def convex_limit(struct_points: np.ndarray) -> int:
"""Given points in order from best to worst,
Returns the length of the maximum initial segment of points such that quasiconvexity is verified."""
points: tp.List[float] = []
d = len(struct_points[0])
if len(struct_points) < 2 * d + 2:
return len(struct_points) // 2
for i in range(0, min(2 * d + 2, len(struct_points)), 2):
points += [struct_points[i]]
hull = ConvexHull(points[:(d + 1)], incremental=True)
num_points = len(hull.vertices)
k = len(points) - 1
for i in range(num_points, len(points)):
# We add the ith point.
hull.add_points(points[i:(i + 1)])
num_points += 1
if len(hull.vertices) != num_points:
return num_points - 1
for j in range(i + 1, len(points)):
# We check that the jth point is not in the convex hull;
# this is ok if the jth point, added in the hull, becomes a vertex.
hull_copy = copy.deepcopy(hull)
hull_copy.add_points(points[j:j + 1])
if len(hull_copy.vertices) != num_points + 1:
return num_points - 1
return k
def free_reward(self, obs):
ch = ConvexHull(np.array(self.points), incremental=True)
area = ch.area
direction = np.array([math.cos(self.theta), math.sin(self.theta)])
velocity = direction * 50
newp = self.point + velocity
ch.add_points(np.array([newp]))
narea = ch.area
return narea - area, newp
def convex_limit(points: np.ndarray) -> int:
"""Given points in order from best to worst,
Returns the length of the maximum initial segment of points such that quasiconvexity is verified."""
d = len(points[0])
hull = ConvexHull(points[:d + 1], incremental=True)
k = min(d, len(points) // 4)
for i in range(d + 1, len(points)):
hull.add_points(points[i:(i + 1)])
if i not in hull.vertices:
k = i - 1
break
return k
def greedy_alg(self):
end = False
oracle_calls = 0
# take an arbitrary element out of F
random.shuffle(self.F)
# label vector of the elements (binary classification {-1, 1}
labels = -1 * np.ones(shape=(self.size_E, 1))
# E labels of initial labelled data
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
if not intersect(self.C_A, self.C_B):
while len(self.F) > 0 and end == False:
# print(len(self.F))
next_point = self.F.pop()
new_hull_C_A = ConvexHull(self.E[self.C_A], 1)
new_hull_C_A.add_points([self.get_point(next_point)], 1)
new_C_A, added = get_points_inside_convex_hull(self.E, new_hull_C_A, self.C_A, self.F + self.C_B,
next_point)
oracle_calls += 1
if not intersect(new_C_A, self.C_B):
self.hull_C_A = new_hull_C_A
self.C_A = new_C_A
self.F = list(set(self.F) - set(added))
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
else:
new_hull_C_B = ConvexHull(self.E[self.C_B], 1)
new_hull_C_B.add_points([self.get_point(next_point)], 1)
new_C_B, added = get_points_inside_convex_hull(self.E, new_hull_C_B, self.C_B, self.F + self.C_A,
next_point)
oracle_calls += 1
if not intersect(self.C_A, new_C_B):
self.hull_C_B = new_hull_C_B
self.C_B = new_C_B
self.F = list(set(self.F) - set(added))
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
else:
return [], [], False
return oracle_calls, labels, True
def squash_down_to_convex_hull(self, M, sim_hull_pts=None):
from scipy.spatial import ConvexHull
ndim = M[0][1].shape[0]
pts = np.empty((len(M) * (2 ** (ndim)), ndim))
i = 0
for (input_range, output_range) in M:
for pt in product(*output_range):
pts[i, :] = pt
i += 1
hull = ConvexHull(pts, incremental=True)
if sim_hull_pts is not None:
hull.add_points(sim_hull_pts)
return hull
def is_stable(sculpture: np.ndarray) -> bool:
"""Given a 'sculpted' NDarray, where number values represent densities and
NaN values represent the areas carved away, determine if, in the orientation
given, whether it will sit stably upon its base.
:param sculpture: NDarray representing a sculpture of variable density material.
"""
# TODO: Complete this function.
# TODO: Add a few good, working Doctests
## 假設sculpture已經是ndarray (scuplture = np.array().reshape())
## if value is nAn -> needs to replace 0 so that center of mass works
sculpture = carve_sculpture_from_density_block(shape_1, marble_block_1)
sculpture = np.nan_to_num(sculpture, 0) # convert nan to zero to make center_of_mass work
center = center_of_mass(sculpture)
if center > 2:
center = center[1:]
## slice the bottom layer ( last item in the array)
sculpture_base = sculpture[-1] # 3d -> 2d -> result a sqare
# nan arrays
sculpture_base = np.argwhere(sculpture_base > 0) # return array of points
# np.transpose(sculpture_base) (?)
# use convexhull 看center of mass有沒有在base裡面
# get ch1
ch1 = ConvexHull(points=sculpture_base, incremental=True)
# summarize_hull_properties ( ch1 )
ch2 = ch1.add_points(center)
# summarize_hull_properties ( ch1 )
# compare ch1 and ch2, if ch1 == ch2 return True, else False
if ch1 == ch2:
return True
def convex_limit(struct_points: np.ndarray) -> int:
"""Given points in order from best to worst,
Returns the length of the maximum initial segment of points such that quasiconvexity is verified."""
points: tp.List[float] = []
d = len(struct_points[0])
if len(struct_points) < 2 * d + 2:
return len(struct_points) // 2
for i in range(0, min(2 * d + 2, len(struct_points)), 2):
points += [struct_points[i]]
hull = ConvexHull(points, incremental=True)
k = len(points)
for i in range(d + 1, len(points)):
hull.add_points(points[i:(i + 1)])
if i not in hull.vertices:
k = i - 1
break
return k
def optimal_alg(self):
time_point = time.time()
oracle_calls = 0
counter = 0
labels = -1 * np.ones(shape=(self.size_E, 1))
outside_points_1 = [x for x in self.convex_A_distances.keys()] + self.C_B
outside_points_2 = [x for x in self.convex_B_distances.keys()] + self.C_A
# add labels
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
if not intersect(self.C_A, self.C_B):
while (len(self.convex_A_distances) > 0 or len(self.convex_B_distances) > 0):
# print(len(self.Set1Distances), len(self.Set2Distances))
added = []
# First set is nearer to nearest not classified point
if self.decide_nearest():
time_point = time_step("Find Neighbour:", time_point)
if len(self.convex_A_distances) > 0:
next_point = self.convex_A_distances.popitem()[0]
new_hull_C_A = ConvexHull(self.E[self.C_A], 1)
new_hull_C_A.add_points([self.get_point(next_point)], 1)
time_point = time_step("Adding Convex Hull E 1:", time_point)
new_C_A, added = get_points_inside_convex_hull(self.E, new_hull_C_A, self.C_A,
outside_points_1)
oracle_calls += 1
time_point = time_step("Getting inside E:", time_point)
# if there is no intersection the point can be added to the first convex set
if not intersect(new_C_A, self.C_B):
time_point = time_step("Intersection Test:", time_point)
self.C_A = new_C_A
self.hull_C_A = new_hull_C_A
for x in added:
# add to labels
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_1 = list(set(outside_points_1) - set(added))
time_point = time_step("Update arrays:", time_point)
# if there is an intersection we have to check if it can be added to the second set
else:
# Test second half space
new_hull_C_B = ConvexHull(self.E[self.C_B], 1)
new_hull_C_B.add_points([self.get_point(next_point)], 1)
new_C_B, added = get_points_inside_convex_hull(self.E, new_hull_C_B, self.C_B,
outside_points_2)
oracle_calls += 1
# the point can be added to the second set,
# if we reach this point the first time all the other E which are classified did not change the optimal margin
if not intersect(self.C_A, new_C_B):
self.C_B = new_C_B
self.hull_C_B = new_hull_C_B
for x in added:
# add to labels
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_2 = list(set(outside_points_2) - set(added))
# the point cannot be added to any set
else:
if next_point in outside_points_1:
outside_points_1.remove(next_point)
if next_point in outside_points_2:
outside_points_2.remove(next_point)
time_point = time_step("Point add Hull:", time_point)
# Second set is nearer to nearest not classified point
else:
time_point = time_step("Find Neighbour:", time_point)
if len(self.convex_B_distances) > 0:
next_point = self.convex_B_distances.popitem()[0]
new_hull_C_B = ConvexHull(self.E[self.C_B], 1)
new_hull_C_B.add_points([self.get_point(next_point)], 1)
new_C_B, added = get_points_inside_convex_hull(self.E, new_hull_C_B, self.C_B,
outside_points_2)
oracle_calls += 1
# we can add the new point to the second, the nearer set
if not intersect(new_C_B, self.C_A):
self.C_B = new_C_B
self.hull_C_B = new_hull_C_B
for x in added:
# add to labels
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_2 = list(set(outside_points_2) - set(added))
# we check if we can add the point to the first set
# if we reach this point the first time all the other E which are classified did not change the optimal margin
else:
# Test first half space
new_hull_C_A = ConvexHull(self.E[self.C_A], 1)
new_hull_C_A.add_points([self.get_point(next_point)], 1)
new_C_A, added = get_points_inside_convex_hull(self.E, new_hull_C_A, self.C_A,
outside_points_1)
oracle_calls += 1
# the point can be classified to the second set
if not intersect(self.C_B, new_C_A):
self.hull_C_A = new_hull_C_A
self.C_A = new_C_A
for x in added:
# add to labels
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_1 = list(set(outside_points_1) - set(added))
# we cannot classify the point
else:
if next_point in outside_points_1:
outside_points_1.remove(next_point)
if next_point in outside_points_2:
outside_points_2.remove(next_point)
time_point = time_step("Point add Hull:", time_point)
else:
return [], [], False
return oracle_calls, labels, True
def r(self, c, gpos, mpos=0.0):
"""
Calculates the virtual distances between grid point locations and
microphone locations or the origin. These virtual distances correspond
to travel times of the sound along a ray that is traced through the
medium.
Parameters
----------
c : float
The speed of sound to use for the calculation.
gpos : array of floats of shape (3, N)
The locations of points in the beamforming map grid in 3D cartesian
co-ordinates.
mpos : array of floats of shape (3, M), optional
The locations of microphones in 3D cartesian co-ordinates. If not
given, then only one microphone at the origin (0, 0, 0) is
considered.
Returns
-------
array of floats
The distances in a twodimensional (N, M) array of floats. If M==1,
then only a onedimensional array is returned.
