def init(self, boot_spec):
# TODO: do the 1D version
shape = boot_spec.get_observations().shape()
if len(shape) > 2:
msg = 'BGDSagent only work with 2D or 1D signals.'
raise UnsupportedSpec(msg)
min_width = np.min(shape)
if min_width <= 5:
msg = ('BGDSagent thinks this shape is too'
'small to compute gradients: %s' % str(shape))
raise UnsupportedSpec(msg)
self.is2D = len(boot_spec.get_observations().shape()) == 2
self.is1D = len(boot_spec.get_observations().shape()) == 1
ExpSwitcher.init(self, boot_spec)
self.count = 0
self.y_deriv = DerivativeBox()
self.bgds_estimator = BGDSEstimator()
self.model = None
self.y_disag = Expectation()
self.y_disag_s = Expectation()
self.u_stats = []
self.last_y0 = None
self.last_y = None
self.rd = RemoveDoubles(0.5)
def init(self, boot_spec):
ExpSwitcher.init(self, boot_spec)
if len(boot_spec.get_observations().shape()) != 1:
raise UnsupportedSpec('I assume 1D signals.')
self.y_stats = MeanCovariance()
self.y_dot_stats = MeanCovariance()
self.y_dot_sgn_stats = MeanCovariance()
self.y_dot_abs_stats = MeanCovariance()
self.count = 0
self.y_deriv = DerivativeBox()
def __init__(self, beta, skip=1, scales=[0], fixed_dt=0):
ExpSwitcher.__init__(self, beta)
self.skip = skip
self.scales = scales
self.fixed_dt = fixed_dt
def __init__(self, statistic='y_corr', scale_score=False, **kwargs): # @UnusedVariable
ExpSwitcher.__init__(self, **kwargs)
self.statistic = statistic
self.scale_score = False
