""" from __future__ import division import numpy as np from magni.cs.reconstruction._config import Configger as _Configger from magni.utils.validation import validate_generic as _generic from magni.utils.validation import validate_numeric as _numeric configger = _Configger( {'iterations': 300, 'kappa_fixed': 0.65, 'precision_float': np.float64, 'threshold': 'far', 'threshold_fixed': 0.1, 'tolerance': 1e-3}, {'iterations': _numeric(None, 'integer', range_='[1;inf)'), 'kappa_fixed': _numeric(None, 'floating', range_='[0;1]'), 'precision_float': _generic(None, type, value_in=( np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_))), 'threshold': _generic(None, 'string', value_in=('far', 'oracle')), 'threshold_fixed': _numeric(None, 'floating', range_='[0;1]'), 'tolerance': _numeric(None, 'floating', range_='[0;inf]')})
""" from __future__ import division import numpy as np from magni.cs.reconstruction._config import Configger as _Configger from magni.utils.validation import validate_generic as _generic from magni.utils.validation import validate_numeric as _numeric configger = _Configger( {'iterations': 300, 'kappa_fixed': 0.65, 'precision_float': np.float64, 'threshold': 'far', 'threshold_fixed': 0.1, 'tolerance': 1e-3}, {'iterations': _numeric(None, 'integer', range_='[1;inf)'), 'kappa_fixed': _numeric(None, 'floating', range_='[0;1]'), 'precision_float': _generic(None, type, value_in=( np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_))), 'threshold': _generic(None, 'string', value_in=('far', 'oracle')), 'threshold_fixed': _numeric(None, 'floating', range_='[0;1]'), 'tolerance': _numeric(None, 'floating', range_='[0;inf]')})
configger = _Configger( { 'damping': 0.0, 'input_channel': _IIDBG, 'input_channel_parameters': dict(), 'iterations': 300, 'output_channel': _AWGN, 'output_channel_parameters': dict(), 'precision_float': np.float64, 'report_A_asq_setup': False, 'report_history': False, 'stop_criterion': _MSEConvergence, 'sum_approximation_constant': { 'rangan': 1.0 }, 'tolerance': 1e-6, 'true_solution': None, 'warm_start': None }, { 'damping': _numeric(None, 'floating', range_='[0;1)'), 'input_channel': _generic(None, 'class', superclass=_MMSEInputChannel), 'input_channel_parameters': _generic(None, 'mapping'), 'iterations': _numeric(None, 'integer', range_='[1;inf)'), 'output_channel': _generic(None, 'class', superclass=_MMSEOutputChannel), 'output_channel_parameters': _generic(None, 'mapping'), 'precision_float': _generic(None, type, value_in=(np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_), getattr(np, 'complex64', np.complex_), getattr(np, 'complex128', np.complex_), getattr(np, 'complex256', np.complex_))), 'report_A_asq_setup': _numeric(None, 'boolean'), 'report_history': _numeric(None, 'boolean'), 'stop_criterion': _generic(None, 'class', superclass=_StopCriterion), 'sum_approximation_constant': _levels( None, (_generic(None, 'mapping', keys_in=('rangan', 'krzakala'), len_=1), _numeric(None, ('integer', 'floating'), range_='(0;inf)'))), 'tolerance': _numeric(None, 'floating', range_='[0;inf)'), 'true_solution': _numeric(None, ('integer', 'floating', 'complex'), shape=(-1, 1), ignore_none=True), 'warm_start': _levels( None, (_generic(None, 'explicit collection', len_=2, ignore_none=True), _numeric(None, ('integer', 'floating', 'complex'), shape=(-1, 1)))) })
configger = _Configger( {'iterations': 300, 'precision_float': np.float64, 'report_history': False, 'stop_criterion': _MSEConvergence, 'threshold': _SoftThreshold, 'threshold_parameters': dict(), 'tolerance': 1e-6, 'true_solution': None, 'warm_start': None}, {'iterations': _numeric(None, 'integer', range_='[1;inf)'), 'precision_float': _generic(None, type, value_in=( np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_), getattr(np, 'complex64', np.complex_), getattr(np, 'complex128', np.complex_), getattr(np, 'complex256', np.complex_))), 'report_history': _numeric(None, 'boolean'), 'stop_criterion': _generic(None, 'class', superclass=_StopCriterion), 'threshold': _generic(None, 'class', superclass=_ThresholdOperator), 'threshold_parameters': _generic(None, 'mapping'), 'tolerance': _numeric(None, 'floating', range_='[0;inf)'), 'true_solution': _numeric( None, ('integer', 'floating', 'complex'), shape=(-1, 1), ignore_none=True), 'warm_start': _numeric(None, ('integer', 'floating', 'complex'), shape=(-1, 1), ignore_none=True)})
