pysensors.utils package

Module contents

pysensors.utils.constrained_binary_solve(w, psi, quiet=False, fit_intercept=True, normalize=True, precompute='auto')

pysensors.utils.constrained_multiclass_solve(w, psi, alpha=1.0, quiet=False, **lasso_kws)

Solve

\[\begin{split}\text{argmin}_s \|s\|_0 \\ \text{subject to} \|w - \psi s\|_2^2 \leq tol\end{split}\]

pysensors.utils.validate_input(x, sensors=None)

Ensure that x is of compatible type and shape.

Parameters

x (numpy ndarray, shape [n_features,] or [n_examples, n_features]) – Data to be validated.

pysensors.utils.get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)

Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix.

Parameters

• x_min (int, lower bound for the x-axis constraint) –

• x_max (int, upper bound for the x-axis constraint) –

• y_min (int, lower bound for the y-axis constraint) –

• y_max (int, upper bound for the y-axis constraint) –

• nx (int, image pixel (x dimensions of the grid)) –

• ny (int, image pixel (y dimensions of the grid)) –

• all_sensors (np.ndarray, shape [n_features], ranked list of sensor locations.) –

Returns

idx_constrained – locations of the grid in terms of column indices of basis_matrix.

Return type

np.darray, shape [No. of constrained locations], array which contains the constrained

pysensors.utils.get_constrained_sensors_indices_linear(x_min, x_max, y_min, y_max, df)

Function for obtaining constrained column indices from already existing linear sensor locations on the grid.

Parameters

• x_min (int, lower bound for the x-axis constraint) –

• x_max (int, upper bound for the x-axis constraint) –

• y_min (int, lower bound for the y-axis constraint) –

• y_max (int, upper bound for the y-axis constraint) –

• df (pandas.DataFrame, a dataframe containing the features and samples) –

Returns

idx_constrained – locations of the grid in terms of column indices of basis_matrix.

Return type

np.darray, shape [No. of constrained locations], array which contains the constrained

pysensors.utils.exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs)

Function for mapping constrained sensor locations with the QR procedure.

Parameters

• lin_idx (np.ndarray, shape [No. of constrained locations]) – Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix.

• dlens (np.ndarray, shape [Variable based on j]) – Array which contains the norm of columns of basis matrix.

• piv (np.ndarray, shape [n_features]) – Ranked list of sensor locations.

• n_const_sensors (int,) – Number of sensors to be placed in the constrained area.

• j (int,) – Iterative variable in the QR algorithm.

Returns

dlens

Return type

np.darray, shape [Variable based on j] with constraints mapped into it.

pysensors.utils.max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs)

Function for mapping constrained sensor locations with the QR procedure (Optimally).

Parameters

• lin_idx (np.ndarray, shape [No. of constrained locations]) – Array which contains the constrained locations of the grid in terms of column indices of basis_matrix.

• dlens (np.ndarray, shape [Variable based on j]) – Array which contains the norm of columns of basis matrix.

• piv (np.ndarray, shape [n_features]) – Ranked list of sensor locations.

• j (int,) – Iterative variable in the QR algorithm.

• const_sensors (int,) – Number of sensors to be placed in the constrained area.

• all_sensors (np.ndarray, shape [n_features]) – Ranked list of sensor locations.

• n_sensors (integer,) – Total number of sensors

Returns

dlens

Return type

np.darray, shape [Variable based on j] with constraints mapped into it.

pysensors.utils.predetermined(lin_idx, dlens, piv, j, n_const_sensors, **kwargs)

Function for mapping constrained sensor locations with the QR procedure.

Parameters

• lin_idx (np.ndarray, shape [No. of constrained locations], array which contains) – the constrained locationsof the grid in terms of column indices of basis_matrix.

• dlens (np.ndarray, shape [Variable based on j], array which contains the norm of columns of basis matrix.) –

• piv (np.ndarray, shape [n_features], ranked list of sensor locations.) –

• n_const_sensors (int, number of sensors to be placed in the constrained area.) –

• j (int, iterative variable in the QR algorithm.) –

Returns

dlens

Return type

np.darray, shape [Variable based on j] with constraints mapped into it.

pysensors.utils.determinant(top_sensors, n_features, basis_matrix)

Function for calculating |C.T phi.T C phi|.

Parameters

• top_sensors (np.darray,) – Column indices of choosen sensor locations

• n_features (int,) – No. of features of dataset

• basis_matrix (np.darray,) – The basis matrix calculated by model.basis_matrix_

Returns

optimality – The dterminant value obtained.

Return type

Float,

pysensors.utils.relative_reconstruction_error(data, prediction)

Function for calculating relative error between actual data and the reconstruction

Parameters

data: np.darray,

The actual data from the dataset evaluated

predictionnp.darray,

The predicted values from model.predict(X[:,top_sensors])

error_valFloat,

The relative error calculated.