def __init__(self, training_samples: TensorList, y: TensorList,
filter_reg: torch.Tensor, sample_weights: TensorList,
response_activation):
self.training_samples = training_samples.variable()
self.y = y.variable()
self.filter_reg = filter_reg
self.sample_weights = sample_weights
self.response_activation = response_activation
def __init__(self,
problem: L2Problem,
variable: TensorList,
cg_eps=0.0,
fletcher_reeves=True,
standard_alpha=True,
direction_forget_factor=0,
debug=False,
plotting=False,
fig_num=(10, 11)):
super().__init__(fletcher_reeves, standard_alpha,
direction_forget_factor, debug or plotting)
self.problem = problem
self.x = variable.variable()
self.plotting = plotting
self.fig_num = fig_num
self.cg_eps = cg_eps
self.f0 = None
self.g = None
self.dfdxt_g = None
self.residuals = torch.zeros(0)
self.losses = torch.zeros(0)
def __init__(self, training_samples: TensorList, y: TensorList, filter_reg: torch.Tensor, projection_reg, params, sample_weights: TensorList,
projection_activation, response_activation):
self.training_samples = training_samples
self.y = y.variable()
self.filter_reg = filter_reg
self.sample_weights = sample_weights
self.params = params
self.projection_reg = projection_reg
self.projection_activation = projection_activation
self.response_activation = response_activation
self.diag_M = self.filter_reg.concat(projection_reg)