"""
if isscalar(mpos):
mpos = array((0, 0, 0), dtype=float32)[:, newaxis]
# the DE system
def f1(t, y, v):
x = y[0:3]
s = y[3:6]
vv, dv = v(x)
sa = sqrt(s[0] * s[0] + s[1] * s[1] + s[2] * s[2])
x = empty(6)
x[0:3] = c * s / sa - vv # time reversal
x[3:6] = dot(s, -dv.T) # time reversal
return x
# integration along a single ray
def fr(x0, n0, rmax, dt, v, xyz, t):
s0 = n0 / (c + dot(v(x0)[0], n0))
y0 = hstack((x0, s0))
oo = ode(f1)
oo.set_f_params(v)
oo.set_integrator(
'vode',
rtol=1e-4, # accuracy !
max_step=1e-4 * rmax) # for thin shear layer
oo.set_initial_value(y0, 0)
while oo.successful():
xyz.append(oo.y[0:3])
t.append(oo.t)
if norm(oo.y[0:3] - x0) > rmax:
break
oo.integrate(oo.t + dt)
gs2 = gpos.shape[-1]
gt = empty((gs2, mpos.shape[-1]))
vv = self.ff.v
NN = int(sqrt(self.N))
for micnum, x0 in enumerate(mpos.T):
xe = gpos.mean(1) # center of grid
r = x0[:, newaxis] - gpos
rmax = sqrt((r * r).sum(0).max()) # maximum distance
nv = spiral_sphere(self.N, self.Om, b=xe - x0)
rstep = rmax / sqrt(self.N)
rmax += rstep
tstep = rstep / c
xyz = []
t = []
lastind = 0
for i, n0 in enumerate(nv.T):
fr(x0, n0, rmax, tstep, vv, xyz, t)
if i and i % NN == 0:
if not lastind:
dd = ConvexHull(vstack((gpos.T, xyz)),
incremental=True)
else:
dd.add_points(xyz[lastind:], restart=True)
lastind = len(xyz)
# ConvexHull includes grid if no grid points on hull
if dd.simplices.min() >= gs2:
break
xyz = array(xyz)
t = array(t)
li = LinearNDInterpolator(xyz, t)
gt[:, micnum] = li(gpos.T)
if gt.shape[1] == 1:
gt = gt[:, 0]
return c * gt #return distance along ray
class GoalBabbling():
def __init__(self):
# this simulates cameras and positions
self.cam_sim = Cam_sim("../../rgb_rectified")
self.lock = threading.Lock()
signal.signal(signal.SIGINT, self.Exit_call)
def initialise(self,
goal_size=3,
image_size=64,
channels=1,
batch_size=16,
goal_selection_mode='db',
exp_iteration=0,
hist_size=35,
prob_update=0.1):
self.exp_iteration = exp_iteration
self.goal_size = goal_size
self.image_size = image_size
self.channels = channels
self.samples = []
self.code_size = 32
self.iteration = 0
self.test_data_step = 500
self.test_size = 50
self.max_iterations = 5000
self.history_size = hist_size # how many batches (*batch_size) are kept?
self.history_buffer_update_prob = prob_update # what is the probabilty that a data element in the history buffer is substituted with a new one
self.pos = []
self.cmd = []
self.img = []
self.history_pos = [] # kept to prevent catastrophic forgetting
self.history_img = [] # kept to prevent catastrophic forgetting
self.goal_code = []
self.mse_inv = []
self.mse_fwd = []
self.mse_inv_goal = [] # on goals only
self.mse_fwd_goal = [] # on goals only
self.samples_pos = []
self.samples_img = []
self.samples_codes = []
self.test_positions = []
self.goal_selection_mode = goal_selection_mode # 'som' or 'kmeans'
self.move = False
self.autoencoder, self.encoder, self.decoder = None, None, None
#self.forward_model = None
self.forward_code_model = None
#self.inverse_model = None
self.inverse_code_model = None
self.goal_som = None
self.interest_model = InterestModel(self.goal_size)
self.current_goal_x = -1
self.current_goal_y = -1
self.current_goal_idx = -1
self.cae_epochs = 1
self.inv_epochs = 2
self.fwd_epochs = 2
self.random_cmd_flag = False
self.random_cmd_rate = 0.30
# self.goal_db = ['./sample_images/img_0.jpg','./sample_images/img_200.jpg','./sample_images/img_400.jpg','./sample_images/img_600.jpg','./sample_images/img_800.jpg','./sample_images/img_1000.jpg','./sample_images/img_1200.jpg','./sample_images/img_1400.jpg','./sample_images/img_1600.jpg' ]
self.goal_image = np.zeros(
(1, self.image_size, self.image_size, channels), np.float32)
self.batch_size = batch_size
self.count = 1
self.log_lp = [] # learning progress for each goal
#self.log_lr_inv = [] # learning rate inverse model
self.log_goal_pos = [] # ccurrent goal position (x,y xcarve position)
for i in range(self.goal_size * self.goal_size):
self.log_goal_pos.append([])
self.log_goal_pred = [
] # ccurrent xcarve position (x,y xcarve position)
for i in range(self.goal_size * self.goal_size):
self.log_goal_pred.append([])
self.log_goal_img = [] # ccurrent goal position (x,y xcarve position)
for i in range(self.goal_size * self.goal_size):
self.log_goal_img.append([])
self.log_curr_img = [
] # ccurrent xcarve position (x,y xcarve position)
for i in range(self.goal_size * self.goal_size):
self.log_curr_img.append([])
self.log_goal_id = []
self.log_cvh_inv = []
dataset = '../../rgb_rectified/compressed_dataset.pkl'
print('Loading test dataset ', dataset)
self.train_images, self.test_images, self.train_cmds, self.test_cmds, self.train_pos, self.test_pos = models.load_data(
dataset, self.image_size, step=self.test_data_step)
# load models
self.autoencoder, self.encoder, self.decoder = models.load_autoencoder(
directory='./models/',
code_size=self.code_size,
image_size=self.image_size,
batch_size=self.batch_size,
epochs=self.cae_epochs)
# self.forward_model = models.load_forward_model(train=False)
self.forward_code_model = models.load_forward_code_model(
directory='./models/', code_size=self.code_size, train=False)
#self.inverse_model = models.load_inverse_model(train=False)
self.inverse_code_model = models.load_inverse_code_model(
directory='./models/', code_size=self.code_size, train=False)
if self.goal_selection_mode == 'kmeans':
self.kmeans = models.load_kmeans()
if self.goal_selection_mode == 'som':
self.goal_som = models.load_som(directory='./models/',
encoder=self.encoder,
goal_size=self.goal_size)
# initialise convex hulls (debug stuff)
np.random.seed(
10) # generate always the same random input to the convex hull
pt_inv = []
for i in range(3):
a = np.random.rand(2) * 0.01
pt_inv.append(a)
np.random.seed() # change the seed
self.convex_hull_inv = ConvexHull(np.asarray(pt_inv), incremental=True)
p = Position()
p.x = int(0)
p.y = int(0)
p.z = int(-90)
p.speed = int(1800)
self.prev_pos = p
#self.run_babbling()
def get_current_inv_mse(self):
#motor_pred = self.inverse_model.predict(self.test_images[0:self.test_size])# (self.goal_size*self.goal_size)])
img_codes = self.encoder.predict(self.test_images[0:self.test_size])
motor_pred = self.inverse_code_model.predict(
img_codes) # (self.goal_size*self.goal_size)])
#motor_pred = self.inverse_model.predict(self.test_images[0:self.test_size])# (self.goal_size*self.goal_size)])
# mse = (np.linalg.norm(motor_pred-self.test_pos[0:(self.goal_size*self.goal_size)]) ** 2) / (self.goal_size*self.goal_size)
mse = (np.linalg.norm(motor_pred - self.test_pos[0:self.test_size])**
2) / self.test_size
print('Current mse inverse code model: ', mse)
self.mse_inv.append(mse)
# def get_current_inv_mse_on_goals(self):
# mse = 0
# if self.goal_selection_mode =='db' or self.goal_selection_mode =='random':
# img_codes = self.encoder.predict(self.test_images[0:(self.goal_size*self.goal_size)])
# motor_pred = self.inverse_code_model.predict(img_codes)
# mse = (np.linalg.norm(motor_pred-self.test_pos[0:(self.goal_size*self.goal_size)]) ** 2) / (self.goal_size*self.goal_size)
# print 'Current mse inverse code model on goals: ', mse
# self.mse_inv_goal.append(mse)
def get_current_fwd_mse(self):
#img_pred = self.forward_model.predict(self.test_pos)
#img_pred = self.decoder.predict(self.forward_code_model.predict(self.test_pos))
#img_obs_code = self.encoder.predict(self.test_images[0:(self.goal_size*self.goal_size)])
img_obs_code = self.encoder.predict(self.test_images[0:self.test_size])
#img_pred_code = self.encoder.predict(img_pred)
# img_pred_code = self.forward_code_model.predict(self.test_pos[0:(self.goal_size*self.goal_size)])
img_pred_code = self.forward_code_model.predict(
self.test_pos[0:self.test_size])
#mse = (np.linalg.norm(img_pred_code-img_obs_code) ** 2) / (self.goal_size*self.goal_size)
mse = (np.linalg.norm(img_pred_code - img_obs_code)**
2) / self.test_size
print('Current mse fwd model: ', mse)
self.mse_fwd.append(mse)
def run_babbling(self):
p = Position()
for _ in range(self.max_iterations):
#test_motor_pred = self.inverse_model.predict([self.test_images[0:self.goal_size*self.goal_size]])
#print np.hstack((test_motor_pred,self.test_pos[0:self.goal_size*self.goal_size]))
# record logs and data
self.get_current_inv_mse()
self.get_current_fwd_mse()
#self.log_lr_inv.append(K.eval(self.inverse_model.optimizer.lr))
#print 'iteration opt inv', K.eval(self.inverse_model.optimizer.iteration)
#print 'current lr: ', self.log_lr_inv[-1]
print('Mode ', self.goal_selection_mode, ' hist_size ',
str(self.history_size), ' prob ',
str(self.history_buffer_update_prob), ' iteration : ',
self.iteration)
self.iteration = self.iteration + 1
# if self.iteration > self.max_iterations:
# self.save_models()
# return
# select a goal
self.current_goal_idx, self.current_goal_x, self.current_goal_y = self.interest_model.select_goal(
)
if self.goal_selection_mode == 'kmeans':
self.goal_code = self.kmeans.cluster_centers_[
self.current_goal_idx].reshape(1, self.code_size)
print(' code ', self.goal_code)
elif self.goal_selection_mode == 'db' or self.goal_selection_mode == 'random':
#self.goal_image=np.asarray(cv2.imread(self.goal_db[self.current_goal_idx], 0)).reshape(1, self.image_size, self.image_size, self.channels).astype('float32') / 255
self.goal_image = self.test_images[
self.current_goal_idx].reshape(1, self.image_size,
self.image_size,
self.channels)
self.goal_code = self.encoder.predict(self.goal_image)
elif self.goal_selection_mode == 'som':
self.goal_code = self.goal_som._weights[
self.current_goal_x,
self.current_goal_y].reshape(1, self.code_size)
else:
print('wrong goal selection mode, exit!')
