def set_heat(self, f):
x = utils.vector(4)
u = utils.vector(2)
func = th.function([], f(0, x, u))
def val(p):
x.set_value(np.asarray([p[0], p[1], 0., 0.]))
return func()
self.heat = val
def set_heat(self, f):
x = utils.vector(4)
u = utils.vector(2)
func = th.function([], f(0, x, u))
def val(p):
x.set_value(np.asarray([p[0], p[1], 0., 0.]))
return func()
self.heat = val
def reset(self):
self.num_iters = 0
self.players_head = [
vector(2 * i, 0) for i in range(self.num_players)
] ### TODO: player init
self.players_body = [{self.players_head[i]}
for i in range(self.num_players)]
self.players_dir = [vector(0, 1) for i in range(self.num_players)]
self.observation = np.zeros(self.board_shape, dtype=np.int16)
self.update_observation()
def __init__(self, T, dyn):
self.dyn = dyn
self.T = T
self.x0 = utils.vector(dyn.nx)
self.u = [utils.vector(dyn.nu) for t in range(self.T)]
self.x = []
z = self.x0
for t in range(T):
z = dyn(z, self.u[t])
self.x.append(z)
self.next_x = th.function([], self.x[0])
def __init__(self, T, dyn):
self.dyn = dyn # dynamiken for systemet
self.T = T # hur manga steg fram den ska kolla
self.x0 = utils.vector(dyn.nx) # state vektorn
self.u = [utils.vector(dyn.nu) for t in range(self.T)] # matris for de nastkommande T stegen
self.x = [] # vektor for alla states
z = self.x0
for t in range(T): # hitta dynamiken for alla tidsteg den ska plannera for
z = dyn(z, self.u[t])
self.x.append(z)
self.next_x = th.function([], self.x[0]) # konverterar grafen av states till en callable object
def __init__(self, T, dyn):
self.dyn = dyn
self.T = T
self.x0 = utils.vector(dyn.nx)
self.u = [utils.vector(dyn.nu) for t in range(self.T)]
self.x = []
z = self.x0
for t in range(T):
z = dyn(z, self.u[t])
self.x.append(z)
self.next_x = th.function([], self.x[0])
def dir4_to_a(self, dirr):
assert (self.n == 4)
a = None
if dirr == vector(1, 0):
a = 0
elif dirr == vector(0, -1):
a = 1
elif dirr == vector(-1, 0):
a = 2
elif dirr == vector(0, 1):
a = 3
return int(a)
def a_to_4dir(self, a):
assert (self.n == 4)
v = None
if a == 0:
v = vector(1, 0)
elif a == 1:
v = vector(0, -1)
elif a == 2:
v = vector(-1, 0)
elif a == 3:
v = vector(0, 1)
return v
def constrain(self, other_item, center):
AB = utils.vector(self.point, other_item.line.point)
n = self.normal
if utils.dot(utils.vector(self.point, center), n) < 0:
n = (-n[0], -n[1])
v_n = utils.dot(other_item.line.direction, n)
AB_n = utils.dot(AB, n)
if v_n > 0:
other_item.k.set_min(-AB_n / v_n)
if v_n < 0:
other_item.k.set_max(-AB_n / v_n)
def __init__(self, Tu, dyn, step_per_u=2):
self.dyn = dyn # dynamiken for systemet
self.Tu = Tu
self.step_per_u = step_per_u
self.Tx = step_per_u*Tu
#self.Tx = Tx # hur manga steg fram den ska kolla
self.x0 = utils.vector(dyn.nx) # state vektorn
self.u = [utils.vector(dyn.nu) for t in range(self.Tu)] # matris for de nastkommande T stegen
self.x = [] # vektor for alla states
z = self.x0
for idx in range(Tu): # hitta dynamiken for alla tidsteg den ska plannera for
for idx_u in range(step_per_u):
z = dyn(z, self.u[idx])
self.x.append(z)
self.next_x = th.function([], self.x[0]) # konverterar grafen av states till en callable object
def move():
"Move pacman and all ghosts."
