def __init__(self,
map_data,
main_sensor='None',
k_name="RBF",
acq="gaussian_sei",
acq_mod="masked"):
# self.agents = agents
self.map_data = map_data
self.acquisition = acq # 'gaussian_sei' 'gaussian_ei' 'maxvalue_entropy_search''gaussian_pi'
self.acq_mod = acq_mod # 'masked' 'split_path' 'truncated', 'normal'
self.k_name = k_name # "RBF" Matern" "RQ"
if k_name == "RBF_N":
self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RBF(100),
alpha=1e-7)
elif k_name == "RBF":
self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RBF(100.0),
alpha=1e-7)
# self.gp = gpr.GaussianProcessRegressor(kernel=kernels.Matern(100, nu=3.5), alpha=1e-7)
elif k_name == "RQ":
self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RBF(100),
alpha=1e-7)
# self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RationalQuadratic(100, 0.1), alpha=1e-7)
self.data = [np.array([[], []]), np.array([])]
self.all_vector_pos = np.mgrid[0:self.map_data.shape[1]:1,
0:self.map_data.shape[0]:1].reshape(
2, -1).T
self.vector_pos = np.fliplr(
np.asarray(np.where(self.map_data == 0)).reshape(2, -1).T)
self.main_sensor = main_sensor
self.splitted_goals = []
def __init__(self, agents, map_data, main_sensor='None', acq="RU"):
self.agents = agents
self.map_data = map_data
self.acquisition = 'gaussian_sei'
self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RBF(100),
alpha=1e-7)
# self.gp = gpr.GaussianProcessRegressor(kernel=kernels.Matern(150, nu=3.5), alpha=1e-7)
# self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RationalQuadratic(150, 0.1), alpha=1e-7)
# self.gp = gpr.GaussianProcessRegressor(normalize_y=True, kernel=20 * kernels.RBF(150), alpha=1e-7)
self.data = [np.array([[], []]), np.array([])]
self.acq_method = acq
self.all_vector_pos = np.mgrid[0:self.map_data.shape[1]:1,
0:self.map_data.shape[0]:1].reshape(
2, -1).T
self.vector_pos = np.fliplr(
np.asarray(np.where(self.map_data == 0)).reshape(2, -1).T)
self.main_sensor = main_sensor
_x, _y = self.obtain_shapely_polygon().exterior.coords.xy
self.fpx, self.fpy = self.obtain_points(_x, _y)
# dists = []
# for i in range(len(self.fpx) - 1):
# dists.append(np.linalg.norm(
# np.subtract(np.array([self.fpx[i], self.fpy[i]]), np.array([self.fpx[i + 1], self.fpy[i + 1]]))))
#
import matplotlib.pyplot as plt
# plt.Figure()
# plt.hist(dists)
# print(np.mean(dists))
# print(np.std(dists))
plt.plot(self.fpx, self.fpy, '-*')
def __init__(self, agents, map_data, main_sensor='None'):
self.agents = agents
self.map_data = map_data
self.acquisition = 'gaussian_lcb'
self.gp = gpr.GaussianProcessRegressor(normalize_y=True, kernel=1000 * kernels.RBF(2))
self.data = [np.array([[], []]), np.array([])]
# self.vector_pos = np.mgrid[0:self.map_data.shape[1]:1, 0:self.map_data.shape[0]:1].reshape(2, -1).T
self.vector_pos = np.asarray(np.where(self.map_data == 0)).reshape(2, -1).T
self.main_sensor = main_sensor
def __init__(self,
map_data,
sensors: set,
k_names=None,
acq="gaussian_ei",
acq_mod="masked",
acq_fusion="decoupled",
d=1.0):
if k_names is None:
k_names = ["RBF"] * len(sensors)
self.map_data = map_data
self.acquisition = acq # 'gaussian_sei' 'gaussian_ei' 'maxvalue_entropy_search''gaussian_pi'
self.acq_mod = acq_mod # 'masked' 'split_path' 'truncated', 'normal'
self.k_names = k_names # "RBF" Matern" "RQ"
self.sensors = sensors
self.gps = dict()
self.train_inputs = [np.array([[], []])]
self.train_targets = dict()
self.proportion = d
self.mus = dict()
self.stds = dict()
self.has_calculated = dict()
for sensor, kernel in zip(sensors, k_names):
if kernel == "RBF": # "RBF" Matern" "RQ"
helper = gpr.GaussianProcessRegressor(kernel=kernels.RBF(100),
alpha=1e-7)
self.gps[sensor] = convert(helper, 'torch')
self.gps[sensor].to('cuda')
self.train_targets[sensor] = np.array([])
self.mus[sensor] = np.array([])
self.stds[sensor] = np.array([])
self.has_calculated[sensor] = False
self.all_vector_pos = np.mgrid[0:self.map_data.shape[1]:1,
0:self.map_data.shape[0]:1].reshape(
2, -1).T
self.vector_pos = np.fliplr(
np.asarray(np.where(self.map_data == 0)).reshape(2, -1).T)
self.acq_fusion = acq_fusion