sys.exit(1)
motor_pred = []
if self.goal_selection_mode == 'db':
#cv2.imshow("Goal", cv2.imread(self.goal_db[self.current_goal_idx], 0))
####cv2.imshow("Goal", self.test_images[self.current_goal_idx])
####cv2.waitKey(1)
#motor_pred = self.inverse_model.predict(self.goal_image)
motor_pred = self.inverse_code_model.predict(self.goal_code)
print('pred ', motor_pred, ' real ',
self.test_pos[self.current_goal_idx])
else:
goal_decoded = self.decoder.predict(self.goal_code)
####cv2.imshow("CAE Decoded Goal", goal_decoded[0])
####cv2.waitKey(1)
# motor_pred = self.inverse_model.predict(goal_decoded)
motor_pred = self.inverse_code_model.predict(self.goal_code)
#image_pred = self.forward_model.predict(np.asarray(motor_pred))
image_pred = self.decoder.predict(
self.forward_code_model.predict(np.asarray(motor_pred)))
####cv2.imshow("FWD Model Predicted Image", image_pred[0])
####cv2.waitKey(1)
#image_pred_curr = forward_model.predict(np.asarray(prev_cmd))
#cv2.imshow("FWD Model Predicted Image curr ", image_pred_curr[0])
#cv2.waitKey(1)
# motor_cmd=motor_pred[0]
# p.x = clamp_x(motor_pred[0][0]*x_lims[1])
# p.y = clamp_x(motor_pred[0][1]*y_lims[1])
noise_x = np.random.normal(0, 0.02)
noise_y = np.random.normal(0, 0.02)
p.x = clamp_x((motor_pred[0][0] + noise_x) * x_lims[1])
p.y = clamp_y((motor_pred[0][1] + noise_y) * y_lims[1])
ran = random.random()
if ran < self.random_cmd_rate or (
len(self.history_pos) / self.batch_size
) != self.history_size or self.goal_selection_mode == 'random': # choose random motor commands from time to time
print('generating random motor command')
p.x = random.uniform(x_lims[0], x_lims[1])
p.y = random.uniform(y_lims[0], y_lims[1])
self.random_cmd_flag = True
else:
self.random_cmd_flag = False
print('predicted position ', motor_pred[0], 'p+noise ',
motor_pred[0][0] + noise_x, ' ', motor_pred[0][1] + noise_y,
' clamped ', p.x, ' ', p.y, ' noise.x ', noise_x, ' n.y ',
noise_y)
p.z = int(-90)
p.speed = int(1400)
self.create_simulated_data(p, self.prev_pos)
self.prev_pos = p
if self.iteration % 50 == 0:
#test_codes= self.encoder.predict(self.test_images[0:self.goal_size*self.goal_size].reshape(self.goal_size*self.goal_size, self.image_size, self.image_size, self.channels))
if self.goal_selection_mode == 'db' or self.goal_selection_mode == 'random':
plot_cvh(self.convex_hull_inv,
title=self.goal_selection_mode + '_' +
str(self.exp_iteration) + 'cvh_inv',
iteration=self.iteration,
dimensions=2,
log_goal=self.test_pos[0:self.goal_size *
self.goal_size],
num_goals=self.goal_size * self.goal_size)
elif self.goal_selection_mode == 'kmeans':
goals_pos = self.inverse_code_model.predict(
self.kmeans.cluster_centers_[0:self.goal_size *
self.goal_size].reshape(
self.goal_size *
self.goal_size,
self.code_size))
plot_cvh(self.convex_hull_inv,
title=self.goal_selection_mode + '_' +
str(self.exp_iteration) + 'cvh_inv',
iteration=self.iteration,
dimensions=2,
log_goal=goals_pos,
num_goals=self.goal_size * self.goal_size)
elif self.goal_selection_mode == 'som':
goals_pos = self.inverse_code_model.predict(
self.goal_som._weights.reshape(
len(self.goal_som._weights) *
len(self.goal_som._weights[0]),
len(self.goal_som._weights[0][0])))
plot_cvh(self.convex_hull_inv,
title=self.goal_selection_mode + '_' +
str(self.exp_iteration) + 'cvh_inv',
iteration=self.iteration,
dimensions=2,
log_goal=goals_pos,
num_goals=self.goal_size * self.goal_size)
#test_codes_ipca = self.fwd_ipca.fit_transform(test_codes)
#plot_cvh(self.convex_hull_fwd, title=self.goal_selection_mode+'_'+str(self.exp_iteration)+'cvh_fwd', iteration = self.iteration, dimensions=2, log_goal=test_codes_ipca, num_goals=self.goal_size*self.goal_size)
observation = self.img[-1]
####cv2.imshow("Current observation", observation)
# update competence of the current goal (it is supposed that at this moment the action is finished
if len(self.img) > 0 and (not self.random_cmd_flag
or self.goal_selection_mode == 'random'):
#observation_code = self.encoder.predict(observation.reshape(1, self.image_size, self.image_size, self.channels))
#prediction_error = np.linalg.norm(np.asarray(self.goal_code[:])-np.asarray(observation_code[:]))
cmd = [p.x / float(x_lims[1]), p.y / float(y_lims[1])]
prediction_code = self.forward_code_model.predict(
np.asarray(cmd).reshape((1, 2)))
prediction_error = np.linalg.norm(
np.asarray(self.goal_code[:]) -
np.asarray(prediction_code[:]))
self.interest_model.update_competences(self.current_goal_x,
self.current_goal_y,
prediction_error)
#print 'Prediction error: ', prediction_error, ' learning progress: ', self.interest_model.get_learning_progress(self.current_goal_x, self.current_goal_y)
self.log_lp.append(
np.asarray(deepcopy(
self.interest_model.learning_progress)))
self.log_goal_id.append(self.current_goal_idx)
#print self.log_lp
# fit models
if len(self.img) > self.batch_size:
if len(self.img) == len(self.pos):
# first fill the history buffer, then update the models
if (len(self.history_pos) /
self.batch_size) != self.history_size:
if len(self.history_pos) == 0:
self.history_pos = deepcopy(
self.pos[-(self.batch_size):])
self.history_img = deepcopy(
self.img[-(self.batch_size):])
self.history_pos = np.vstack(
(self.history_pos, self.pos[-(self.batch_size):]))
self.history_img = np.vstack(
(self.history_img, self.img[-(self.batch_size):]))
else:
img_io = []
pos_io = []
if (self.history_size != 0):
for i in range(-(self.batch_size), 0):
#print i
r = random.random()
if r < self.history_buffer_update_prob:
r_i = random.randint(
0,
self.history_size * self.batch_size -
1)
#print 'r_i ', r_i, ' i ', i
self.history_pos[r_i] = deepcopy(
self.pos[i])
self.history_img[r_i] = deepcopy(
self.img[i])
# update models
img_io = np.vstack(
(np.asarray(self.history_img[:]).reshape(
len(self.history_img), self.image_size,
self.image_size, self.channels),
np.asarray(
self.img[-(self.batch_size):]).reshape(
self.batch_size, self.image_size,
self.image_size, self.channels)))
pos_io = np.vstack(
(np.asarray(self.history_pos[:]),
np.asarray(self.pos[-(self.batch_size):])))
else:
img_io = np.asarray(
self.img[-(self.batch_size):]).reshape(
self.batch_size, self.image_size,
self.image_size, self.channels)
pos_io = np.asarray(self.pos[-(self.batch_size):])
#models.train_forward_model_on_batch(self.forward_model, pos_io, img_io, batch_size=self.batch_size, epochs = self.epochs)
codes_io = self.encoder.predict(img_io)
models.train_forward_code_model_on_batch(
self.forward_code_model,
pos_io,
codes_io,
batch_size=self.batch_size,
epochs=self.fwd_epochs)
#models.train_inverse_model_on_batch(self.inverse_model, img_io, pos_io, batch_size=self.batch_size, epochs = self.inv_epochs)
models.train_inverse_code_model_on_batch(
self.inverse_code_model,
codes_io,
pos_io,
batch_size=self.batch_size,
epochs=self.inv_epochs)
#train_autoencoder_on_batch(self.autoencoder, self.encoder, self.decoder, np.asarray(self.img[-32:]).reshape(32, self.image_size, self.image_size, self.channels), batch_size=self.batch_size, cae_epochs=5)
# update convex hulls
obs_codes = self.encoder.predict(
np.asarray(self.img[-(self.batch_size):]).reshape(
self.batch_size, self.image_size, self.image_size,
self.channels))
#print self.pos[-32:]
#print np.asarray(self.pos[-32:]).reshape((32,2))
self.convex_hull_inv.add_points(np.asarray(
self.pos[-(self.batch_size):]).reshape(
((self.batch_size), 2)),
restart=True)
self.log_cvh_inv.append(self.convex_hull_inv.volume)
#print 'conv hull inv. volume: ', self.convex_hull_inv.volume
#print 'a', obs_codes[:]
#self.fwd_ipca.partial_fit(obs_codes[:])
#ipca_codes = self.fwd_ipca.transform(obs_codes[:])
#print 'p', ipca_codes
#print 'p', ipca_codes[:]
#self.convex_hull_fwd.add_points(np.asarray(obs_codes[:]), restart=False)
# this may be inconsistent. IPCA could change over time, so previous points projected for the CVH calculation could change. Check this
#self.convex_hull_fwd.add_points(np.asarray(ipca_codes[:]), restart=True)
#print 'conv hull fwd. volume: ', self.convex_hull_fwd.volume
#self.log_cvh_fwd.append(self.convex_hull_fwd.volume)
else:
print('lenghts not equal')
if not self.random_cmd_flag and len(self.cmd) > 0:
#print 'test pos', self.test_positions[0:10]
test_p = self.test_pos[self.current_goal_idx]
#curr_cmd = self.cmd[-1]
#pred_p = self.inverse_model.predict(self.goal_image)
pred_p = self.inverse_code_model.predict(self.goal_code)
self.log_goal_pos[self.current_goal_idx].append(
[test_p[0], test_p[1]])
#self.log_curr_pos[self.current_goal_idx].append([curr_cmd[0], curr_cmd[1] ])
self.log_goal_pred[self.current_goal_idx].append(
[pred_p[0][0], pred_p[0][1]])
#print 'hg ', self.log_goal_pos
#g_code = self.forward_model.predict(np.asarray(test_p).reshape((1,2)))
#g_code = self.decoder.predict(self.forward_code_model.predict(np.asarray(test_p).reshape((1,2))))
#g_code = self.fwd_ipca.transform(self.forward_code_model.predict(np.asarray(test_p).reshape((1,2))) )
#c_code = self.forward_model.predict(np.asarray(curr_cmd).reshape((1,2)))
#c_code = self.decoder.predict(self.forward_code_model.predict(np.asarray(curr_cmd).reshape((1,2))) )
#c_code = self.fwd_ipca.transform(self.forward_code_model.predict(np.asarray(curr_cmd).reshape((1,2))) )
#print 'g ipca ', g_code
#print 'c ipca ', c_code
#self.log_goal_img[self.current_goal_idx].append([g_code[0][0],g_code[0][1], g_code[0][2],g_code[0][3] ])
#self.log_curr_img[self.current_goal_idx].append([c_code[0][0],c_code[0][1], c_code[0][2],c_code[0][3] ])
#self.log_goal_img[self.current_goal_idx].append([g_code[0][0],g_code[0][1] ])
#self.log_curr_img[self.current_goal_idx].append([c_code[0][0],c_code[0][1] ])
print('Saving models')
self.save_models()
def create_simulated_data(self, cmd, pos):
self.lock.acquire()
a = [int(pos.x), int(pos.y)]
b = [int(cmd.x), int(cmd.y)]
