"""
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)')})