# simple_max: maximum value found
# max_sum: sum of acq on max for each maximum
self.splitted_goals = []
self.nans = None
def __init__(self,
map_data,
sensors: set,
k_names=None,
acq="gaussian_ei",
acq_mod="masked",
acq_fusion="decoupled",
d=0.375):
if k_names is None:
k_names = ["RBF"] * len(sensors)
self.map_data = map_data
self.acquisition = acq # 'gaussian_sei' 'gaussian_ei' 'maxvalue_entropy_search''gaussian_pi'
self.acq_mod = acq_mod # 'masked' 'split_path' 'truncated', 'normal',
self.k_names = k_names # "RBF" Matern" "RQ"
self.sensors = sensors
self.gps = dict()
self.train_inputs = [np.array([[], []])]
self.train_targets = dict()
self.proportion = d
self.mus = dict()
self.stds = dict()
self.has_calculated = dict()
for sensor, kernel in zip(sensors, k_names):
if kernel == "RBF": # "RBF" Matern" "RQ"
self.gps[sensor] = gpr.GaussianProcessRegressor(
kernel=kernels.RBF(100), alpha=1e-7)
# self.gps[sensor] = gprnew.GaussianProcessRegressor(kernel=kernels.RBF(100), alpha=1e-7, noise=0.01)
self.train_targets[sensor] = np.array([])
self.mus[sensor] = np.array([])
self.stds[sensor] = np.array([])
self.has_calculated[sensor] = False
self.all_vector_pos = np.mgrid[0:self.map_data.shape[1]:1,
0:self.map_data.shape[0]:1].reshape(
2, -1).T
self.vector_pos = np.fliplr(
np.asarray(np.where(self.map_data == 0)).reshape(2, -1).T)
self.problem = MyProblem(gps=self.gps,
train_targets=self.train_targets,
map_data=map_data)
self.algorithm = NSGA2(pop_size=50)
self.splitted_goals = []
self.nans = None
def __init__(self,
scenario_map,
initial_position=None,
battery_budget=100,
resolution=1,
seed=0):
self.id = "Continuous BO Ypacarai"
# Map of the environment #
self.scenario_map = scenario_map
# Environment boundaries
self.map_size = self.scenario_map.shape
self.map_lims = np.array(self.map_size) - 1
# Action space and sizes #
self.action_space = gym.spaces.Box(low=np.array([0, 0]),
high=np.array([1, 1]))
self.action_size = 2
# Internal state of the Markov Decision Process #
self.state = None
self.next_state = None
self.reward = None
self.done = False
# Create the ground truth object #
self.gt = GroundTruth(1 - self.scenario_map, 1, max_number_of_peaks=5)
# Initial position, referred as a [X,Y] vector #
self.initial_position = initial_position
self.step_count = 0
# Penalization for a collision in the reward funcion #
self.collision_penalization = 10
# Seed for deterministic replication #
self.seed = seed
np.random.seed(self.seed)
# Battery budget
self.battery = battery_budget
# Battery cost -> Cost of the battery per 1m movement
# This is calculated approximately using the long size of the map ~ 15km in the Ypacarai case
self.battery_cost = 100 / np.max(self.map_size) / resolution
# Gaussian Process parameters
# GP with RBF kernel of 10% long-size of the map lengthscale (in pixels)
self.gp = gpr.GaussianProcessRegressor(kernel=kernels.RBF(
0.1 * np.min(self.map_size)),
alpha=1e-7)
# Matrix de (num_muestra, features): num_muestra: numero de posiciones en la que se tomaron muestras
# features: cada una de las dimenciones, i.e., features son y, x
self.train_inputs = None
# Solution vector: y = f(x)
self.train_targets = None
# Matrix de (num_pos, features): num_pos: numero de posiciones en la que puede encontrarse el ASV
# features: cada una de las dimenciones, i.e., features son y, x
# [[2 17]
# [2 18]
# ...
# [y x]
# ...
# [54 31]
# [54 32]]
self.possible_locations = np.asarray(
np.where(self.scenario_map == 1)).reshape(2, -1).T
# Generate vector of possible gt (speeds up the reward MSE process)
self.target_locations = None
# Vector!! de dimension 1 fila x (mxn) columnas representando el mapa de incertidumbre anterior
self.current_std = None
# Vector!! de dimension 1 fila x (mxn) columnas representando el mapa de media anterior
self.current_mu = None
# Current MSE for reward
self.previous_mse = None
self._max_step_distance = np.min(self.map_size)
# Initial position #
# The initial position HAS PIXEL UNITS:
# The action spaces translates to PIXEL UNITS TO FORM THE STATE
self.position = None
self.place_agent()
self.reset()