# b[0] = int(cmd.x)
# b[1] = int(cmd.y)
tr = self.cam_sim.get_trajectory(a, b)
trn = self.cam_sim.get_trajectory_names(a, b)
#a[0] = b[0]
#a[1] = b[1]
rounded = self.cam_sim.round2mul(tr, 5) # only images every 5mm
# print 'trajectory '
for i in range(len(tr)):
# print 'curr_dir ' , os.getcwd()
# print(tr[i],rounded[i],trn[i])
self.pos.append([
float(rounded[i][0]) / x_lims[1],
float(rounded[i][1]) / y_lims[1]
])
self.cmd.append(
[float(int(cmd.x)) / x_lims[1],
float(int(cmd.y)) / y_lims[1]])
cv2_img = cv2.imread(trn[i], 1)
if self.channels == 1:
cv2_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2GRAY)
cv2_img = cv2.resize(cv2_img, (self.image_size, self.image_size),
interpolation=cv2.INTER_LINEAR)
cv2_img = cv2_img.astype('float32') / 255
cv2_img.reshape(1, self.image_size, self.image_size, self.channels)
self.img.append(cv2_img)
self.lock.release()
def ats_callback(self, image, cmd, pos):
self.lock.acquire()
self.msg_cmd = cmd
self.msg_pos = pos
self.msg_image = image
self.pos.append([
float(self.msg_pos.x) / x_lims[1],
float(self.msg_pos.y) / y_lims[1]
])
self.cmd.append([
float(self.msg_cmd.x) / x_lims[1],
float(self.msg_cmd.y) / y_lims[1]
])
self.samples.append({
'image': self.msg_image,
'position': self.msg_pos,
'command': self.msg_cmd
})
cv2_img = bridge.imgmsg_to_cv2(self.msg_image, "bgr8")
if self.channels == 1:
cv2_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2GRAY)
cv2_img = cv2_img.astype('float32') / 255
cv2_img.reshape(1, self.image_size, self.image_size, self.channels)
self.img.append(cv2_img)
#img_list = []
#for i in range(self.batch_size):
# img_list.append(self.img[-1].reshape(self.image_size, self.image_size, self.channels))
#print np.asarray(self.img[-1]).reshape(1, self.image_size, self.image_size, self.channels).shape
#img_code = self.encoder.predict(np.asarray(self.img[-1]).reshape(1, self.image_size, self.image_size, self.channels) )
#print np.asarray(img_list).shape
#img_code = self.encoder.predict(np.asarray(img_list))
#error = np.fabs(np.linalg.norm(np.asarray(self.goal_code)-np.asarray(img_code[0])))
#self.log_distance_to_goal.append(error)
#error=0
#print 'error ', error
self.lock.release()
# if len(images) ==batches+1:
# print 'Updating fwd model'
#print np.asarray(prev_poss).shape, " ", np.asarray(prev_cmds).shape
# train_forward_model_on_batch(forward_model, np.hstack((np.asarray(prev_pos), np.asarray(prev_cmd))) , np.asarray(images), batchttps://stackoverflow.com/questions/961632/converting-integer-to-string-in-pythonh_size=10)
# print 'Updating inv model'
# train_inverse_model_on_batch(inverse_model, encoder, np.asarray(prev_pos), np.asarray(images), np.asarray(prev_cmd), batch_size=10)
# prev_pos =[]#
# prev_cmd =[]
# images = []
#current_pos = []
def save_models(self):
timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%X')
name = 'goal_babbling'
filename = './data/' + name + '_' + timestamp + '.pkl'
#with gzip.open(filename, 'wb') as file:
# cPickle.dump(self.samples, file, protocol=2)
####cv2.destroyAllWindows()
#print 'Dataset saved to ', filename
self.autoencoder.save('./models/autoencoder.h5', overwrite=True)
self.encoder.save('./models/encoder.h5', overwrite=True)
self.decoder.save('./models/decoder.h5', overwrite=True)
# self.inverse_model.save('./models/inverse_model.h5')
self.inverse_code_model.save('./models/inverse_code_model.h5',
overwrite=True)
#self.forward_model.save('./models/gb/forward_model.h5')
self.forward_code_model.save('./models/forward_code_model.h5',
overwrite=True)
#trained_som_weights = self.goal_som.get_weights().copy()
#som_file = h5py.File('./models/goal_som.h5', 'w')
#som_file.create_dataset('goal_som', data=trained_som_weights)
#som_file.close()
#np.savetxt('./models/log_goal_pos.txt', self.log_goal_pos)
pickle.dump(self.log_goal_pos, open('./models/log_goal_pos.txt', 'wb'))
#go_file = open('./models/log_goal_pos.txt','w')
#go_file.write(str(self.log_goal_pos))
#go_file.close()
#np.savetxt('./models/log_curr_pos.txt', self.log_curr_pos)
# cPickle.dump(self.log_curr_pos, open('./models/log_curr_pos.txt', 'wb'))
pickle.dump(self.log_goal_pred, open('./models/log_goal_pred.txt',
'wb'))
#cu_file = open('./models/log_curr_pos.txt','w')
#cu_file.write(str(self.log_curr_pos))
#cu_file.close()
np.savetxt('./models/log_learning_progress.txt', self.log_lp)
#lp_file = open('./models/log_learning_progress.txt','w')
#lp_file.write(str(self.log_lp))
#lp_file.close()
np.savetxt('./models/log_goal_id.txt', self.log_goal_id)
#gi_file = open('./models/log_goal_id.txt','w')
#gi_file.write(str(self.log_goal_id))
#gi_file.close()
#np.savetxt('./models/log_distance_to_goal.txt', self.log_distance_to_goal)
#dg_file = open('./models/log_distance_to_goal.txt','w')
#dg_file.write(str(self.log_distance_to_goal))
#dg_file.close()
np.savetxt('./models/log_mse_inv.txt', self.mse_inv)
plot_simple(self.mse_inv, 'MSE INV', save=True, show=False)
np.savetxt('./models/log_mse_fwd.txt', self.mse_fwd)
plot_simple(self.mse_fwd, 'MSE FWD', save=True, show=False)
np.savetxt('./models/log_cvh_inv.txt', self.log_cvh_inv)
plot_simple(self.log_cvh_inv, 'CVH Inv Volume', save=True, show=False)
#np.savetxt('./models/log_cvh_fwd.txt', self.log_cvh_fwd)
#plot_simple(self.log_cvh_fwd, 'CVH Fwd Volume', save = True, show = False)
#np.savetxt('./models/log_lr_inv.txt', self.log_lr_inv)
#plot_simple(self.log_lr_inv, 'LR_inv')
plot_learning_progress(self.log_lp,
self.log_goal_id,
num_goals=self.goal_size * self.goal_size,
save=True,
show=False)
# plot_log_goal_inv(self.log_goal_pos, self.log_curr_pos, num_goals = self.goal_size*self.goal_size, save = True, show = False)
plot_log_goal_inv(self.log_goal_pos,
self.log_goal_pred,
num_goals=self.goal_size * self.goal_size,
save=True,
show=False)
#plot_log_goal_fwd(self.log_goal_img, self.log_curr_img, num_goals = self.goal_size*self.goal_size, save = True, show = False)
self.clear_session()
print('Models saved')
self.goto_starting_pos()
def clear_session(self):
print('Clearning variables')
#del self.log_goal_pred
#del self.log_curr_img
#del self.log_cvh_inv
#del self.log_goal_id
#del self.log_goal_img
#del self.log_goal_pos
#del self.log_lp
#del self.samples
#del self.samples_img
#del self.samples_codes
#del self.samples_pos
#del self.autoencoder
#del self.encoder
#del self.decoder
#del self.inverse_code_model
#del self.forward_code_model
#del self.mse_inv
#del self.mse_fwd
#del self.history_img
#del self.history_pos
# reset
print('Clearing TF session')
if tf.__version__ < "1.8.0":
tf.reset_default_graph()
else:
tf.compat.v1.reset_default_graph()
def Exit_call(self, signal, frame):
self.goto_starting_pos()
self.save_models()
def goto_starting_pos(self):
p = Position()
p.x = int(0)
p.y = int(0)
p.z = int(-50)
p.speed = int(1400)
self.create_simulated_data(p, self.prev_pos)
self.prev_pos = p
class atari_env(object): # to change env: extra punishment
def __init__(self, args, rank=0, save_img=False):
self.total_step = 0
self.rank = rank
self.img = []
self.save_img = save_img
self.args = args
self.grid = 3
self.env_path = '../env/AnimalAI'
self.worker_id = random.randint(1, 100)
self.arena_config_in = ArenaConfig('configs/3-Obstacles_my.yaml')
self.base_dir = 'models/dopamine'
self.gin_files = ['configs/rainbow.gin']
self.env = self.create_env_fn()
self.previous_velocity = [1, 1]
self.observation_space = self.env.observation_space
self.theta = 3.1415926 / 2
self.pi = 3.1415926
self.rotation = 40
self.convex_hull = None
self.points = [np.array([0, 0])]
self.point = np.array([0, 0])
self.turned = 0
self.velocity = {
0: [1, 0],
1: [-1, 0],
2: [0, 1],
3: [0, -1],
4: [0.7, 0.7],
5: [-0.7, 0.7],
6: [-0.7, -0.7],
7: [0.7, -0.7],
}
def create_env_fn(self):
env = AnimalAIEnv(environment_filename=self.env_path,
worker_id=self.worker_id,
n_arenas=1,
arenas_configurations=self.arena_config_in,
docker_training=False,
retro=False,
inference=self.args['inference'],
seed=self.rank,
resolution=84)
return env
def update(self, theta, angle):
theta = theta + angle
if theta > self.pi:
theta = theta - self.pi
if theta < -self.pi:
theta = theta + self.pi
return theta
def transform_action(self, action):
action = action[0][0]
if action == 0:
return [1, 0]
elif action == 1:
return [0, 1]
else:
return [0, 2]
def step(self, action):
# turn to the right direction
action = self.transform_action(action)
state, reward, done, info = self.env.step(action)
instrinsic_reward = 0
if reward < 0 and reward > -0.1:
reward = 0
self.total_step = self.total_step + 1
if self.total_step % 500 == 0:
print("reward, action, done, total_step, thread is ", reward,
action, done, self.total_step, self.rank)
if np.linalg.norm(np.array(
state[1])) < 1 and args['stationary_punish']:
instrinsic_reward = -0.1
if reward > 0:
reward = 4
if self.args['convex_hull']:
direction = np.array([math.cos(self.theta), math.sin(self.theta)])
velocity = direction * np.linalg.norm(np.array(state[1]))
if np.linalg.norm(velocity) > 0.1:
self.point = self.point + velocity
self.points.append(self.point)
if len(self.points
) > 10 and self.turned > 3 and self.convex_hull is None:
self.convex_hull = ConvexHull(np.array(self.points),
incremental=True)
elif self.convex_hull is not None:
previous_area = self.convex_hull.area
self.convex_hull.add_points(np.array([self.point]))
new_area = self.convex_hull.area
convex_reward = max(new_area - previous_area, 0)
convex_reward = math.sqrt(convex_reward) # [0,4]
instrinsic_reward = instrinsic_reward + convex_reward * self.args[
'ir_weight']
#reward = reward + instrinsic_reward * self.args['ir_weight']
if action[1] == 1:
self.theta = self.update(self.theta, -6 / 180 * self.pi)
self.turned = self.turned + 1
if action[1] == 2:
self.theta = self.update(self.theta, 6 / 180 * self.pi)
self.turned = self.turned + 1
state = state[0]
state = state.transpose(2, 0, 1)
