pymoto.PNorm

class pymoto.PNorm(p: float = 2, scaling: AggScaling = None, active_set: AggActiveSet = None)

P-norm aggregration

:math:`S_p(x_1, x_2, dotsc, x_n) = left( sum_i (|x_i|^p) right)^{1/p}

Only valid for positive \(x_i\) when approximating the minimum or maximum

__init__(p: float = 2, scaling: AggScaling = None, active_set: AggActiveSet = None)

Initialize P-norm aggregation module

Parameters:

p (float, optional) – Power of the p-norm, approaches maximum for p>0 and minimum for p<0. Defaults to 2.
scaling (pymoto.AggScaling, optional) – Scaling strategy to improve approximation
active_set (pymoto.AggActiveSet, optional) – Active set strategy to improve approximation

Methods

`__init__`([p, scaling, active_set])	Initialize P-norm aggregation module
`aggregation_derivative`(x)	" Calculates df(x) / dx
`aggregation_function`(x)	Calculates f(x)
`connect`(sig_in[, sig_out])	Connect without automatic adding to a function network
`get_input_sensitivities`([as_list])
`get_input_states`([as_list])
`get_output_sensitivities`([as_list])
`get_output_states`([as_list])
`reset`()	Reset the state of the sensitivities (they are set to zero or to None)
`response`()	Calculate the response from sig_in and output this to sig_out
`sensitivity`()	Calculate sensitivities using backpropagation

Attributes

`n_in`	Get the number of input signals
`n_out`	Get the number of output signals
`sig_in`
`sig_out`

connect(sig_in: Signal | Iterable[Signal], sig_out: Signal | Iterable[Signal] = None): Connect without automatic adding to a function network

property n_out: int

Get the number of output signals

Note: Cannot be used in the initial __call__()

reset(): Reset the state of the sensitivities (they are set to zero or to None)

sensitivity()

Calculate sensitivities using backpropagation

Based on the sensitivity we get from sig_out, reverse the process and output the new sensitivities to sig_in