)