mud_examples.linear package#

Submodules#

mud_examples.linear.lin.compare_linear_sols(transform, lam_ref, A, b, alpha=1, mean=None, cov=None)[source]#: Input dimension fixed, varying according to the output of the anonymous function transform’s return.

mud_examples.linear.lin.compare_linear_sols_dim(lam_ref, A, b, alpha=1, mean=None, cov=None)[source]#: Input dimension fixed, varying output dimension.

mud_examples.linear.lin.compare_linear_sols_rank_list(lam_ref, A, b, alpha=1, mean=None, cov=None)[source]#: Input and output dimensions fixed, varying rank 1..dim_output.

mud_examples.linear.lin.compare_mud_map_pin(A, b, y, mean, cov)[source]#

mud_examples.linear.lin.contour_example(A=array([[1, 1]]), b=array([[0.]]), cov_11=0.5, cov_01=-0.25, initial_mean=array([0.25, 0.25]), alpha=1, omega=1, obs_std=1, show_full=True, show_data=True, show_est=False, param_ref=None, compare=False, fsize=42, figname='latest_figure.png', save=False)[source]#: alpha: float in [0, 1], weight of Tikhonov regularization omega: float in [0, 1], weight of Modified regularization

mud_examples.linear.lin.main(args)[source]#: Main entrypoint for example-generation

mud_examples.linear.lin.main_contours(args)[source]#: Main entrypoint for 2D Linear Rank-Deficient Example (Contour Plots)

mud_examples.linear.lin.main_dim(args)[source]#: Main entrypoint for High-Dim Linear Dimension Example

mud_examples.linear.lin.main_meas(args)[source]#: Main entrypoint for High-Dim Linear Measurement Example

mud_examples.linear.lin.main_meas_var(args)[source]#: Main entrypoint for High-Dim Linear Measurement Example

mud_examples.linear.lin.main_rank(args)[source]#: Main entrypoint for High-Dim Linear Rank Example

mud_examples.linear.lin.run_meas()[source]#: Entry point for console_scripts

mud_examples.linear.lin.run_meas_var()[source]#: Entry point for console_scripts

mud_examples.linear.lin.setup_logging(loglevel)[source]#

Setup basic logging

mud_examples.linear.lin.transform_measurements(operator_list, data_list, measurements, std_list, noise)[source]#

mud_examples.linear.lin.transform_rank_list(lam_ref, A, b, rank)[source]#: A is a list here. We sum the first rank elements of it to return a matrix with the desired rank.

mud_examples.linear.models.createNoisyReferenceData(M, reference_point, std, num_data=None)[source]#

mud_examples.linear.models.createRandomLinearMap(dim_input, dim_output, dist='normal', repeated=False)[source]#: Create random linear map from P dimensions to S dimensions.

mud_examples.linear.models.createRandomLinearPair(reference_point, num_data, std, dist='normal', repeated=False)[source]#: data will come from a normal distribution centered at zero with standard deviation given by std QoI map will come from standard uniform, or normal if dist=normal if repeated is True, the map will be rank 1.

mud_examples.linear.models.createRandomLinearProblem(reference_point, num_qoi, num_observations, std_list, dist='normal', repeated=False)[source]#: Wrapper around createRandomLinearQoI to generalize to multiple QoI maps.

mud_examples.linear.models.randA_gauss(dim_output, dim_input=None, seed=None)[source]#: Generate random Gaussian matrix, perform QR, and returns the resulting (orthogonal) Q

mud_examples.linear.models.randA_list_svd(dim_output, dim_input=None, seed=None) → List[source]#: Generate random square Gaussian matrix, perform SVD, and construct rank-1 matrices from components. Return list of them. Sum R entries of this returned list to generate a rank-R matrix.

mud_examples.linear.models.randA_outer(dim_output, dim_input=None, seed=None)[source]#: Generate dimension rank-1 matrices using Gaussian entries to generate a vector x and then take outer-product with self.

mud_examples.linear.models.randA_qr(dim_output, dim_input=None, seed=None)[source]#: Generate random Gaussian matrix, perform QR, and returns the resulting (orthogonal) Q

mud_examples.linear.models.randP(dim_output, dim_input=None, randA=<function randA_gauss>, seed=None)[source]#: Constructs problem set