writer.undo()
writer.write(state['score'])
clear()
if valid(pacman + aim):
pacman.move(aim)
index = offset(pacman)
if tiles[index] == 1:
tiles[index] = 2
state['score'] += 1
x = (index % 20) * 20 - 200
y = 180 - (index // 20) * 20
square(x, y)
up()
goto(pacman.x + 10, pacman.y + 10)
dot(20, 'yellow')
for point, course in ghosts:
if valid(point + course):
point.move(course)
else:
options = [
vector(5, 0),
vector(-5, 0),
vector(0, 5),
vector(0, -5),
]
plan = choice(options)
course.x = plan.x
course.y = plan.y
up()
goto(point.x + 10, point.y + 10)
dot(20, 'red')
update()
for point, course in ghosts:
if abs(pacman - point) < 20:
return
ontimer(move, 100)
def move():
"Update object positions."
bird.y -= 5
for ball in balls:
ball.x -= 3
if randrange(10) == 0:
y = randrange(-199, 199)
ball = vector(199, y)
balls.append(ball)
while len(balls) > 0 and not inside(balls[0]):
balls.pop(0)
if not inside(bird):
draw(False)
return
for ball in balls:
if abs(ball - bird) < 15:
draw(False)
return
draw(True)
ontimer(move, 50)
def add_item(self, item):
u = utils.vector(self.point, item.point)
direction = utils.orthogonal(u)
center = (.5 * (self.point[0] + item.point[0]),
.5 * (self.point[1] + item.point[1]))
line = shapes.InfiniteLine(center, direction)
new_item = OtherItem(item, line)
keep_new_item = True
others_to_remove = set()
for other in self.others:
other.line.constrain(new_item, self.point)
new_item.line.constrain(other, self.point)
if not other.is_valid():
others_to_remove |= {other}
if not new_item.is_valid():
keep_new_item = False
break
self.others -= others_to_remove
if keep_new_item:
self.others |= {new_item}
return new_item
return None
def __init__(self, id, tam_agente, cor=None):
# add uma identificação única pro agente
self._id = id
self._tam_agente = tam_agente
# add uma tartaruga específica pro agente
self._turtle = Turtle()
self._turtle.hideturtle()
# define a cor do agente
self._cor = cor
# REQ
# deve definir a cor do agente aleatoriamente (verde, vermelho, rosa, laranja e marrom)
# se não for passado no construtor
# é um gerador de percursos
self._waze = None
# add os seguintes comandos
# TODO: Conferir estas direções
# vector(1, 0) => direita
# vector(-1, 0) => esquerda
# vector(0, 1) => cima
# vector(0, -1) => baixo
self.direcao = vector(1, 0) # este vector significa direita
def prox_passo(self):
""" Obtém o próximo passo do agente na direção em que se encontra """
dir_x = self.direcao[0] * self.tam_passo
dir_y = self.direcao[1] * self.tam_passo
passo = vector(dir_x, dir_y)
return passo
def run_irl(world, car, reward, theta, data):
def gen():
for point in data:
for c, x0, u in zip(world.cars, point['x0'], point['u']):
c.traj.x0.set_value(x0)
for cu, uu in zip(c.traj.u, u):
cu.set_value(uu)
yield
r = car.traj.reward(reward)
g = utils.grad(r, car.traj.u)
H = utils.hessian(r, car.traj.u)
I = tt.eye(utils.shape(H)[0])
reg = utils.vector(1)
reg.set_value([1e-1])
H = H - reg[0] * I
L = tt.dot(g, tt.dot(tn.MatrixInverse()(H), g)) + tt.log(tn.Det()(-H))
for _ in gen():
pass
optimizer = utils.Maximizer(L, [theta],
gen=gen,
method='gd',
eps=0.1,
debug=True,
iters=1000,
inf_ignore=10)
optimizer.maximize()
print theta.get_value()
def item_that_contains(self, point):
for point_item in self.point_items:
if not point_item.is_bounded():
continue
skip = False
for other in point_item.others:
edge_normal = utils.orthogonal(other.line.direction)
A, B = other.vertices()
AO = utils.vector(A, point_item.point)
AP = utils.vector(A, point)
if utils.dot(AO, edge_normal) * utils.dot(AP, edge_normal) < 0:
skip = True
break
if not skip:
return point_item
raise Exception("PointSet.item_that_contains: not found")