with torch.no_grad():
state = torch.tensor(state).unsqueeze(0).float().to(device)
return state, (reward, instrinsic_reward), done, info
def reset(self):
if self.args['convex_hull']:
self.theta = 3.1415926 / 2
self.convex_hull = None
self.points = [np.array([0, 0])]
self.point = np.array([0, 0])
state = self.env.reset()[0]
self.turned = 0
state = state.transpose(2, 0, 1)
with torch.no_grad():
state = torch.tensor(state).unsqueeze(0).float().to(device)
return state
def render(self):
self.env.render()
def seed(self, seed):
self.env.seed(seed)
def close(self):
self.env.close()
str(len(binPnts[-1])) +
' points found within the max threshold distance of a ligand atom\n'
)
# print('From ' + str(len(nonBuriedPnts)) + ' points, ' + str(len(binPnts[-1])) + ' points found within the threshold distance of a ligand atom\n')
# Exclude points outside of convex hull
initHull = ConvexHull(proCoords)
hullVerts = initHull.points[initHull.vertices]
hull = ConvexHull(points=hullVerts, incremental=True)
inHullPnts = []
for p in binPnts[
-1]: # Exclude points from largest cpocket threshold that are exterior to convex hull first, then check for those points prescence in the smaller pocket threshold point sets
newVertInd = len(
hull.points
) # If a new vert shows up with this index, it is outside of the convex hull
hull.add_points(np.array([p]))
if newVertInd in hull.vertices: # if point is outside convex hull
hull = ConvexHull(
points=hullVerts,
incremental=True) # reset convex hull
else:
inHullPnts.append(p)
hull.close()
# coords2Mol2('./data/unilectin/structures/bSites/pntsb4DBSCAN/' + pdb + '_' + bs.replace(':','-') + '.mol2', inHullPnts)
logFH.write('From ' + str(len(binPnts[-1])) + ' points, ' +
str(len(inHullPnts)) +
' points found within the convex hull\n\n')
# print('From ' + str(len(binPnts[-1])) + ' points, ' + str(len(inHullPnts)) + ' points found within the convex hull\n')
# Drop points outside of the convex hull from the binned points as well
for i in range(len(binPnts)):
class Agent(object):
def __init__(self):
"""
Load your agent here and initialize anything needed
WARNING: any path to files you wish to access on the docker should be ABSOLUTE PATHS
"""
self.green = np.array([129.0, 191.0, 65.0])
self.yellow = np.array([100.0, 65.0, 5.0])
self.red = np.array([185.0, 50.0, 50.0])
self.blue = np.array([50.0, 77.0, 121.0])
self.grey = np.array([115.0, 115.0, 115.0])
self.blue_threshold = 50
self.grey_threshold = 100
self.best_dir = None
self.explore_turns = 0
self.obstacle_threshold = 1764
self.mode = 'spin'
self.spin_turns = 0
self.steps = 0
self.k = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
self.mean = np.zeros((3, 3))
self.mean = self.mean + 1 / 9
self.k2 = np.array([[1, 0, -1], [0, 0, 0], [-1, 0, 1]])
self.max_r = -100
self.theta = 3.1415926 / 2
self.pi = 3.1415926
self.points = [
np.array([0, 0]),
np.array([0.1, 0]),
np.array([0, 0.1])
]
self.convex_hull = ConvexHull(np.array(self.points), incremental=True)
self.point = np.array([0, 0])
self.attempt_point = None
self.init = True
self.force_turn = False
self.fetch_steps = 0
self.semantic_mask = semantic_mask()
def reset(self, t=250):
"""
Reset is called before each episode begins
Leave blank if nothing needs to happen there
:param t the number of timesteps in the episode
"""
self.setmode('spin')
self.steps = 0
self.points = [
np.array([0, 0]),
np.array([0.1, 0]),
np.array([0, 0.1])
]
self.convex_hull = ConvexHull(np.array(self.points), incremental=True)
self.point = np.array([0, 0])
self.init = True
self.force_turn = False
self.t = t
def get_colours(self, pixels, colour):
if np.array_equal(colour, self.blue):
return np.all(pixels > np.minimum(colour * .95, colour - 5),
axis=-1) & np.all(
pixels < np.maximum(colour * 1.05, colour + 5),
axis=-1)
return np.all(
pixels > np.minimum(colour * .8, colour - 25), axis=-1) & np.all(
pixels < np.maximum(colour * 1.2, colour + 25), axis=-1)
def inspect(self, img):
green = self.get_colours(img, self.green)
yellow = self.get_colours(img, self.yellow)
ag, bg = np.where(green == True)
ay, by = np.where(yellow == True)
return len(ag) > 0 or len(ay) > 0
def fetch(self, obs):
img = obs[0]
green = self.get_colours(img, self.green)
yellow = self.get_colours(img, self.yellow)
a, b = np.where(green == True)
ay, by = np.where(yellow == True)
self.fetch_steps = self.fetch_steps + 1
if len(ay) != 0:
# print('ye')
self.spin_turns = 0
if np.mean(by) > 40:
action = [1, 1]
elif np.mean(by) < 40:
action = [1, 2]
else:
action = [1, 0]
elif len(a) != 0:
# print('gr')
self.spin_turns = 0
if np.mean(b) > 60:
action = [0, 1]
elif np.mean(b) < 20:
action = [0, 2]
elif np.mean(b) > 40:
action = [1, 1]
elif np.mean(b) < 40:
action = [1, 2]
else:
action = [1, 0]
else:
action = [0, 1]
self.setmode('spin')
if self.fetch_steps > 2 and obs[1][2] < 0.5:
grey = self.get_colours(img, self.grey)
a, b = np.where(grey == True)
if np.mean(b) > 40:
action = [0, 2]
else:
action = [0, 1]
if np.mean(b) < 25 or np.mean(b) > 57:
self.fetch_steps = 0
return action
def spin(self, obs, reward, done, info):
img = obs[0]
action = [0, 1]
self.spin_turns = self.spin_turns + 1
if self.spin_turns > 360 / 6:
self.setmode('explore')
if self.init:
self.init = False
return action
def check(self, obs):
img = obs[0]
blue = self.get_colours(img, self.blue)
a, b = np.where(blue == True)
if len(a) > 0:
self.sky_appear = True
else:
if self.sky_appear == True:
self.setmode('explore')
self.spin_turns = self.spin_turns + 1
if self.spin_turns > 360 / 6:
self.setmode('explore')
return self.check_turn
def explore(self, obs, reward, done, info):
self.explore_turns = self.explore_turns - 1
if self.explore_turns > 0:
return self.explore_turn
if self.explore_turns == 0:
self.force_turn = False
if obs[1][0] > 0.5:
return [1, 1]
if obs[1][0] < -0.5:
return [1, 2]
img = obs[0]
blue = self.get_colours(img, self.blue)
a, b = np.where(blue == True)
if len(a) > 10:
self.sky_mean = np.mean(b)
if obs[1][2] < 0.5 and self.explore_turns < -5:
self.setmode('check', obs=obs)
return [2, 0]
return [1, 0]
def valid_openning(self, obs):
img = obs[0]
blue = self.get_colours(img[:, 41:45, :], self.blue)
a, b = np.where(blue == True)
grey = self.semantic_mask.grey_mask(img)
ga, gb = np.where(grey[42:, :] == True)
return len(a) > self.blue_threshold and len(
ga) < self.obstacle_threshold and self.danger(img) == False
def score(self, img):
green = self.get_colours(img, self.green)
yellow = self.get_colours(img, self.yellow)
ag, bg = np.where(green == True)
ay, by = np.where(yellow == True)
return 10000 * max(len(ag), len(ay))
def getedge(self, img):
edge = img
edge = np.mean(edge, axis=-1)
img = Image.fromarray(edge.astype(np.uint8))
img.save('crr/my' + str(self.steps) + '.png')
edge = ndimage.convolve(edge, self.mean, mode='constant', cval=0.0)
edge = ndimage.convolve(edge, self.k, mode='constant', cval=0.0)
# edge = feature.canny(edge, sigma=3)*255
print(np.mean(edge))
ff = edge.astype(np.uint8)
img = Image.fromarray(ff)
img.save('cr/my' + str(self.steps) + '.png')
return edge
def update_convexhull(self, obs):
direction = np.array([math.cos(self.theta), math.sin(self.theta)])
velocity = direction * obs[1][2]
if np.linalg.norm(velocity) > 0.1:
self.point = self.point + velocity
self.points.append(self.point)
self.convex_hull.add_points(np.array([self.point]))
def setmode(self, mode, obs=None):
self.mode = mode
if mode == 'spin':
self.spin_turns = 0
self.max_r = -100
elif mode == 'explore':
desired_spin = self.update(-self.theta, self.best_dir)
if desired_spin > 0:
self.explore_turn = [0, 2]
self.explore_turns = desired_spin // (6 / 180 * self.pi)
else:
self.explore_turn = [0, 1]
self.explore_turns = desired_spin // (-6 / 180 * self.pi)
self.sky_mean = -1
self.convex_hull.add_points(np.array([self.attempt_point]))
elif mode == 'fetch':
self.fetch_steps = 0
elif mode == 'check':
assert obs is not None
self.spin_turns = 0
self.max_r = -100
img = obs[0]
blue = self.get_colours(img, self.blue)
a, b = np.where(blue == True)
if len(a) > 0:
if np.mean(b) < 42:
self.check_turn = [0, 2]
else:
self.check_turn = [0, 1]
self.sky_appear = True
else:
if self.sky_mean < 42:
self.check_turn = [0, 2]
else:
self.check_turn = [0, 1]
self.sky_appear = False
elif mode == 'forward':
self.mode = 'forward'
def forward(self, obs):
if self.nodanger(obs[0]):
self.setmode('spin')
return [1, 0]
def update(self, theta, angle):
theta = theta + angle
if theta > self.pi:
theta = theta - 2 * self.pi
if theta < -self.pi:
theta = theta + 2 * self.pi
return theta
def free_reward(self, obs):
ch = ConvexHull(np.array(self.points), incremental=True)
area = ch.area
direction = np.array([math.cos(self.theta), math.sin(self.theta)])
velocity = direction * 50
newp = self.point + velocity
ch.add_points(np.array([newp]))
narea = ch.area
return narea - area, newp
def savefig(self, img):
img = Image.fromarray(img.astype(np.uint8))
img.save('crr/my' + str(self.steps) + '.png')
def danger(self, img):
red = self.get_colours(img, self.red)
kk = red[50:, 35:50]
aa, bb = np.where(kk)
return len(aa) > 0
def nodanger(self, img):
red = self.get_colours(img, self.red)
kk = red[50:, :]
aa, bb = np.where(kk)
return len(aa) == 0
def step(self, obs, reward, done, info):
"""
A single step the agent should take based on the current state of the environment
We will run the Gym environment (AnimalAIEnv) and pass the arguments returned by env.step() to
the agent.
Note that should if you prefer using the BrainInfo object that is usually returned by the Unity
environment, it can be accessed from info['brain_info'].
:param obs: agent's observation of the current environment
:param reward: amount of reward returned after previous action
:param done: whether the episode has ended.
:param info: contains auxiliary diagnostic information, including BrainInfo.
