Sensor Module¶

TODO: Maybe add grid, grf, n_ouptuts to the sensor class.

class meslas.sensor.DiscreteSensor(discrete_grf)¶

Bases: Sensor

Sensor on a fixed discretization.

Methods

`choose_next_point_myopic`(noise_std, lower[, ...])	Choose the next observation location (given the current one) using the myopic strategy.
`compute_exursion_prob`(lower[, upper])	Compute the excursion probability on the whole grid, given the currently available data.
`get_neighbors_isotopic_eibv`(noise_std, lower)	For each neighbouring node of the current sensor location, compute the eibv if we were to measure every response (isotopic) at that node.
`run_lookahead_stragegy`(n_steps, data_feed, ...)	Run the myopic strategy for n_steps, starting from the current location.
`run_myopic_stragegy`(n_steps, data_feed, ...)	Run the myopic strategy for n_steps, starting from the current location.
`update_design`(S_y_inds, L_y, y[, noise_std])	Updates the full design grid by computing current conditional mean and covariance.

choose_next_point_myopic(noise_std, lower, upper=None)¶

Choose the next observation location (given the current one) using the myopic strategy.

Parameters:

noise_std: float: Standar deviation of measurement noise.
lower: (p) Tensor: List of lower threshold for each response. The excursion set is the set where responses are above the specified threshold. Note that np.inf is supported.
upper: (p) Tensor: If not provided, defaults to +infty (excursion set above threshold).

Returns:

next_point_ind: (1) Tensor: Grid index of next observation location chosen according to the myopic strategy.
next_point_eibv: (1) Tensor: EIBV corresponding to the next chosen point.

compute_exursion_prob(lower, upper=None)¶

Compute the excursion probability on the whole grid, given the currently available data.

Parameters:

lower: (p) Tensor: List of lower threshold for each response. The excursion set is the set where responses are above the specified threshold. Note that np.inf is supported.
upper: (p) Tensor: List of upper threshold for each response. The excursion set is the set where responses are above the specified threshold. If not provided, defaults to + infinity.

Returns:

excursion_proba: (self.grid.n_points) Tensor: Excursion probability at each point.

get_neighbors_isotopic_eibv(noise_std, lower, upper=None)¶

For each neighbouring node of the current sensor location, compute the eibv if we were to measure every response (isotopic) at that node. Returns a list containing the EIBV for each neighbor.

Parameters:

noise_std: float: Standar deviation of measurement noise.
lower: (p) Tensor: List of lower threshold for each response. The excursion set is the set where responses are above the specified threshold. Note that np.inf is supported.
upper: (p) Tensor: If not provided, defaults to +infty (excursion set above threshold).

Returns:

neighbors_eibv: (n_neighbors) Tensor: EIBV for each neighbouring cell.
neighbors_inds: (n_neighbors) Tensor: Grid indices of the neighbouring cells.

run_lookahead_stragegy(n_steps, data_feed, noise_std, lower, upper=None, n_prune=3)¶

Run the myopic strategy for n_steps, starting from the current location. That is, at each point, pick the neighbors with the smallest EIBV, move there, observe, update model, repeat.

Parameters:

n_steps: int: Number of steps (observations) to run the strategy for.
data_feed: function(int): Function that, given a node index, returns the measured data (isotopic) at that node.
noise_std
lower
upper
n_prune: int: Number of paths to consider at the first step. From the starting node, we will only consider the best n_prune nodes, ranked using the myopic strategy.

run_myopic_stragegy(n_steps, data_feed, noise_std, lower, upper=None)¶

Run the myopic strategy for n_steps, starting from the current location. That is, at each point, pick the neighbors with the smallest EIBV, move there, observe, update model, repeat.

Parameters:

n_steps: int: Number of steps (observations) to run the strategy for.
data_feed: function(int): Function that, given a node index, returns the measured data (isotopic) at that node.

update_design(S_y_inds, L_y, y, noise_std=None)¶

Updates the full design grid by computing current conditional mean and covariance. Note that this updates the internal of the discrete GRF.

Returns:

mu_cond_iso: (self.grid.n_points, self.grf.n_out): Conditional mean.
K_cond_iso: (self.grid.n_points, self.grid.n_points, self.grf.n_out, self.grf.n_out) Tensor: Conditional covariance matrix in isotopic ordered form. It means that the covariance matrix at cell i can be otained by subsetting K_cond_iso[i, i, :, :].

class meslas.sensor.Sensor(grid, grf)¶

Bases: object

Implements the data collection process. Will be responsible for querying the GP for mean and variances conditional on the already collected data.

Attributes:

S_y_tot: (N, d) Tensor: Spatial locations of the already collected data.
L_y_tot: (N) Tensor: Corresponding response indices.
location: (d) Tensor: Current position of the sensor.
grid: IrregularGrid: A discretization of space that defines the locations the sensor can move to.
grf: GRF: Gaussian Random Field used to model the unknown phenomenon of interest.
current_node_ind: int: Index of the point in the grid that is closest to the sensor location.
visited_nodes_inds: Tensor: Grid indices of the visited locations.
noise_std: float: Standard deviation of the sensor noise. TODO: allow different noises for each component.

Methods

`add_data`(S_y_inds, L_y, y)	Add new data to the already collected one.
`compute_exursion_prob`(points, lower[, upper])	Compute the excursion probability at a set of points given the currently available data.
`compute_neighbors_exursion_prob`(lower[, upper])	Compute the excursion probability of the neighbors of the current location.
`get_current_neighbors`()	Get the neighbouring grid nodes of the current sensor location.
`set_location`(location)	Setter for the location.

add_data(S_y_inds, L_y, y)¶

Add new data to the already collected one. Can also handle batches. This will just concatenate the new data vectors with the current ones.

Parameters:

S_y_inds: (n) Tensor: Grind index of spatial locations of the new measurements.
L_y :(n) Tensor: Corresponding response indices.
y :(n, p) Tensor: Measured data.

compute_exursion_prob(points, lower, upper=None)¶

Compute the excursion probability at a set of points given the currently available data.

Note this is a helper function that take an index in the grid as input.

Parameters:

points: (N, d) Tensor: List of points (coordinates) at which to compute the excursion probability.
lower: (p) Tensor: List of lower threshold for each response. The excursion set is the set where responses are above the specified threshold. Note that np.inf is supported.
upper: (p) Tensor: List of upper threshold for each response. The excursion set is the set where responses are above the specified threshold. If not provided, defaults to + infinity.

Returns:

excursion_proba: (N) Tensor: Excursion probability at each point.

compute_neighbors_exursion_prob(lower, upper=None)¶

Compute the excursion probability of the neighbors of the current location.

Parameters:

lower: (p) Tensor: List of lower threshold for each response. The excursion set is the set where responses are above the specified threshold. Note that np.inf is supported.
upper: (p) Tensor: List of upper threshold for each response. The excursion set is the set where responses are above the specified threshold. If not provided, defaults to + infinity.

get_current_neighbors()¶

Get the neighbouring grid nodes of the current sensor location.

This is done by first finding the node closes to the sensor location, and then returning its neighbors.

Returns:

neighbors_inds: (n_neighbors): Grid indices of the neighbors.

set_location(location)¶

Setter for the location.

Parameters:

location: (d) array_like