def __init__(self, *args, **vargs):
SimpleOptimizerCar.__init__(self, *args, **vargs)
self.social_u = utils.vector(2)
self.l = utils.scalar()
self.watching = []
self.copyx = None
self.copyu = None
self.l_default = 3
def mudar_direcao_aleatoriamente(self):
""" Escolhe alguma direção aleatoriamente que não seja a atual """
# REQ implementar o método
""" k1 = [
vector(1, 0), # este vector significa direita
vector(-1, 0), # esquerda
vector(0, 1), # cima
vector(0, -1), # baixo
]
teste=np.random.choice(k1)
print(teste)
"""
passo = [vector(1, 0), vector(-1, 0), vector(0, 1), vector(0, -1)]
random = choice(passo)
return random
def move(LatLon):
global old_pos, init, old_bearing, mesh, bearings, tractor
utm_pos = Coordinate(x=LatLon.x, y=LatLon.y)
if init is False:
tractor.m_p_s = 2
tractor.run()
init = True
old_pos = utm_pos
scene.camera.pos = vec(utm_pos.x, 4, utm_pos.y - 50)
scene.camera.axis = vec(0, -4, -50)
obj = []
for x in range(-50, 50):
for y in range(-50, 50):
a = Coordinate(x=utm_pos.x + x * 500,
y=utm_pos.y + y * 500 + 500)
b = Coordinate(x=utm_pos.x + x * 500 + 500,
y=utm_pos.y + y * 500 + 500)
c = Coordinate(x=utm_pos.x + x * 500 + 500,
y=utm_pos.y + y * 500)
d = Coordinate(x=utm_pos.x + x * 500, y=utm_pos.y + y * 500)
Q = quad(
canvas=None,
v0=vertex(pos=vec(a.x, -1, a.y)),
v1=vertex(pos=vec(b.x, -1, b.y)),
v2=vertex(pos=vec(c.x, -1, c.y)),
v3=vertex(pos=vec(d.x, -1, d.y)),
)
obj.append(Q)
compound(obj, canvas=scene, color=vec(0, .60, 0))
else:
curso = utils.bearing(utm_pos, old_pos)
a = utils.offset(utm_pos, curso - 90, distance / 2)
b = utils.offset(utm_pos, curso + 90, distance / 2)
c = utils.offset(old_pos, old_bearing + 90, distance / 2)
d = utils.offset(old_pos, old_bearing - 90, distance / 2)
Q = quad(
v0=vertex(pos=vec(a.x, .5, a.y), color=color.yellow),
v1=vertex(pos=vec(b.x, .5, b.y), color=color.yellow),
v2=vertex(pos=vec(c.x, .5, c.y), color=color.yellow),
v3=vertex(pos=vec(d.x, .5, d.y), color=color.yellow),
)
qobj.append(Q)
left.append(vec(a.x, .5, a.y))
right.append(vec(b.x, .5, b.y))
old_bearing = curso
old_pos = utm_pos
bearings.pop()
bearings.insert(0, curso)
curso = sum(bearings) / len(bearings)
camera = utils.offset(utm_pos, curso, 300)
x, z = utils.vector(curso + 180)
#scene.camera.pos=vec(camera.x, 100, camera.y)
#scene.camera.axis=200*vec(x, -0.3 , z)
tractor_3d.pos = tractor.get_vec(1.5)
def hard_coded_policy(ob, head, a, board_shape, A_space, eps=0.5):
"""
head = np.array [y, x]
"""
def valid(pos):
if pos.y >= board_shape[0] or pos.y < 0:
return False
if pos.x >= board_shape[1] or pos.x < 0:
return False
if ob[pos.y, pos.x] != 0:
return False
return True
head = vector(head[1], head[0])
forward = head + A_space.a_to_4dir(a)
sample = np.random.random()
if valid(forward) and sample > eps:
selected = forward
else:
possible = []
right = head + A_space.a_to_4dir(0)
down = head + A_space.a_to_4dir(1)
left = head + A_space.a_to_4dir(2)
up = head + A_space.a_to_4dir(3)
if valid(up): possible.append(up)
if valid(down): possible.append(down)
if valid(left): possible.append(left)
if valid(right): possible.append(right)
possible = np.array(possible)
try:
selected = possible[
np.random.randint(0, high=possible.shape[0]), :]
except ValueError:
selected = forward
return A_space.dir4_to_a(vector(selected[0], selected[1]) - head)
def _k_intersect(self, other):
u2 = utils.dot(self.direction, self.direction)
v2 = utils.dot(other.direction, other.direction)
uv = utils.dot(self.direction, other.direction)
AC = utils.vector(self.point, other.point)
AC_u = utils.dot(AC, self.direction)
AC_v = utils.dot(AC, other.direction)
lower = u2 * v2 - uv * uv
if lower == 0:
return None