:return: the action to take, a list or size 2
"""
a, b = obs
obs = (a * 255, b)
if self.inspect(
obs[0]
) and self.force_turn == False and self.init == False and self.danger(
obs[0]) == False:
self.setmode('fetch')
#points = self.semantic_mask.boundary_mask(obs[0])
#self.semantic_mask.checkmask(obs[0],self.semantic_mask.boundary_mask(obs[0]))
if savefile:
self.savefig(obs[0])
self.steps = self.steps + 1
self.update_convexhull(obs)
if (self.valid_openning(obs) or self.inspect(
obs[0])) and (self.mode == 'spin' or self.mode == 'check'):
free_r, newp = self.free_reward(obs)
if self.inspect(obs[0]):
free_r = self.score(obs[0])
self.force_turn = True
if free_r > self.max_r:
self.max_r = free_r
self.best_dir = self.theta
self.attempt_point = newp
print(self.mode)
if self.mode == 'spin':
action = self.spin(obs, reward, done, info)
elif self.mode == 'explore':
action = self.explore(obs, reward, done, info)
elif self.mode == 'fetch':
action = self.fetch(obs)
elif self.mode == 'forward':
action = self.forward(obs)
else:
action = self.check(obs)
if self.danger(obs[0]) and (obs[1][2] > 0.001 or action[1] == 1):
action = [0, 1]
if obs[1][2] > 5:
action = [2, 1]
self.setmode('forward')
if action[1] == 1:
self.theta = self.update(self.theta, -6 / 180 * self.pi)
if action[1] == 2:
self.theta = self.update(self.theta, 6 / 180 * self.pi)
return action
class DelaunayTriangulation:
def __init__(self, convex_covering_space):
self.__convex_covering_space = convex_covering_space
self.__coordinates_to_points = None
self.__coordinates_number = 0
self.__points_to_coordinates = None
self.__points = None
self.__convex_hull = None
def add_points(self, points, restart=False):
import numpy as np
coordinates, c2p, p2c = self.__convex_covering_space.coordinates(
points)
self.__points = self.__convex_covering_space.extend(
self.__points, points)
if self.__convex_hull is None:
from scipy.spatial import ConvexHull
if self.__convex_covering_space.infinity is not None:
coordinates = np.vstack(
(self.__convex_covering_space.infinity, coordinates))
self.__coordinates_to_points = np.hstack(([0], c2p + 1)) - 1
self.__points_to_coordinates = p2c + 1
else:
self.__coordinates_to_points = c2p
self.__points_to_coordinates = p2c
self.__convex_hull = ConvexHull(coordinates, incremental=True)
else:
self.__coordinates_to_points = np.hstack(
(self.__coordinates_to_points, c2p + self.__points_number))
self.__points_to_coordinates = np.hstack(
(self.__points_to_coordinates,
p2c + self.__coordinates_number))
self.__convex_hull.add_points(coordinates, restart)
self.__coordinates_number = len(self.__coordinates_to_points)
self.__points_number = len(self.__points_to_coordinates)
@property
def points(self):
return self.__points
@property
def simplices(self):
self.__simplices = self.__coordinates_to_points[
self.__filter_external_simplices(
self.__filter_infinite_simplices(
self.__convex_hull.simplices))]
return self.__simplices
@property
def neighbors(self):
pass
def __filter_infinite_simplices(self, simplices):
import numpy as np
if self.__convex_covering_space.infinity is not None:
finite_mask = np.all(simplices != 0, axis=1)
simplices = simplices[finite_mask]
return simplices
def __filter_external_simplices(self, simplices):
import numpy as np
internal_mask = np.any(self.__points_to_coordinates[
self.__coordinates_to_points[simplices]] == simplices,
axis=1)
simplices = simplices[internal_mask]
return simplices
warden_points = np.array(warden_list)
warden_cv = ConvexHull(warden_points, True)
if poach > 0:
poach_points = np.array(poach_list)
poach_cv = ConvexHull(poach_list, True)
for i in range(manatee):
x, y = map(int, stdin.readline().split())
info = Manatee_info()
info.x_coordi = x
info.y_coordi = y
manatee_list.append(info)
if warden > 0:
for info in manatee_list:
temp_points = warden_cv.points
temp_vertices = warden_cv.vertices
warden_cv.add_points(info.get_points(), True)
com_result = np.array_equal(temp_vertices, warden_cv.vertices)
warden_cv = ConvexHull(temp_points, True)
if com_result:
info.manatee_status = "safe"
if poach > 0:
for info in manatee_list:
temp_points = poach_cv.points
temp_vertices = poach_cv.vertices
poach_cv.add_points(info.get_points(), True)
com_result = np.array_equal(temp_vertices, poach_cv.vertices)
poach_cv = ConvexHull(temp_points, True)
if com_result and info.manatee_status != "safe":
info.manatee_status = "endangered"
for info in manatee_list:
stdout.write(info.get_result())
plt.plot(police_points[:, 0], police_points[:, 1], 'o', color='black')
plt.plot(bar_points[:, 0], bar_points[:, 1], 'o', color='blue')
police_hull = ConvexHull(police_points, incremental=True)
bar_hull = ConvexHull(bar_points, incremental=True)
draw_simplices_hull(police_hull, police_points, 'k-')
draw_simplices_hull(bar_hull, bar_points, 'r-')
plt.show()
#집과 경찰서와의 관계
for home in home_list:
temp_vertices = police_hull.vertices
home_point = np.array(home.get_points())
print(home_point)
police_hull_added_home = police_hull.add_points(home_point)
safe = np.array_equal(temp_vertices, police_hull_added_home)
if safe:
home.status = 'safe'
#집과 술집과의 관계
for home in home_list:
temp_vertices = police_hull.vertices
home_point = np.array(home.get_points())
bar_hull_added_home = bar_hull.add_points(home_point)
danger = np.array_equal(temp_vertices, bar_hull_added_home)
if danger and home.status == 'safe':
home.status = 'vulnerable'
if danger and home.status == 'vulnerable':
home.status = 'endangered'
for home in home_list:
class BinarySeparation(object):
def __init__(self, E, A, B, is_manifold_true=False):
self.field_size = math.inf
self.size_E = len(E)
self.dimension_E = len(E[0])
self.colors_E = []
self.A = A
self.B = B
self.size_A = len(A)
self.size_B = len(B)
# set colors
for i in range(self.size_E):
if i in self.A:
self.colors_E.append("red")
elif i in self.B:
self.colors_E.append("green")
else:
self.colors_E.append("blue")
# point distances
self.convex_A_distances = {}
self.convex_B_distances = {}
self.convex_A_hull_distances = {}
self.convex_B_hull_distances = {}
self.E = E
self.hull_C_A = ConvexHull(self.E[self.A], 1)
self.C_A, added = get_points_inside_convex_hull(self.E, self.hull_C_A, self.A, [x for x in range(self.size_E) if x not in self.A])
for x in added:
self.colors_E[x] = 'orange'
self.hull_C_B = ConvexHull(self.E[self.B], 1)
self.C_B, added = get_points_inside_convex_hull(self.E, self.hull_C_B, self.B, [x for x in range(self.size_E) if x not in self.B])
for x in added:
self.colors_E[x] = 'violet'
if is_manifold_true:
# manifolds from hull
self.m1 = gel.Manifold()
for s in self.hull_C_A.simplices:
self.m1.add_face(self.hull_C_A.points[s])
self.m1dist = gel.MeshDistance(self.m1)
self.m2 = gel.Manifold()
for s in self.hull_C_B.simplices:
self.m2.add_face(self.hull_C_B.points[s])
self.m2dist = gel.MeshDistance(self.m2)
# Set unlabeled elements
self.F = list(set(range(self.size_E)) - set([x for x in self.C_A or x in self.C_B]))
# Pre-computation of monotone linkages
self.convex_a_neighbors()
self.convex_b_neighbors()
def plot_2d_classification(self, name="Test", colorlist=[]):
# initialize first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), self.dimension_E))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
# Initialize second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), self.dimension_E))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
# Draw convex initial_hulls of disjoint convex sets
for simplex in self.C_A.simplices:
plt.plot(self.C_A.points[simplex, 0], self.C_A.points[simplex, 1], 'k-')
for simplex in self.C_B.simplices:
plt.plot(self.C_B.points[simplex, 0], self.C_B.points[simplex, 1], 'k-')
x_val_dict = {}
y_val_dict = {}
if colorlist == []:
colorlist = self.colors_E
for i, x in enumerate(colorlist, 0):
if x not in x_val_dict:
x_val_dict[x] = [self.E[i][0]]
y_val_dict[x] = [self.E[i][1]]
else:
x_val_dict[x].append(self.E[i][0])
y_val_dict[x].append(self.E[i][1])
for key, value in x_val_dict.items():
plt.scatter(value, y_val_dict[key], c=key)
tikzplotlib.save(name + ".tex")
plt.show()
def plot_3d_classification(self):
# initialize first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), self.dimension_E))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
# Initialize second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), self.dimension_E))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
# print(self.ConvexHull1.equations)
ax = plt.axes(projection='3d')
for simplex in self.C_A.simplices:
ax.plot3D(self.C_A.points[simplex, 0], self.C_A.points[simplex, 1],
self.C_A.points[simplex, 2], 'k-')
for simplex in self.C_B.simplices:
ax.plot3D(self.C_B.points[simplex, 0], self.C_B.points[simplex, 1],
self.C_B.points[simplex, 2], 'k-')
X_coord = np.ones(self.size_E)
Y_coord = np.ones(self.size_E)
Z_coord = np.ones(self.size_E)
for i in range(self.size_E):
X_coord[i] = self.E[i][0]
Y_coord[i] = self.E[i][1]
Z_coord[i] = self.E[i][2]
ax.scatter3D(X_coord, Y_coord, Z_coord, c=self.colors_E)
plt.show()
def get_point(self, n):
return self.E[n]
def convex_a_neighbors(self, is_manifold=False):
for n in self.F:
min_dist = sys.maxsize
for x in self.C_A:
point_dist = dist(self.get_point(n), self.get_point(x))
if point_dist < min_dist:
min_dist = point_dist
self.convex_A_distances[n] = min_dist
if is_manifold:
d = self.m1dist.signed_distance(self.get_point(n))
self.convex_A_hull_distances[n] = d * np.sign(d)
self.convex_A_distances = OrderedDict(sorted(self.convex_A_distances.items(), key=lambda x: x[1], reverse=True))
if is_manifold:
self.convex_A_hull_distances = OrderedDict(
sorted(self.convex_A_hull_distances.items(), key=lambda x: x[1], reverse=True))
def convex_b_neighbors(self, is_manifold=False):
for n in self.F:
min_dist = sys.maxsize
for x in self.C_B:
point_dist = dist(self.get_point(n), self.get_point(x))
if point_dist < min_dist:
min_dist = point_dist
self.convex_B_distances[n] = min_dist
if is_manifold:
d = self.m2dist.signed_distance(self.get_point(n))
self.convex_A_hull_distances[n] = d * np.sign(d)
self.convex_B_distances = OrderedDict(sorted(self.convex_B_distances.items(), key=lambda x: x[1], reverse=True))
if is_manifold:
self.convex_B_hull_distances = OrderedDict(
sorted(self.convex_B_distances.items(), key=lambda x: x[1], reverse=True))
def decide_nearest(self):
len1 = len(self.convex_A_distances)
len2 = len(self.convex_B_distances)
if len1 == 0:
return 0
elif len2 == 0:
return 1
elif next(reversed(self.convex_A_distances.values())) <= next(reversed(self.convex_B_distances.values())):
return 1
else:
return 0
def decide_nearest_hull(self):
len1 = len(self.convex_A_hull_distances)
len2 = len(self.convex_B_hull_distances)
if len1 == 0:
return 0
elif len2 == 0:
return 1
elif next(reversed(self.convex_A_hull_distances.values())) <= next(
reversed(self.convex_B_hull_distances.values())):
return 1
else:
return 0
def decide_farthest(self):
len1 = len(self.convex_A_distances)
len2 = len(self.convex_B_distances)
if len1 == 0:
return 0
elif len2 == 0:
return 1
elif (next(iter(self.convex_A_distances.values())) <= next(iter(self.convex_B_distances.values()))):
return 1
else:
return 0
def add_C_A_nearest(self):
point = list(self.convex_A_distances.items())[0][0]
self.C_A.append(point)
self.colors_E[point] = "orange"