configger = _Configger( { 'epsilon': 1e-2, 'L': 'geometric', 'L_fixed': 2, 'L_geometric_ratio': 2.0, 'L_geometric_start': 2.0, 'mu': 'step', 'mu_fixed': 1.0, 'mu_step_end': 1.5, 'mu_step_iteration': 4, 'mu_step_start': 0.001, 'precision_float': np.float64, 'sigma_geometric': 0.7, 'sigma_start': 'reciprocal', 'sigma_start_fixed': 2.0, 'sigma_start_reciprocal': 2.75, 'sigma_stop_fixed': 0.01 }, { 'epsilon': _numeric(None, 'floating', range_='(0;inf)'), 'L': _generic(None, 'string', value_in=('fixed', 'geometric')), 'L_fixed': _numeric(None, 'integer', range_='[1;inf)'), 'L_geometric_ratio': _numeric(None, 'floating', range_='(0;inf)'), 'L_geometric_start': _numeric(None, 'floating', range_='[1;inf)'), 'mu': _generic(None, 'string', value_in=('fixed', 'step')), 'mu_fixed': _numeric(None, 'floating', range_='(0;inf)'), 'mu_step_end': _numeric(None, 'floating', range_='(0;inf)'), 'mu_step_iteration': _numeric(None, 'integer', range_='(0;inf)'), 'mu_step_start': _numeric(None, 'floating', range_='(0;inf)'), 'precision_float': _generic(None, type, value_in=(np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_))), 'sigma_geometric': _numeric(None, 'floating', range_='(0;1)'), 'sigma_start': _generic(None, 'string', value_in=('fixed', 'reciprocal')), 'sigma_start_fixed': _numeric(None, 'floating', range_='(0;inf)'), 'sigma_start_reciprocal': _numeric(None, 'floating', range_='(0;inf)'), 'sigma_stop_fixed': _numeric(None, 'floating', range_='(0;inf)') })
configger = _Configger( {'iterations': 300, 'kappa': 'fixed', 'kappa_fixed': 0.65, 'precision_float': np.float64, 'report_history': False, 'stop_criterion': 'residual_measurements_ratio', 'threshold': 'far', 'threshold_fixed': 1, 'threshold_operator': 'hard', 'threshold_weights': np.array([[1]]), 'tolerance': 1e-3, 'true_solution': None, 'warm_start': None}, {'iterations': _numeric(None, 'integer', range_='[1;inf)'), 'kappa': _generic( None, 'string', value_in=_util._get_methods('step_size')), 'kappa_fixed': _numeric(None, 'floating', range_='(0;inf)'), 'precision_float': _generic(None, type, value_in=( np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_))), 'report_history': _numeric(None, 'boolean'), 'stop_criterion': _generic( None, 'string', value_in=_util._get_methods('stop_criterion')), 'threshold': _generic( None, 'string', value_in=_util._get_methods('threshold')), 'threshold_fixed': _numeric(None, 'integer', range_='(0;inf)'), 'threshold_operator': _generic( None, 'string', value_in=_util._get_operators('threshold_operators')), 'threshold_weights': _numeric( None, ('integer', 'floating'), shape=(-1, 1)), 'tolerance': _numeric(None, 'floating', range_='[0;inf]'), 'true_solution': _numeric( None, ('integer', 'floating', 'complex'), shape=(-1, 1), ignore_none=True), 'warm_start': _numeric( None, ('integer', 'floating', 'complex'), shape=(-1, 1), ignore_none=True)})
configger = _Configger( {'epsilon': 1e-2, 'L': 'geometric', 'L_fixed': 2, 'L_geometric_ratio': 2.0, 'L_geometric_start': 2.0, 'mu': 'step', 'mu_fixed': 1.0, 'mu_step_start': 0.001, 'mu_step_end': 1.5, 'precision_float': np.float64, 'sigma_geometric': 0.7, 'sigma_start': 'reciprocal', 'sigma_start_fixed': 2.0, 'sigma_start_reciprocal': 2.75, 'sigma_stop_fixed': 0.01}, {'epsilon': _numeric(None, 'floating', range_='(0;inf)'), 'L': _generic(None, 'string', value_in=('fixed', 'geometric')), 'L_fixed': _numeric(None, 'integer', range_='[1;inf)'), 'L_geometric_ratio': _numeric(None, 'floating', range_='(0;inf)'), 'L_geometric_start': _numeric(None, 'floating', range_='[1;inf)'), 'mu': _generic(None, 'string', value_in=('fixed', 'step')), 'mu_fixed': _numeric(None, 'floating', range_='(0;inf)'), 'mu_step_start': _numeric(None, 'floating', range_='(0;inf)'), 'mu_step_end': _numeric(None, 'floating', range_='(0;inf)'), 'precision_float': _generic(None, type, value_in=( np.float, getattr(np, 'float16', np.float_), getattr(np, 'float32', np.float_), getattr(np, 'float64', np.float_), getattr(np, 'float128', np.float_))), 'sigma_geometric': _numeric(None, 'floating', range_='(0;1)'), 'sigma_start': _generic(None, 'string', value_in=('fixed', 'reciprocal')), 'sigma_start_fixed': _numeric(None, 'floating', range_='(0;inf)'), 'sigma_start_reciprocal': _numeric(None, 'floating', range_='(0;inf)'), 'sigma_stop_fixed': _numeric(None, 'floating', range_='(0;inf)')})