return (AC_u * v2 - AC_v * uv) / lower
def run_irl(world, car, reward, theta, data):
def gen():
for point in data:
for c, x0, u in zip(world.cars, point['x0'], point['u']):
c.traj.x0.set_value(x0)
for cu, uu in zip(c.traj.u, u):
cu.set_value(uu)
yield
r = car.traj.reward(reward)
g = utils.grad(r, car.traj.u)
H = utils.hessian(r, car.traj.u)
I = tt.eye(utils.shape(H)[0])
reg = utils.vector(1)
reg.set_value([1e-1])
H = H-reg[0]*I
L = tt.dot(g, tt.dot(tn.MatrixInverse()(H), g))+tt.log(tn.Det()(-H))
for _ in gen():
pass
optimizer = utils.Maximizer(L, [theta], gen=gen, method='gd', eps=0.1, debug=True, iters=1000, inf_ignore=10)
optimizer.maximize()
print theta.get_value()
class Properties:
width = 600
height = 600
time = 0
last_clear_time = 0
running = True
bullets = []
enemies = []
timeline = []
me = vector(300, 500)
meObj = pygame.Surface((10, 10))
meObj.fill((
255,
255,
255,
))
pygame.draw.rect(meObj, (
0,
255,
0,
), (0, 0, 10, 10), 10)
def init_board(self):
ob = np.zeros(self.board_shape, dtype=np.int16)
head_board = np.zeros(self.board_shape, dtype=np.int16)
snakes = []
mid_height = int(self.board_shape[0] / 2)
for i in range(self.num_players):
# init each snake with length init_len
x = int((i + 1) * self.board_shape[1] / (self.num_players + 1))
init_vecs = [
vector(x, y)
for y in range(mid_height - self.init_len + 1, mid_height + 1)
]
snakes.append(deque(init_vecs))
for vec in init_vecs:
ob[vec.y, vec.x] = i + 1
head_board[init_vecs[-1].y, init_vecs[-1].x] = i + 1
return ob, head_board, snakes
def get_moves_old(game_map, turns, pid, training=False, graph=False):
w = game_map.width
h = game_map.height
me = game_map.get_me()
out = np.zeros((w, h))
graph_o, all_axes, F, Z = get_gx(game_map, training)
gradU, gradV = np.gradient(Z, axis=(0, 1)) # gradient of func
for idx, ship in enumerate(me.all_ships()):
sx, sy = int(ship.x), int(ship.y)
sv = vector(ship) # vector
# Distance/Magnitude/Norm/Length = np.sqrt(x**2+y**2) = np.sqrt([x,y].dot([x,y])
dx, dy = gradU[sx][sy], gradV[sx][sy] # unit vector of grad @ sx,sy
gm = F.norm(dx, dy)
u, v = -gm * dx, -gm * dy
angle = degrees(np.arctan2(v, u)) % 360
out[sx][sy] = angle
is_last_ship = idx == len(me.all_ships()) - 1
is_my_pid = pid == 0
if is_last_ship and is_my_pid and graph:
plotter(Z, sv, graph_o, turns, pid, w, h)
graph_o.clear()
return out
def get_moves(game_map, turns, pid, training=False, graph=False):
w = game_map.width
h = game_map.height
me = game_map.get_me()
ships = me.all_ships()
out = {}
if graph:
grad_u, grad_v, graph_objs, Func, gridZ = get_gradient(
ships, game_map, graph)
else:
grad_u, grad_v = get_gradient(ships, game_map, graph)
for idx, ship in enumerate(ships):
sx, sy = int(ship.x), int(ship.y)
# Distance/Magnitude/Norm/Length = np.sqrt(x**2+y**2) = np.sqrt([x,y].dot([x,y])
sv = vector(ship)
u, v = grad_u[idx], grad_v[idx]
angle = degrees(np.arctan2(v, u)) % 360
out[sx, sy] = angle
is_last_ship = idx == len(ships) - 1
is_my_pid = pid == 0
if graph and is_last_ship and is_my_pid:
plotter(gridZ, sv, graph_objs, turns, pid, w, h)
graph_objs.clear()
return out
def __init__(self, T, dyn, x0=None):
self.x = [vector(4) if x0 is None else x0]
self.u = [vector(2) for _ in range(T)]
self.dyn = dyn
for t in range(T):
self.x.append(dyn(self.x[t], self.u[t]))
else:
the_car = None
for c in the_world.cars:
if isinstance(c, car.UserControlledCar):
the_car = c
T = the_car.traj.T
train = []
for fname in files:
with open(fname) as f:
us, xs = pickle.load(f)
for t in range(T, len(xs[0])-T, T):
point = {
'x0': [xseq[t-1] for xseq in xs],
'u': [useq[t:t+T] for useq in us]
}
train.append(point)
theta = utils.vector(5)
theta.set_value(np.array([1., -50., 10., 10., -60.]))