self.F.remove(point)
del self.convex_A_distances[point]
del self.convex_B_distances[point]
def add_C_B_nearest(self):
point = list(self.convex_B_distances.items())[0][0]
self.C_B.append(point)
self.colors_E[point] = "violet"
self.F.remove(point)
del self.convex_B_distances[point]
del self.convex_A_distances[point]
# SeCos-Algorithm from paper applied to convex initial_hulls in R^d
def greedy_alg(self):
end = False
oracle_calls = 0
# take an arbitrary element out of F
random.shuffle(self.F)
# label vector of the elements (binary classification {-1, 1}
labels = -1 * np.ones(shape=(self.size_E, 1))
# E labels of initial labelled data
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
if not intersect(self.C_A, self.C_B):
while len(self.F) > 0 and end == False:
# print(len(self.F))
next_point = self.F.pop()
new_hull_C_A = ConvexHull(self.E[self.C_A], 1)
new_hull_C_A.add_points([self.get_point(next_point)], 1)
new_C_A, added = get_points_inside_convex_hull(self.E, new_hull_C_A, self.C_A, self.F + self.C_B,
next_point)
oracle_calls += 1
if not intersect(new_C_A, self.C_B):
self.hull_C_A = new_hull_C_A
self.C_A = new_C_A
self.F = list(set(self.F) - set(added))
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
else:
new_hull_C_B = ConvexHull(self.E[self.C_B], 1)
new_hull_C_B.add_points([self.get_point(next_point)], 1)
new_C_B, added = get_points_inside_convex_hull(self.E, new_hull_C_B, self.C_B, self.F + self.C_A,
next_point)
oracle_calls += 1
if not intersect(self.C_A, new_C_B):
self.hull_C_B = new_hull_C_B
self.C_B = new_C_B
self.F = list(set(self.F) - set(added))
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
else:
return [], [], False
return oracle_calls, labels, True
def greedy_fast_alg(self):
end = False
oracle_calls = 0
random.shuffle(self.F)
# label vector of the elements (binary classification {1, 0} -1 are unclassified
labels = -1 * np.ones(shape=(self.size_E, 1))
outside_1 = self.F + self.C_B
outside_2 = self.F + self.C_A
# E labels of initial labelled data
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
# E initial initial_hulls
inside1, added, intersection = get_inside_points(self.E, self.C_A, self.F, CheckSet=self.C_B)
oracle_calls += 1
if not intersection:
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
self.C_A = inside1
# E initial initial_hulls
inside2, added, intersection = get_inside_points(self.E, self.C_B, self.F, CheckSet=self.C_A)
oracle_calls += 1
if not intersection:
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
self.C_B = inside2
if not intersect(inside1, inside2):
for x in self.C_A:
if x in self.F:
self.F.remove(x)
for x in self.C_B:
if x in self.F:
self.F.remove(x)
while len(self.F) > 0 and end == False:
# print(len(self.F))
next_point = self.F.pop()
inside1, added, intersection = get_inside_points(self.E, self.C_A, self.F, next_point,
self.C_B)
oracle_calls += 1
if not intersection:
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.F:
self.F.remove(x)
self.C_A = inside1
else:
inside2, added, intersection = get_inside_points(self.E, self.C_B, self.F, next_point,
self.C_A)
oracle_calls += 1
if not intersection:
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.F:
self.F.remove(x)
self.C_B = inside2
else:
return ([], [], False)
return (oracle_calls, labels, True)
def greedy_alg2(self):
end = False
oracle_calls = 0
random.shuffle(self.F)
# label vector of the elements (binary classification {-1, 1}
labels = -1 * np.ones(shape=(self.size_E, 1))
outside_1 = self.F + self.C_B
outside_2 = self.F + self.C_A
# E labels of initial labelled data
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
# E initial initial_hulls
Hull1 = self.C_A
inside1, added = get_points_inside_convex_hull(self.E, Hull1, self.C_A, self.F + self.C_B)
oracle_calls += 1
if not intersect(inside1, self.C_B):
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
self.C_A = inside1
# E initial initial_hulls
Hull2 = self.C_B
inside2, added = get_points_inside_convex_hull(self.E, Hull2, self.C_B, self.F + self.C_A)
oracle_calls += 1
if not intersect(inside2, self.C_A):
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
self.C_B = inside2
if not intersect(inside1, inside2):
for x in self.C_A:
if x in self.F:
self.F.remove(x)
for x in self.C_B:
if x in self.F:
self.F.remove(x)
while len(self.F) > 0 and end == False:
# print(len(self.F))
next_point = self.F.pop()
if (random.randint(0, 1)):
Hull1 = self.C_A
Hull1.add_points([self.get_point(next_point)], 1)
inside1, added = get_points_inside_convex_hull(self.E, Hull1, self.C_A, self.F + self.C_B,
next_point)
oracle_calls += 1
if not intersect(inside1, self.C_B):
self.C_A.add_points([self.get_point(next_point)], 1)
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.F:
self.F.remove(x)
self.C_A = inside1
else:
# initialize first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), self.dimension_E))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
Hull2 = self.C_B
Hull2.add_points([self.get_point(next_point)], 1)
inside2, added = get_points_inside_convex_hull(self.E, Hull2, self.C_B,
self.F + self.C_A, next_point)
oracle_calls += 1
if not intersect(self.C_A, inside2):
self.C_B.add_points([self.get_point(next_point)], 1)
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.F:
self.F.remove(x)
self.C_B = inside2
else:
# initialize second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), self.dimension_E))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
else:
Hull2 = self.C_B
Hull2.add_points([self.get_point(next_point)], 1)
inside2, added = get_points_inside_convex_hull(self.E, Hull2, self.C_B, self.F + self.C_A,
next_point)
oracle_calls += 1
if not intersect(inside2, self.C_A):
self.C_B.add_points([self.get_point(next_point)], 1)
for x in added:
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.F:
self.F.remove(x)
self.C_B = inside2
else:
# initialize first half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), self.dimension_E))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
Hull1 = self.C_A
Hull1.add_points([self.get_point(next_point)], 1)
inside1, added = get_points_inside_convex_hull(self.E, Hull1, self.C_A,
self.F + self.C_B, next_point)
oracle_calls += 1
if not intersect(self.C_B, inside1):
self.C_A.add_points([self.get_point(next_point)], 1)
for x in added:
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.F:
self.F.remove(x)
self.C_A = inside1
else:
# initialize second half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), self.dimension_E))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
else:
return ([], [], False)
return (oracle_calls, labels, True)
def optimal_alg(self):
time_point = time.time()
oracle_calls = 0
counter = 0
labels = -1 * np.ones(shape=(self.size_E, 1))
outside_points_1 = [x for x in self.convex_A_distances.keys()] + self.C_B
outside_points_2 = [x for x in self.convex_B_distances.keys()] + self.C_A
# add labels
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
if not intersect(self.C_A, self.C_B):
while (len(self.convex_A_distances) > 0 or len(self.convex_B_distances) > 0):
# print(len(self.Set1Distances), len(self.Set2Distances))
added = []
# First set is nearer to nearest not classified point
if self.decide_nearest():
time_point = time_step("Find Neighbour:", time_point)
if len(self.convex_A_distances) > 0:
next_point = self.convex_A_distances.popitem()[0]
new_hull_C_A = ConvexHull(self.E[self.C_A], 1)
new_hull_C_A.add_points([self.get_point(next_point)], 1)
time_point = time_step("Adding Convex Hull E 1:", time_point)
new_C_A, added = get_points_inside_convex_hull(self.E, new_hull_C_A, self.C_A,
outside_points_1)
oracle_calls += 1
time_point = time_step("Getting inside E:", time_point)
# if there is no intersection the point can be added to the first convex set
if not intersect(new_C_A, self.C_B):
time_point = time_step("Intersection Test:", time_point)
self.C_A = new_C_A
self.hull_C_A = new_hull_C_A
for x in added:
# add to labels
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_1 = list(set(outside_points_1) - set(added))
time_point = time_step("Update arrays:", time_point)
# if there is an intersection we have to check if it can be added to the second set
else:
# Test second half space
new_hull_C_B = ConvexHull(self.E[self.C_B], 1)
new_hull_C_B.add_points([self.get_point(next_point)], 1)
new_C_B, added = get_points_inside_convex_hull(self.E, new_hull_C_B, self.C_B,
outside_points_2)
oracle_calls += 1
# the point can be added to the second set,
# if we reach this point the first time all the other E which are classified did not change the optimal margin
if not intersect(self.C_A, new_C_B):
self.C_B = new_C_B
self.hull_C_B = new_hull_C_B
for x in added:
# add to labels
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_2 = list(set(outside_points_2) - set(added))
# the point cannot be added to any set
else:
if next_point in outside_points_1:
outside_points_1.remove(next_point)
if next_point in outside_points_2:
outside_points_2.remove(next_point)
time_point = time_step("Point add Hull:", time_point)
# Second set is nearer to nearest not classified point
else:
time_point = time_step("Find Neighbour:", time_point)
if len(self.convex_B_distances) > 0:
next_point = self.convex_B_distances.popitem()[0]
new_hull_C_B = ConvexHull(self.E[self.C_B], 1)
new_hull_C_B.add_points([self.get_point(next_point)], 1)
new_C_B, added = get_points_inside_convex_hull(self.E, new_hull_C_B, self.C_B,
outside_points_2)
oracle_calls += 1
# we can add the new point to the second, the nearer set
if not intersect(new_C_B, self.C_A):
self.C_B = new_C_B
self.hull_C_B = new_hull_C_B
for x in added:
# add to labels
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_2 = list(set(outside_points_2) - set(added))
# we check if we can add the point to the first set
# if we reach this point the first time all the other E which are classified did not change the optimal margin
else:
# Test first half space
new_hull_C_A = ConvexHull(self.E[self.C_A], 1)
new_hull_C_A.add_points([self.get_point(next_point)], 1)
new_C_A, added = get_points_inside_convex_hull(self.E, new_hull_C_A, self.C_A,
outside_points_1)
oracle_calls += 1
# the point can be classified to the second set
if not intersect(self.C_B, new_C_A):
self.hull_C_A = new_hull_C_A
self.C_A = new_C_A
for x in added:
# add to labels
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.convex_A_distances.keys():
del self.convex_A_distances[x]
if x in self.convex_B_distances.keys():
del self.convex_B_distances[x]
outside_points_1 = list(set(outside_points_1) - set(added))
# we cannot classify the point
else:
if next_point in outside_points_1:
outside_points_1.remove(next_point)
if next_point in outside_points_2:
outside_points_2.remove(next_point)
time_point = time_step("Point add Hull:", time_point)
else:
return [], [], False
return oracle_calls, labels, True
def optimal_hull_alg(self):
time_point = time.time()
oracle_calls = 0
counter = 0
labels = -1 * np.ones(shape=(self.size_E, 1))
outside_points_1 = [x for x in self.convex_A_hull_distances.keys()] + self.C_B
outside_points_2 = [x for x in self.convex_B_hull_distances.keys()] + self.C_A
# add labels
for i in self.C_A:
labels[i] = 1
for i in self.C_B:
labels[i] = 0
# check if initial_hulls are intersecting
Hull1 = self.C_A
inside1, added = get_points_inside_convex_hull(self.E, Hull1, self.C_A, outside_points_1)
self.C_A = inside1
Hull2 = self.C_B
inside2, added = get_points_inside_convex_hull(self.E, Hull2, self.C_B, outside_points_2)
self.C_B = inside2
if not intersect(inside1, inside2):
while (len(self.convex_A_hull_distances) > 0 or len(self.convex_B_hull_distances) > 0):
# print(len(self.Set1HullDistances), len(self.Set2HullDistances))
added = []
# First set is nearer to nearest not classified point
if self.decide_nearest_hull():
time_point = time_step("Find Neighbour:", time_point)
if len(self.convex_A_hull_distances) > 0:
next_point = self.convex_A_hull_distances.popitem()[0]
Hull1 = self.C_A
Hull1.add_points([self.get_point(next_point)], 1)
time_point = time_step("Adding Convex Hull E 1:", time_point)
inside1, added = get_points_inside_convex_hull(self.E, Hull1, self.C_A,
outside_points_1)
oracle_calls += 1
time_point = time_step("Getting inside E:", time_point)
# if there is no intersection the point can be added to the first convex set
if not intersect(inside1, self.C_B):
time_point = time_step("Intersection Test:", time_point)
self.C_A = Hull1
time_point = time_step("Adding Convex Hull E:", time_point)
for x in added:
# add to labels
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.convex_A_hull_distances.keys():
del self.convex_A_hull_distances[x]
if x in self.convex_B_hull_distances.keys():
del self.convex_B_hull_distances[x]
outside_points_1.remove(x)
self.C_A = inside1
time_point = time_step("Update arrays:", time_point)
# if there is an intersection we have to check if it can be added to the second set
else:
time_point = time_step("Intersection Test:", time_point)
# Renew first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), self.dimension_E))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
# Test second half space
Hull2 = self.C_B
Hull2.add_points([self.get_point(next_point)], 1)
inside2, added = get_points_inside_convex_hull(self.E, Hull2, self.C_B,
outside_points_2)
oracle_calls += 1
# the point can be added to the second set,
# if we reach this point the first time all the other E which are classified did not change the optimal margin
if not intersect(self.C_A, inside2):
self.C_B = Hull2
for x in added:
# add to labels
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.convex_A_hull_distances.keys():
del self.convex_A_hull_distances[x]
if x in self.convex_B_hull_distances.keys():
del self.convex_B_hull_distances[x]
outside_points_2.remove(x)
self.C_B = inside2
# the point cannot be added to any set
else:
# Renew second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), self.dimension_E))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
if next_point in outside_points_1:
outside_points_1.remove(next_point)
if next_point in outside_points_2:
outside_points_2.remove(next_point)
time_point = time_step("Point add Hull:", time_point)
# Second set is nearer to nearest not classified point
else:
time_point = time_step("Find Neighbour:", time_point)
if len(self.convex_B_hull_distances) > 0:
next_point = self.convex_B_hull_distances.popitem()[0]
Hull2 = self.C_B
Hull2.add_points([self.get_point(next_point)], 1)
inside2, added = get_points_inside_convex_hull(self.E, Hull2, self.C_B,
outside_points_2)
oracle_calls += 1
# we can add the new point to the second, the nearer set
if not intersect(inside2, self.C_A):
self.C_B = Hull2
for x in added:
# add to labels
labels[x] = 0
self.colors_E[x] = "violet"
if x in self.convex_A_hull_distances.keys():
del self.convex_A_hull_distances[x]
if x in self.convex_B_hull_distances.keys():
del self.convex_B_hull_distances[x]
outside_points_2.remove(x)
self.C_B = inside2
# we check if we can add the point to the first set
# if we reach this point the first time all the other E which are classified did not change the optimal margin
else:
# Renew second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), self.dimension_E))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
# Test first half space
Hull1 = self.C_A
Hull1.add_points([self.get_point(next_point)], 1)
inside1, added = get_points_inside_convex_hull(self.E, Hull1, self.C_A,
outside_points_1)
oracle_calls += 1
# the point can be classified to the second set
if not intersect(self.C_B, inside1):
self.C_A = Hull1
for x in added:
# add to labels
labels[x] = 1
self.colors_E[x] = "orange"
if x in self.convex_A_hull_distances.keys():
del self.convex_A_hull_distances[x]
if x in self.convex_B_hull_distances.keys():
del self.convex_B_hull_distances[x]
outside_points_1.remove(x)
self.C_A = inside1
# we cannot classify the point
else:
# Renew first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), self.dimension_E))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
if next_point in outside_points_1:
outside_points_1.remove(next_point)
if next_point in outside_points_2:
outside_points_2.remove(next_point)
time_point = time_step("Point add Hull:", time_point)
else:
return ([], [], False)
return (oracle_calls, labels, True)
def optimal_runtime_alg(self):
oracle_calls = 0
end = False
counter = 0
while len(self.F) > 0 and end == False:
# print(len(self.F))
if not self.decide_farthest():
items = list(self.convex_A_distances.items())
if len(items) > 0:
next_point = items[len(items) - 1][0]
Hull1 = self.C_A
Hull1.add_points([self.get_point(next_point)], 1)
inside1 = get_points_inside_convex_hull(self.E, Hull1)
oracle_calls += 1
if not intersect(inside1, self.C_B):
self.C_A.add_points([self.get_point(next_point)], 1)
for x in inside1:
if x not in self.C_A:
self.colors_E[x] = "orange"
self.F.remove(x)
del self.convex_A_distances[x]
del self.convex_B_distances[x]
self.C_A = inside1
else:
# Renew first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), 2))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
# Test second half space
Hull2 = self.C_B
Hull2.add_points([self.get_point(next_point)], 1)
inside2 = get_points_inside_convex_hull(self.E, Hull2)
oracle_calls += 1
if not intersect(self.C_A, inside2):
self.C_B.add_points([self.get_point(next_point)], 1)
for x in inside2:
if x not in self.C_B:
self.colors_E[x] = "violet"
self.F.remove(x)
del self.convex_A_distances[x]
del self.convex_B_distances[x]
self.C_B = inside2
else:
# Renew second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), 2))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
self.F.remove(next_point)
del self.convex_A_distances[next_point]
del self.convex_B_distances[next_point]
else:
items = list(self.convex_B_distances.items())
if len(items) > 0:
next_point = items[len(items) - 1][0]
Hull2 = self.C_B
Hull2.add_points([self.get_point(next_point)], 1)
inside2 = get_points_inside_convex_hull(self.E, Hull2, self.C_B)
oracle_calls += 1
if not intersect(inside2, self.C_A):
self.C_B.add_points([self.get_point(next_point)], 1)
for x in inside2:
if x not in self.C_B:
self.colors_E[x] = "violet"
self.F.remove(x)
del self.convex_A_distances[x]
del self.convex_B_distances[x]
self.C_B = inside2
else:
# Renew second half space
PointsH_2 = np.ndarray(shape=(len(self.C_B), 2))
counter = 0
for i in self.C_B:
PointsH_2[counter] = self.get_point(i)
counter += 1
self.C_B = ConvexHull(PointsH_2, 1)
# Test first half space
Hull1 = self.C_A
Hull1.add_points([self.get_point(next_point)], 1)
inside1 = get_points_inside_convex_hull(self.E, Hull1)
oracle_calls += 1
if not intersect(self.C_B, inside1):
self.C_A.add_points([self.get_point(next_point)], 1)
for x in inside1:
if x not in self.C_A:
self.colors_E[x] = "orange"
self.F.remove(x)
del self.convex_A_distances[x]
del self.convex_B_distances[x]
self.C_A = inside1
else:
# Renew first half space
PointsH_1 = np.ndarray(shape=(len(self.C_A), 2))
counter = 0
for i in self.C_A:
PointsH_1[counter] = self.get_point(i)
counter += 1
self.C_A = ConvexHull(PointsH_1, 1)
self.F.remove(next_point)
del self.convex_A_distances[next_point]
del self.convex_B_distances[next_point]
return oracle_calls
box2 = [[0, 0],
[1, 0],
[1, 1],
[0.5, 0.5], # this point is in the center of the square
[0, 1]]
ch1 = ConvexHull(points=box1, incremental=True)
print("ch1:")
summarize_hull_properties(ch1)
print('''\n\nCompared to the above, the following hull contains an extra point at
0.5, 0.5. We can tell it is INSIDE the hull but not on its boundary, by any
of these:
A) adding that point did not increase the area.
B) That point is not included in the vertices.
C) Therefore, that point is also not an endpoint of any simplices.
''')
ch2 = ConvexHull(points=box2, incremental=True)
print("ch2:")
summarize_hull_properties(ch2)
print('\nNext, we add some outside points to the previous ConvexHulls and notice things change:')
ch1.add_points([[1.99, 0], [0, 2.0]])
print("ch1 is now:")
summarize_hull_properties(ch1)
ch2.add_points([[2.0, 0], [0, 2.000000001]])
print("ch2 is now:")
summarize_hull_properties(ch1)
def r( self, c, gpos, mpos=0.0):
"""
Calculates the virtual distances between grid point locations and
microphone locations or the origin. These virtual distances correspond
to travel times of the sound along a ray that is traced through the
medium.
Parameters
----------
c : float
The speed of sound to use for the calculation.
gpos : array of floats of shape (3, N)
The locations of points in the beamforming map grid in 3D cartesian
co-ordinates.
mpos : array of floats of shape (3, M), optional
The locations of microphones in 3D cartesian co-ordinates. If not
given, then only one microphone at the origin (0, 0, 0) is
considered.
Returns
-------
array of floats
The distances in a twodimensional (N, M) array of floats. If M==1,
then only a onedimensional array is returned.
"""
if isscalar(mpos):
mpos = array((0, 0, 0), dtype = float32)[:, newaxis]
# the DE system
def f1(t, y, v):
x = y[0:3]
s = y[3:6]
vv, dv = v(x)
sa = sqrt(s[0]*s[0]+s[1]*s[1]+s[2]*s[2])
x = empty(6)
x[0:3] = c*s/sa - vv # time reversal
x[3:6] = dot(s, -dv.T) # time reversal
return x
# integration along a single ray
def fr(x0, n0, rmax, dt, v, xyz, t):
s0 = n0 / (c+dot(v(x0)[0], n0))
y0 = hstack((x0, s0))
oo = ode(f1)
oo.set_f_params(v)
oo.set_integrator('vode',
rtol=1e-4, # accuracy !
max_step=1e-4*rmax) # for thin shear layer
oo.set_initial_value(y0, 0)
while oo.successful():
xyz.append(oo.y[0:3])
t.append(oo.t)
if norm(oo.y[0:3]-x0)>rmax:
break
oo.integrate(oo.t+dt)
gs2 = gpos.shape[-1]
gt = empty((gs2, mpos.shape[-1]))
vv = self.ff.v
NN = int(sqrt(self.N))
for micnum, x0 in enumerate(mpos.T):
xe = gpos.mean(1) # center of grid
r = x0[:, newaxis]-gpos
rmax = sqrt((r*r).sum(0).max()) # maximum distance
nv = spiral_sphere(self.N, self.Om, b=xe-x0)
rstep = rmax/sqrt(self.N)
rmax += rstep
tstep = rstep/c
xyz = []
t = []
lastind = 0
for i, n0 in enumerate(nv.T):
fr(x0, n0, rmax, tstep, vv, xyz, t)
if i and i % NN == 0:
if not lastind:
dd = ConvexHull(vstack((gpos.T, xyz)), incremental=True)
else:
dd.add_points(xyz[lastind:], restart=True)
lastind = len(xyz)
# ConvexHull includes grid if no grid points on hull
if dd.simplices.min()>=gs2:
break
xyz = array(xyz)
t = array(t)
li = LinearNDInterpolator(xyz, t)
gt[:, micnum] = li(gpos.T)
if gt.shape[1] == 1:
gt = gt[:, 0]
return c*gt #return distance along ray
