cr.sparse.RecoveryTrialsAtFixed_M_N

class cr.sparse.RecoveryTrialsAtFixed_M_N(M, N, Ks, num_dict_trials, num_signal_trials)

Experiment of sparse recovery trials for multiple solvers at fixed dictionary size

Procedure

• Setup the experiment parameters

• Add the solvers to be evaluated under the experiment

• Run the experiment (specify the CSV file in which the results will be saved)

The results are saved automatically during the experiment.

Sample usage:

from functools import partial
import jax.numpy as jnp
from cr.sparse.pursuit.eval import RecoveryTrialsAtFixed_M_N
from cr.sparse.pursuit import htp

Ks = jnp.array(list(range(1, 4)) + list(range(4, 60, 8)))
evaluation = RecoveryTrialsAtFixed_M_N(
    M = 200,
    N = 1000,
    Ks = Ks,
    num_dict_trials = 1,
    num_signal_trials = 20
)
# Add multiple solvers
htp_solve_jit = partial(htp.solve_jit, normalized=False)
nhtp_solve_jit = partial(htp.solve_jit, normalized=True)
evaluation.add_solver('HTP', htp_solve_jit)
evaluation.add_solver('NHTP', nhtp_solve_jit)
# Run evaluation
evaluation('record_combined_performance.csv')

__init__(M, N, Ks, num_dict_trials, num_signal_trials)

Initializes the experiment parameters.

Parameters

• M – (fixed) signal/measurement space dimension

• N – (fixed) number of atoms / representation space dimension

• Ks – Different values of sparsity levels for which experiments will be run

• num_dict_trials – Number of dictionaries sampled for each value of K

• num_signal_trials – Number of sparse vectors sampled for each sampled dictionary for each K

Methods

__init__(M, N, Ks, num_dict_trials, …)	Initializes the experiment parameters.
add_solver(name, solver)
save()	Saves the experiment results in the CSV file