r = 0.1*feature.control()
for lane in the_world.lanes:
r = r + theta[0]*lane.gaussian()
for fence in the_world.fences:
r = r + theta[1]*lane.gaussian()
for road in the_world.roads:
r = r + theta[2]*road.gaussian(10.)
r = r + theta[3]*feature.speed(1.)
for car in the_world.cars:
if car!=the_car:
r = r + theta[4]*car.traj.gaussian()
run_irl(the_world, the_car, r, theta, train)
the_car = None
for c in the_world.cars:
if isinstance(c, car.UserControlledCar):
the_car = c
T = the_car.traj.T
train = []
for fname in files:
with open(fname) as f:
us, xs = pickle.load(f)
for t in range(T, len(xs[0]) - T, T):
point = {
'x0': [xseq[t - 1] for xseq in xs],
'u': [useq[t:t + T] for useq in us]
}
train.append(point)
theta = utils.vector(5)
theta.set_value(np.array([1., -50., 10., 10., -60.]))
r = 0.1 * feature.control()
#features, thetas are weights
for lane in the_world.lanes:
r = r + theta[0] * lane.gaussian()
for fence in the_world.fences:
r = r + theta[1] * lane.gaussian()
for road in the_world.roads:
r = r + theta[2] * road.gaussian(10.)
r = r + theta[3] * feature.speed(1.)
for car in the_world.cars:
if car != the_car:
r = r + theta[4] * car.traj.gaussian()
run_irl(the_world, the_car, r, theta, train)
def __init__(
self,
dom: domain.Domain,
num_queries: int,
query_length: int,
num_expectation_samples: int,
include_previous_query: bool,
generate_scenario: bool,
objective_fn: ObjectiveFunctionType,
beta_pref: float,
) -> None:
assert num_queries >= 1, \
"QueryGenerator.__init__: num_queries must be at least 1"
assert query_length >= 1, \
"QueryGenerator.__init__: query_length must be at least 1"
assert num_expectation_samples >= 1, \
"QueryGenerator.__init__: num_expectation_samples must be \
at least 1"
self.domain = dom
self.num_queries = num_queries
self.query_length = query_length
self.num_expectation_samples = num_expectation_samples
self.include_previous_query = include_previous_query
self.generate_scenario = generate_scenario
self.objective_fn = objective_fn
self.beta_pref = beta_pref
# Variable to store the built computation graph. Set in self.optimizer.
self._optimizer = None
# List of variables to optimize.
self._variables: typing.List[tt.TensorVariable] = []
# List of bounds for variables.
self._bounds: typing.Dict[tt.TensorVariable, domain.BoundsType] = {}
self.num_generated_queries = self.num_queries
if self.include_previous_query:
self.num_generated_queries = self.num_queries - 1
# xs[<query>][<time>][<agent>]
self.xs: typing.List[typing.List[typing.List[tt.TensorVariable]]] = []
# us[<query>][<time>][<agent>]
self.us: typing.List[typing.List[typing.List[tt.TensorVariable]]] = []
if self.include_previous_query:
# previous_x0s[<agent>]
self.previous_x0s: typing.List[tt.TensorVariable] = \
[utils.vector(self.domain.state_size,
name="previous_x0s[%d]" % (i))
for i in range(self.domain.num_agents)]
# previous_us[<time>][<agent>]
self.previous_us: typing.List[typing.List[tt.TensorVariable]] = \
[[utils.vector(self.domain.control_size,
name="previous_us[%d][%d]" % (t, i))
for i in range(self.domain.num_agents)]
for t in range(self.query_length)]
# previous_xs[<time>][<agent>]
self.previous_xs: typing.List[tt.TensorVariable] = \
[self.previous_x0s]
for t in range(1, self.query_length):
xs = self.previous_xs[t - 1]
us = self.previous_us[t - 1]
f = self.domain.dynamics_function
self.previous_xs.append(
[f(xs[i], us[i]) for i in range(self.domain.num_agents)])
self.us.append(self.previous_us)
self.xs.append(self.previous_xs)
# x0s[<agent>]
self.x0s = [
utils.vector(self.domain.state_size, name="x0s[%d]" % (i))
for i in range(self.domain.num_agents)
]
# other_us[<time>][<agent>]
self.other_us = [[
utils.vector(self.domain.control_size,
name="other_us[t=%d][agent=%d]" % (t, i))
for i in range(self.domain.num_others)
] for t in range(self.query_length)]
# query_us[<query>][<time>]
self.query_us = [[
utils.vector(self.domain.control_size,
name="query_us[query=%d][t=%d]" % (i, t))
for t in range(self.query_length)
] for i in range(self.num_generated_queries)]
if self.generate_scenario:
for i in range(self.domain.num_agents):
v = self.x0s[i]
self._variables.append(v)
self._bounds[v] = self.domain.state_bounds
for t in range(self.query_length):
for i in range(self.domain.num_others):
v = self.other_us[t][i]
self._variables.append(v)
self._bounds[v] = self.domain.control_bounds
for i in range(self.num_generated_queries):
for t in range(self.query_length):
v = self.query_us[i][t]
self._variables.append(v)
self._bounds[v] = self.domain.control_bounds
for i in range(self.num_generated_queries):
# merged_us[time][agent]
merged_us = []
for t in range(self.query_length):
us_t = [self.query_us[i][t]]
for j in range(self.domain.num_others):
us_t.append(self.other_us[t][j])
merged_us.append(us_t)
self.us.append(merged_us)
query_xs = [self.x0s]
for t in range(1, self.query_length):
xs = query_xs[t - 1]
us = merged_us[t - 1]
f = self.domain.dynamics_function
query_xs.append(
[f(xs[i], us[i]) for i in range(self.domain.num_agents)])
self.xs.append(query_xs)
# The features summed over the trajectory.
self.traj_features_list = [
sum_trajectory_features(
self.domain, self.query_length,
[self.xs[i][t][0] for t in range(self.query_length)],
[self.xs[i][t][1:] for t in range(self.query_length)])
for i in range(self.num_queries)
]
# traj_features is dimension num_queries by num_features
self.traj_features = tt.stack(self.traj_features_list)
# The samples of the weight vector, used to approximate
# the expectation in our objective.
self.w_samples = utils.matrix(self.num_expectation_samples,
self.domain.feature_size,
name="w_samples")
self._objective = self.objective_fn(self.num_queries,
self.num_expectation_samples,
self.w_samples, self.traj_features,
self.beta_pref)
print("Compiling Optimizer")
self.optimizer()
print("Finished Compiling Optimizer")
"""Flappy, game inspired by Flappy Bird.
Exercises
1. Keep score.
2. Vary the speed.
3. Vary the size of the balls.
4. Allow the bird to move forward and back.
"""
from random import *
from turtle import *
from utils import vector
bird = vector(0, 0)
balls = []
def tap(x, y):
"Move bird up in response to screen tap."
up = vector(0, 30)
bird.move(up)
def inside(point):
"Return True if point on screen."
return -200 < point.x < 200 and -200 < point.y < 200
def draw(alive):
def tap(x, y):
"Move bird up in response to screen tap."
up = vector(0, 30)
bird.move(up)
opts = dict(optlist)
#db = shelve.open(args[1] if args[1].endswith('.db') else args[1]+'.db', writeback=True)
N = int(opts.get('-n', 200))
S = int(opts.get('-s', 2000))
P = float(opts.get('-p', 1.))
method = int(opts.get('-m', 0))
if method == 4:
world.avg_case = True
phis = []
'''db['W'] = W
db['N'] = N
db['S'] = S
db['P'] = P
db['method'] = method'''
if method == 2:
f = vector(len(W))
phi = f / tt.maximum(1., f.norm(2))
A = matrix(0, len(W))
y = tt.dot(A, phi)
p = tt.sum(tt.switch(y < 0, 1., 0.))
q = tt.sum(tt.switch(y > 0, 1., 0.))
#obj = tt.minimum(tt.sum(1.-tt.exp(-tn.relu(y))), tt.sum(1.-tt.exp(-tn.relu(-y))))
obj = p * tt.sum(1. - tt.exp(-tn.relu(y))) + q * tt.sum(
1. - tt.exp(-tn.relu(-y)))
optimizer = Maximizer(obj, [f])
if method == 5:
cand_phis = []
for i in range(50):
x = np.random.normal(size=len(W))
cand_phis.append(x / np.linalg.norm(x))
if method == 6:
